Collective List-only Entity Linking: A Graph-based Approach...ABSTRACT List-only entity linking is the task of mapping ambiguous mentions in texts to target entities in a group of

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Digital Object Identifier 10.1109/ACCESS.2017.DOI

Collective List-only Entity Linking: AGraph-based ApproachWEIXIN ZENG1, XIANG ZHAO1,2, JIUYANG TANG1,2 AND HAICHUAN SHANG3,4.1College of System Engineering, National University of Defense Technology, China.2Collaborative Innovation Center of Geospatial Technology, China.3National Institute of Information and Communications Technology, Japan.4Institute of Industrial Science, University of Tokyo, Japan.

Corresponding author: Xiang Zhao (e-mail: [email protected]).

The work was supported by NSFC under grants Nos. 61402494, 71690233 and 71331008, and NSF of Hunan Province under grant No.2015JJ4009.

ABSTRACT List-only entity linking is the task of mapping ambiguous mentions in texts to targetentities in a group of entity lists. Different from traditional entity linking task, which leverages richsemantic relatedness in knowledge bases to improve linking accuracy, list-only entity linking can merelytake advantage of co-occurrences information in entity lists. State-of-the-art work utilizes co-occurrencesinformation to enrich entity descriptions, which are further used to calculate local compatibility betweenmentions and entities to determine results. Nonetheless, entity coherence is also deemed to play an importantpart in entity linking, which is yet currently neglected. In this work, in addition to local compatibility,we take into account global coherence among entities. Specifically, we propose to harness co-occurrencesin entity lists for mining both explicit and implicit entity relations. The relations are then integrated intoan entity graph, on which Personalized PageRank is incorporated to compute entity coherence. Thefinal results are derived by combining local mention-entity similarity and global entity coherence. Theexperimental studies validate the superiority of our method. Our proposal not only improves the performanceof list-only entity linking, but also opens up the bridge between list-only entity linking and conventionalentity linking solutions.

INDEX TERMS list-only entity linking, named entity disambiguation, graph-based approach.

I. INTRODUCTION

ENTITY Linking (EL) is the task of detecting corre-sponding named entities for ambiguous mentions in

text. Mention refers to character string, such as Jackson inthe example shown in Fig. 1, the true meaning of whichneeds to be determined by being linked to an entity, such asthe basketball coach Phil Jackson. Traditional EL methodsleverage knowledge bases (KBs), which offer rich seman-tic information of entities, for robust and accurate disam-biguation process. Nevertheless, despite the effectiveness ofknowledge-based EL, it might not be applicable in situationswhere there is insufficient information of entities, such asentity lists.

Entity list, as is often the case, consists of a group ofclosely-related entities, and it exists in various informationsources [1]. In contrast to KBs, where complete structure ofentities facilitates almost all entity-related tasks, entity listminimizes necessary information to mere co-occurrences of

interrelated entities, thus serving as a light-weight alternativein terms of describing entity correlations.

Entity lists can be found useful, for instance, in the sce-nario concerning detection of emerging stock names. Wheninvestors search new stock names in Wikipedia 1, a frequentlyupdated KB, chances are that there are no correspondingitems. In fact, as is shown in [2], for a dataset including 2,468stock names, merely 340 of them can be found in Wikipedia.Nevertheless, those stocks can be found co-occurring withothers in stock lists on financial websites. Thus, the stocklists will be of great use if people have doubts concerningnew stocks. There are much more similar situations, suchas searching for specific car brands or collecting informationabout bars in a small town, where the knowledge about targetentities is sparse.

Consequently, the demand for list-only EL emerges [1],which targets at solving the problem of mapping ambiguous

1https://www.wikipedia.org/

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Author et al.: Preparation of Papers for IEEE TRANSACTIONS and JOURNALS

Jackson holds the NBA record for the most combined

championships, among which two are attained as a

player with the New York Knicks, and eleven as coach

of the Chicago and the Los Angeles.

Jackson New York

KnicksChicago Los Angeles

BOXERS

Phil Jackson,

Chris Byrd,

Brian

Nielsen

COACHES

Phil Jackson,

Larry Brown,

Gregg

Popovich

TEAMS

New York

Knicks,

Chicago Bulls,

Los Angeles

Lakers

CITIES

New York,

Chicago,

Los Angeles,

Boston,

Mention Detection

Entity

Lists

Mentions

FIGURE 1: Example of list-only entity linking

mentions to entity lists (rather than KBs); Fig. 1 describes anexample of list-only EL problem. State-of-the-art method [1]addresses the challenge by merely considering the localcompatibilities between mentions and entities to determinematching pairs, whereas neglecting the global coherenceamong entities.

Example 1: As shown in Fig. 1, there is a piece of textwith mentions Jackson, New York Nicks, Chicago and LosAngeles; and there are 4 sample entity lists to be linkedto, namely Boxers, Coaches, Teams and Cities. Thetask of list-only EL is to link mentions to correct entitiesin the entity lists. It can be seen that entity Chicago andentity Los Angeles in entity list Cities have the same namestrings with mention Chicago and mention Los Angeles inthe text. Because of the high mention-entity compatibility,existing method tends to map mentions Chicago and LosAngeles to entities Chicago and Los Angeles in the entitylist featured Cities. However, the true entities for them areChicago Bulls and Los Angeles Lakers in entity list Teams.Furthermore, it is hard for current method to decide whichentity that mention Jackson should be linked to, since thereare two possible candidate entities with the same name PhilJackson and they are in different entity lists.

Moreover, the dataset used for empirical study might be in-appropriate and need a redesign. Current dataset is comprisedof documents, which contain mentions to be disambiguated,and a group of entity lists, which include the true entities formentions. However, each document only contains a singlemention for disambiguation, which may not reflect the realitywell. A pragmatic scenario may look like the example inFig. 1, where there are four mentions in one document. Ad-ditionally, the entities in different entity lists are dissimilar,making the task much easier to cope with since each mention

may well only have one candidate entity. This also deviatesfrom reality and simplifies the problem.

In short, the shortcomings of the existing list-only ELsolution are two-fold:• Entity coherence within or across entity lists was over-

looked and not leveraged; and• Results were supportless for lack of appropriate dataset

and deliberate experiment design.We close the gap and address the deficiencies in this

article. In particular, we propose to solve list-only EL task bytaking account of the correlations in entities and convertingthe disambiguation problem to a graph problem. We showthe merits of graph-based list-only EL by referring to theexample in Fig. 1. It is easy to map mention New York Knicksto entity New York Knicks in the entity list featured Teams.Then by considering the interdependence of entities in thesame list Teams, mention Chicago will be mapped to entityChicago Bulls, and mention Los Angeles will be mapped toentity Los Angeles Lakers. Additionally, by further takinginto account cross-dependence of entities across differententity lists, entity Phil Jackson in the Coaches entity list,rather than entity Phil Jackson in the Boxers entity list, willbe chosen as the target entity for mention Jackson.

To implement graph-based list-only EL, we mainly carrythrough the following three steps. (1) Pre-processing—including an optional named entity recognition process andthe candidate entity generation process. This step formalizesraw texts and produces mentions and candidate entities asinputs for later steps. (2) Entity information enrichment. Thedescriptions of entities are enriched by collecting represen-tative texts from the inputs, which in turn enable the estab-lishment of coherence among entities. (3) Graph-based entitydisambiguation. An entity graph is constructed by integratingoutputs from earlier steps. We propose a graph-based algo-

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rithm Gloel, which implements Personalized PageRank todetermine how likely an entity is the target entity by takinginto consideration both coherences among entities, and com-patibilities between mentions and entities. The outputs are alist of pairs comprised of mentions and their most possibleentities.

Furthermore, we put forward a new procedure to constructdatasets applicable to evaluating list-only EL. The experi-mental results in this new dataset validate the effectiveness ofgraph-based linking, a popular method of collective linking,and the in-depth analysis shows that compared with existinglist-only linking method, our graph-based solution achievesbetter performance in list-only EL task.

Contributions. The main contributions of this article canbe summarized into three ingredients:• We motivate to revise list-only EL by taking into ac-

count relations between entity lists, i.e., global coher-ence, in addition to local compatibilities between men-tions and entities.

• We tackle the problem by a graph-based method and of-fer a new algorithm Gloel, where Personalized PageR-ank is adopted to capture global coherence among can-didate entities.

• A new dataset construction procedure is presented tocater to the redefined task, and Gloel is experimentallyevaluated on top of it, and shown to outperform state-of-the-art method.

Organization. This paper is organized as follows. InSection 2, the new definition of list-only EL problem andthe methodology, which contains three steps, are elaborated.New dataset construction and experiment results are detailedin Section 3. Section 4 summarizes related work and bridgeslist-only EL with conventional KB-oriented EL, followed byconclusion in Section 5.

II. METHODOLOGYWe start with defining the proposed problem. Existing workdefined list-only EL as mapping a single mention mi indocument di to the corresponding entity ei,j ∈ Ej in theentity lists. Nonetheless, on the one hand, in most real-life documents, there are more than one mention, differen-tiating this definition from reality. On the other hand, theambiguity between entity lists is not stressed, which canturn the problem of mapping mentions to a group of highlyambiguous entities into determining whether the mentionshave corresponding entities in the entity lists. And the latteralso deviates from the original motivation of EL task, whichcentres on disambiguating mentions from several possiblemeanings. We will further elaborate the definition of ambigu-ity between entity lists via mathematical equations in Section3.

As a consequence, it is vital to extend the definition ofthis task so as to cater to broader scenarios. Specifically, weformalize list-only EL problem as follows.

Definition 1 (List-only entity linking): Given a set of doc-uments D = {d1, . . . dn}, each of which contains a set ofmentions Mi = {mi1, . . .mis}, an ambiguous set of entitylists E = {E1, . . . El}, the task is to determine the mostpossible entity eij ,k ∈ Ek for each mention mij , or returnNIL if there is no corresponding entity.Note that the set of entity lists has to be ambiguous to followthe motivation of EL task. In other words, for the majority ofentities, there ought to be at least one more ambiguous entityin the entity lists.

We take the example in Fig. 1 to explain the definition.There are four mentions to be disambiguated in the docu-ment. By utilizing list-only EL, mentions New York Knicks,Chicago, Los Angeles should be mapped to entities New YorkKnicks, Chicago Bulls, Los Angeles Lakers in the entity listfeatured Teams respectively, instead of New York, Chicago,Los Angeles in the entity list featured Cities. And men-tion Jackson should be linked to entity Phil Jackson in theCoaches entity list, rather than entity Phil Jackson in theBoxers entity list.

Overview. The specific procedure for graph-based list-only entity linking includes three steps, namely, pre-processing, entity information enrichment and graph disam-biguation. As shown in Fig. 2, the former two steps generateinputs, based on which the entity graph is constructed andGloel is performed to determine results.

A. PREPARATION FOR GRAPH INPUTThis subsection presents treatment of raw text data andgeneration of inputs for graph construction.

1) Pre-processingIn the pre-processing step, mentions in the text are detectedand the candidate entities are also generated.

Specifically, the initial input for EL is a set of raw doc-uments, either with specified mentions to be disambiguatedor without. Under the circumstance where mentions are notpointed out, Named Entity Recognition (NER) should be har-nessed to finish the mention detection task. State-of-the-artNER methods utilize Neutral Networks and Deep Learningtechniques to achieve better performances, whereas they havenot been widely used yet on account of the freshness andcomplexity. Instead, Stanford NER Tagger, a NER tool whichis less accurate but maturer, embraces higher popularity intasks involving but not focusing on NER. In our experiment,we have already extracted the mentions during dataset con-struction process.

After obtaining mentions, the following step is to retrievepossible candidate entities for each mention. Take Fig. 1 forinstance, for mention Chicago, both entities Chicago Bullsand Chicago should be generated as candidates. In order toimprove recall and generate more candidate entities, mostKB-oriented EL methods tend to take advantage of namedictionaries embedded in KBs, or use alias dictionaries builtfrom collecting Wikipedia redirecting and disambiguation

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Pre-

processing

Entity

Information

Enrichment

Docu

ments

Entity Lists Gloel

Mention-

Entity

Matching Pairs

Graph

Construction

FIGURE 2: Flowchart of graph-based list-only EL.

pages. However, considering the limited number of targetentities and sparse information of entity lists, we design aset of simple but efficient string matching rules for entitygeneration, as is shown in Table 1. In the examples, the leftare mentions while the right are candidate entities.

TABLE 1: String matching rules

Rules ExamplesContainment Chicago → Chicago Bulls

Partial Matching President Trump → Donald TrumpLA → Los Angeles

Alternative Names National Capital → Washington, D.C.Smiley → Miley Cyrus

The generated candidate entities for mention mij are rep-resented by Can(mij). Noteworthily, we adopt candidate-pruning policy to ensure that a mention will not have two ormore candidates from the same list, since entity list is utilizedto help candidate entity within it to compete with entitiesfrom other lists, and choosing among candidate entities fromthe same list will render coherence within entity list useless.

2) Entity Information EnrichmentSolely relying on co-occurrences between entities is notenough to establish relations among entities, let alone seman-tically bridge mentions with candidate entities. Therefore, weenrich information on entity side by selecting representativesderived from input documents.

Given input documents D = {d1, . . . dn} , the mentionsMi = {mi1, . . .mis} in each di, a set of entity lists E ={E1, . . . El}, the enrichment process should collect a set ofhighly relevant and representative texts T r = {tr1, . . . trh}around mentions for Er, which can be achieved by harness-ing co-occurrences of entities in the same entity list.

Specifically, the idea is that, since a document is not onlycomposed of mentions, but also a lot of other irrelevant in-formation, we merely extract the texts around all mentions inall documents as candidate representatives τ to avoid noisyinformation. If a candidate representative tp ∈ τ containsmany entity names from the same entity list Er, chances arethat it indeed shares the same category or topic with entitylist Er, and the mention mp in candidate representative tp isthus much more likely to refer to the candidate entity fromEr. Consequently, tp is a representative of Er and the text intp can be used to enrich the textual descriptions of entities inEr.

We further illustrate the method in Fig. 3. Note that in eachcandidate representative, the bold text represents a mention,and the rest texts are its surroundings. Given an entity listCities and the entity Chicago, the goal is to collect rele-vant representatives for Chicago from documents, which arethen used to enrich representatives of entity list Cities.In Document: United Paramount Network, there are threecandidate representatives, two of them contain name stringChicago. However, Candidate Representative 1 includes noextra name strings of other entities from the entity list, thusmight not refer to Entity List Cities. In contrary, both NewYork and Los Angeles co-occur with Chicago in CandidateRepresentative 3, indicating the high possibility that it isa true representative for Entity List Cities. Switching toDocument: Gotham City, both Candidate Representativescontain name string Chicago. Despite the fact that CandidateRepresentative 4 is derived from mention Chicago, we cannotconsider it as a representative due to lack of co-occurrencesinformation. Conversely, containing several name stringsfrom entity list Cities, Candidate Representative 5 is cho-sen as a representative, even though it is built surroundingmention Detroit.

B. GRAPH CONSTRUCTION AND DISAMBIGUATIONIn this subsection, we illustrate the construction of candidateentity graph, followed by the description of our proposedalgorithm Gloel, which takes advantage of PersonalizedPageRank so as to determine target entities.

1) Graph Construction

Through the pre-processing step, mentions and their candi-date entities are obtained. Then after enriching textual de-scriptions in the entity side, the compatibility score betweeneach mention and corresponding candidate entity can becalculated in terms of text similarity. Previous list-only ELranked the candidate entities for each mention merely basedon mention-entity compatibility scores, thereby producingthe results accordingly. We argue that the judgement simplydepending on compatibility score is not convincing enoughbecause the coherence among entities is ignored, which playsan indispensable role in the linking process. For instance, asis shown in Fig. 1, it is easy to map mentions Chicago andLos Angeles to the Cities entities Chicago and Los Ange-les due to the short text information and high name stringsimilarity. Provided that the candidate entity coherence is

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Document : United Paramount Network

CITIES

Chicago,

New York ,

Los Angeles,

Boston,

For example, WFLD in Chicago moved the 4Kids TV schedule.

Candidate Representative 1

Milwaukee affiliate WCGV branded as "Channel 24" from 1998.

UPN is one of broadcast networks not having had O&O in three

largest media markets, New York, Los Angeles and Chicago.

Document : Gotham City

Nolan has stated that Chicago is the basis of his portrayal of Gotham.

Locations used as inspiration or filming locations for Gotham City in

the live-action Batman films and television series have included

Chicago, Detroit, Los Angeles, New Jersey, and New York.

Entity List





FIGURE 3: Example of entity information enrichment.

considered, the high interdependence among Teams entitiesNew York Knicks, Chicago Bulls, Los Angeles Lakers wouldlead to the correct answers for mentions Chicago and LosAngeles.

To better capture the correlations among entities, similar tomany existing KB-based EL methods, we construct an entitygraph, which is depicted in Fig. 4. The definition of entitygraph is defined as follows:

(m1,e1)

(m1,e2)

(m1,e3)

(m2,e1) (m2,e1)

(m2,e3)

(m3,e1)

(m3,e2)

FIGURE 4: Entity graph.

Definition 2 (Entity graph): An entity graphG = {V,E} is aweighed graph, in which the nodes V represent all candidateentities, with their source mentions specified, and edges Einclude relations between entities.It is noteworthy that we differentiate the mentions with iden-tical name strings even though they might appear in the samedocument, and similarly, by specifying the source mentionof nodes, the candidate entities with the same name but

generated from different mentions are also treated differently.In this way, the situations where there are duplicate nodes,either caused by mentions or entities, can be avoided. In themathematical form, we represent the r-th candidate entity formentionmij in document di as eij,r, which clearly shows thesource mention mij of candidate entity eij,r.

With reference to edges, following the tradition in KB-based EL and adapting it to list-only problem, we connecttwo nodes with an edge under three circumstances: (1) Thename strings of the two entities are in the same entity listE ∈ E , and in this case, the edge weight is defined as1. (2) The name strings of the two entities simultaneouslyappear in at least one candidate representative t ∈ τ . (3) Thename strings of the other entities in the entity lists thesetwo entities separately belong to, simultaneously appear inat least one candidate representative t ∈ τ . The first twokinds of relations are termed as explicit relations, while thethird method of adding edges among entities, named implicitrelations mining, leverages the unique characteristic of entitylist — that the rest entities E

′

i = Ei\{ej} in the same entitylist Ei can help mine more correlations for entity ej even ifej is in the long tail. As for edge weight, which is definedbelow, takes into account both explicit and implicit relationsbetween entities. Furthermore, the edges among candidateentities with the same source mention are pruned so as toeliminate the influence generated by competitors themselves.

We further assign initial node weight ini(v) and edgeweight on the graph. The initial node weight ini(v) is definedas the compatibility score between candidate entity and itssource mention, while edge weight is determined by relationscore between the entities on the two sides of the edge.

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The specific approaches to calculate compatibility score andrelation score are:

Compatibility score. Given a document di, and mij , amention contained in di, suppose eij ,r ∈ Er is a candidateentity for mij and T r = {tr1, . . . trh} is the set of represen-tative texts for Er. The compatibility score φ(mij , eij,r) canbe measured by the following equation

ini(mij , eij,r) = φ(mij , eij,r) =1

|T r|

|T r|∑p=1

Sim(mij , trp).

(1)Since entities in the same entity list share the same repre-

sentative texts, which are collected according to the methodproposed in former section, calculating compatibility be-tween a pair of mention mij and candidate entity eij ,r ∈ Ercan be converted to computing the average text similaritySim between texts surrounding mention mij and all the textrepresentatives T r of candidate entity eij ,r.

There are many ways to measure text similarity Sim andin this paper, we choose to compute the similarity betweenembedding vectors of two texts, which is represented asE(mij , t

rp). Additionally, we also regard the name string

similarity between mention and candidate entity as an appro-priate indicator, and it is denoted as N(mij , eij ,r ). Thus, theCompatibility score equation is converted to

φ(mij , eij,r) = αN(mij , eij ,r ) + β1

|T r|

|T r|∑p=1

E(mij , trp).

(2)In the equation above, α and β are the weight coefficientsbalancing the importance of text similarity and name stringsimilarity.

Relation score. Given two entities epi ∈ Ep, eqj ∈ Eq

(We merely consider relationships among entities when cal-culating Relation Score, which is mention-irrelevant, thus weneglect the mention here), the Relation Score is denoted inthe following equation

Rel(epi , eqj)=

ηO(epi , eqj) +

θM

Ep−i∑u

Eq−j∑v

O(epu, eqv), p 6= q;

1, p = q,(3)

where

O(ei, ej) =|Occur(ei)∩Occur(ej)||Occur(ei)∪Occur(ej)| ,

Occur(e) = {t|e ∈ t, t ∈ τ}, andM = (|Ep|−1)(|Eq|−1).We illustrate equations above as follows: Occur(e) de-

notes the occurrences of entity e in all candidate repre-sentatives τ , since compared with noisy textual informationcontained in the whole documents, merely considering textsaround mentions (candidate representatives) can improve theaccuracy. The Co-occurrence Frequency O(ei, ej) of twoentities ei and ej is defined as the number of candidate repre-sentatives they both occur in, divided by all the candidate rep-resentatives they either occur in together, or separately. As for

the Relation Score Rel(epi , eqj) of two entities epi ∈ Ep, e

qj ∈

Eq , if p equals q, which means epi and eqj are from the sameentity list, we set the relation score as 1. Otherwise, the scoreis composed of two parts. The first component is the directCo-occurrence Frequency of these two entities, multipliedby a weight factor η, which indicates explicit relations. Theimplicit relations are represented by indirect Co-occurrenceFrequency, which is the second component with a coefficientθ, and it takes into account the co-occurrences of the restentities in Ep and Eq in a pair-wise fashion.

Furthermore, as is shown in Fig. 4, there are three kindsof lines. The bold line represents that entities on the twosides are in the same entity list, and the Relation Score is 1.The dotted line denotes that two entities merely have implicitrelations, while the normal line requires that there are explicitrelations between entities.

It is noteworthy that, different from traditional KB-oriented EL problem which merely considers the direct rela-tions between two entities, we extend the definition by takinginto account the contribution made by relations between twoentity lists as well, and represent them as implicit relations oftwo entities. The detailed approach to quantitatively describethe implicit relations is embodied in the equations above.

2) Ranking Mention-entity PairsGiven a weighed entity graph Gi of document di, the targetis to find the most likely entity eij,k from a group of entitiesfor each mention mij in document di. In line with popularmethods proposed in KB-oriented EL [3], we propose graph-based list-only entity linking algorithm, namely Gloel, whichutilizes Personalized PageRank to depict the coherenceamong candidate entities.

Specifically, we assign a vector p(vs) with length n toeach node vs to represent the results of a PageRank processstarting from vs. To better capture the coherence amongentities within the same document, instead of regarding thesimilarity between the vectors of nodes as the coherencescore, we define it as how a candidate entity fits in thedocument. To enable the definition, a n-length vector p(di) isalso assigned to document di, representing the results of thePageRank process initiating from a group of unambiguousnodes. Consequently, the coherence score of a candidateentity eij,r for mention mij in document di is defined as

ψ(eij,r, di) =p(vij,r)p(di)

|p(vij,r)||p(di)|. (4)

We first elaborate the random walk process initiating froma single node, then extend it to calculating document PageR-ank vector. The PageRank algorithm, based on randomwalk theory, is firstly proposed to measure the importance ofweb pages by counting the number and quality of links to thispage. It has been applied to EL problems in recent years, andhas achieved great performance [3]–[6]. The basic elementsof PageRank include initial vector r0, transition matrix A,and preference vector s. Note that in our method, r0 = s.

6 VOLUME 4, 2016


Transition Matrix A is the same in both individual andcollective processes, the value at ith row and jth column isdefined as

Aij =Rel(ei, ej)∑

ek∈Edges(ei)Rel(ei, ek). (5)

where Edges(ei) represents the edges connected to entity ei.When computing the vector p(vt) for a single node vt ,

r0 = s = (0 . . . 0, 1(tth), 0 . . . )n, which means that r0 and sare identical n-length vectors, the position t of the vector isassigned with 1 and the rest are endowed with 0.

The situation is slightly more complicated as for documentPageRank vector p(di). Firstly, we regard a candidate entityeij,r as a unambiguous one if it satisfies one of the followingconditions:

1) eij,r is the only candidate entity of mention mij andini(eij,r) is above threshold µ. The unambiguous enti-ties of this kind is endowed with initial weight λ.

2) When there are more than one candidate entities andeij,r is the candidate entity with the largest initialvalue, suppose e

′

ij,r is the candidate entity with thesecond largest initial value. It suffices that ini(eij,r) −ini(e

′

ij,r) ≥ ν. The initial weight of this kind is κ.3) If there are no candidate entities meeting the conditions,

all the candidate entities will be added to the unambigu-ous entities set, with the same weight endowments.

After obtaining unambiguous entities set, the actual weightcan be assigned via normalization of initial weight values.Note that in graph, unambiguous entities are presented asequivalent nodes, and by placing the actual weight of unam-biguous nodes in the corresponding positions of the n-lengthvector, we can attain r0 and s for document accordingly.Furthermore, we adopt an iterative disambiguation approach.In other words, after erasing ambiguity for each mention, thechosen result entity will be regarded as unambiguous andadded to the unambiguous entities set, with initial weight ofι. Afterwards, the document PageRank vector will be re-computed by utilizing the new unambiguous entities set.

With initial vector r0, transition matrix A, and preferencevector s defined as above, the Personalized PageRank ispresented as following

rt+1 = (1− ρ)×A× rt + ρ× s. (6)

In the equation above, t represents the tth iteration, and ρdenotes the probability that the random walk process jumpsout of the original iteration and starts from a new vector,which is usually set at 0.15. Normally, the restarting nodesare all nodes in the graph, and the weights in vector s are thesame, which equal to 1

|V | . Nonetheless, in this work, vector sis personalized and set as the same with initial vector, whichmeans that the random walk merely restarts from the initialnodes, eliminating the effect from other nodes. When theiterative calculation reaches to a stage where rk does notchange any more or the variation is within a minimal range,we consider that it converges and p(vs), p(di) are thereby

attained. At last, we formalize the list-only EL problem in amathematical way:Definition 3 (List-only entity linking in mathematical form):Given a set of documents D = {d1, . . . dn}, each of whichcontains a set of mentions Mi = {mi1, . . .mis}, an am-biguous set of entity lists E = {E1, . . . El}, the task isto determine the most possible entity eij ,k ∈ Ek for eachmention mij , and

eij ,k = argmaxeij,r∈Can(mij)

(γφ(mij , eij,r) + δψ(eij,r, di)). (7)

where γ and δ are two weight coefficients balancing theweight between mention-entity compatibility score and entitycoherence score. NIL will be returned if there is no corre-sponding entity.

III. EXPERIMENTS AND RESULTSConsidering the deficiency in current list-only EL dataset,we propose a similar but more comprehensive approachfor dataset construction. Then our method is validated viaexperiments on this dataset and the merits are highlightedthrough comparison with state-of-the-art method.

A. DATASETThe current list-only EL dataset [1] contains 11065 doc-uments and 7 groups of entity lists, with 139 entities intotal. Each document merely includes a single mention tobe disambiguated. In addition, the entity lists cover the cat-egories of President, Company, University, State, Character,Brand, Restaurant, and the entities in different entity lists aredisparate both in terms of surface forms and true meanings.

There are two shortcomings in current dataset. For onething, each document merely contains a single mention to bedisambiguated, which does not fit in most real-life occasions.For another, the target entity lists are not ambiguous enough,giving rise to the situation that most mentions merely haveone candidate entity, and the disambiguation problem isconverted to judging whether this sole candidate entity is trueor not. Take entity Apple in Company entity list shown inthe dataset of [1], there is no other similar entities in the setof entity lists. As a result, when given a mention Apple, thecandidate entity for it will only be Apple in Company entitylist, and the problem is transformed into deciding whether themention can be mapped to entity lists or not.

In order to overcome the deficiencies, we propose tomine target entity lists and collect documents. The entitylists can be constructed both manually and automatically,but the ambiguity must be ensured. Given two entity listsEm = {e1,m, . . . ei,m} and En = {e1,n, . . . ej,n}, for Em,the ambiguity caused by the existence of En is defined as

Amb(Em, En) =1

|Em|∑

ei,m∈Em

argmaxej,n∈En

amb(ei,m, ej,n).

(8)

Note that amb(ei,m, ej,n) represents the ambiguity between

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two entities in different entity lists. Many approaches canbe utilized to measure it, and in this paper, we harnessthe matching rules defined in the candidate entities retrievalsection. If matching rules are satisfied, we endow 1 toamb(ei,m, ej,n). Otherwise, the value is determined by namestring similarity. Furthermore, the reason why only the high-est ambiguity value for ei,m is chosen lies in the fact that wemerely need to assure ei,m has one ambiguous competitor toavoid the situation as the example above.

For the whole entity lists set E = {E1, . . . El}, theambiguity is denoted as

A(E) = 1

|E|∑Ep∈E

argmaxEq∈E\Ep

Amb(Ep, Eq). (9)

Again, for each entity list Ep, we only consider the highestambiguity it has with the rest entity lists in E , since construct-ing a entity lists set with high ambiguity between each pairof entity lists is nearly impossible.

Referring to [1], we generated raw entity lists by utilizingNeedleSeek 2, which were then filtered and processed ac-cording to the definition of ambiguity. At last, seven entitylists with 70 entities in total were generated. The ambiguityof newly-constructed entity lists set is 0.965, calculated ac-cording to the equations given above, while the value 3 forentity lists set in [1] is 0.267. Part of the newly constructedentity lists are presented in Table 2.

TABLE 2: Part of entity lists.

Ei Entities1 Atlanta, Chicago, Boston, Houston, New York, Detroit...2 Atlanta Hawks, Chicago Bulls, Boston Celtics, Houston Rockets...3 Atlanta Braves Chicago Cubs, Boston Red Sox, Houston Astros...4 Toyota Camry, Ford Ikon, Tata Indica, Honda Accord...5 Toyota, Ford, Tata Motors, Honda, Hyundai, Chevrolet...6 Cambridge, Oxford, St Andrews, Warwick, London...7 University of Cambridge, University of Oxford...

As for building the documents dataset, we emphasize thatthere have to be at least two mentions in the same documentto enable the construction of candidate entity graph. Other-wise there will be no difference between independent linkingmethod and the proposed collective linking method based ongraph.

To be specific, we utilized wikilinks in Wikipediato obtain the documents. For each entity ei,k in en-tity list Ek, its referent Wikipedia page was deter-mined in the first place. For instance, the Wikipediapage of entity Atlanta is en.wikipedia.org/wiki/Atlanta.Then we randomly retrieved 1,000 Wikipedia pagesdirecting at ei,k via the WhatLinksHere page. Asfor Atlanta, the url of its WhatLinksHere page isen.wikipedia.org/wiki/Special:WhatLinksHere/Atlanta. Afterconducting the same operation for all the entities in entity

2http://needleseek.msra.cn3Since the author did not offer the full entity lists information, we compute

the ambiguity of the segmental entity lists presented in the previous work.

list Ek, the links appearing in at least three entities’ 1,000Wikipedia pages were selected and the web pages texts theyrefer to were considered as documents. In this way, we canaffirm that each document involves at least three mentions.Table 3 describes the specific information of documents andmentions.

B. RESULTS AND ANALYSESWe compare Gloel and the method utilized in [1] (denoted asIndependent) on the dataset we create. The results are shownin Table 4 and the settings of parameters are listed as follows:α = 0.4, β = 0.6, η = 0.7, θ = 0.3, γ = 0.5, δ = 0.5, λ =0.5, κ = 0.4, ι = 0.3.

TABLE 3: Dataset statistics

Target Ei #documents #mentions1 156 7312 535 3,4503 528 3,0544 41 1085 151 7426 97 4727 115 564

Total 1,623 9,121

The measurements we adopt are the same with the metricsin [7], namely Precision, Recall and F1. Precision takes intoaccount all entity mentions that are linked by the systemand determines the correctness. Recall on the other hand,considers all the mentions should be linked, and reflectsthe fraction of correctly linked mentions. F1 is a balancedindicator of Precision and Recall.

We first report the results on original dataset. As is de-picted in Table 4, Gloel outperforms independent EL methodin all occasions, with a overall F1 gain at 1.1%. Nevertheless,it is evident that both methods achieve high Precision, Recalland F1 scores. This can be justified that most mentions in thedocuments appear in the same name string form as the entityname strings. For instance, in documents containing mentionreferring to entity University of Cambridge, the name formof the mention is also University of Cambridge, thus thehigh name string similarity basically guarantees the correctmatching and rules out the possibility of other candidateentities. Plus, this does not fit in situations of most textsources other than Wikipedia. In news reports concerningUniversity of Cambridge, it constantly goes by the nameCambridge as in sentence Cambridge beats Oxford in termsof computer science. In these cases, the probability of gen-erating result entity Cambridge is enhanced significantly andthe disambiguation difficulty also rises up.

As a consequence, we corrupted the dataset to observe thecorresponding results produced by these two methods. Toachieve corruption and increase ambiguity, we replaced men-tion names of the entities in lists 2,3,5,7 to the correspondingambiguous names in lists 1,4,6. For instance, the mentionnames Atlanta Hawks and Atlanta Braves were substitutedby Atlanta. Considering the fact that after corruption, thename string similarity (in [1] the NER results) might be of

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TABLE 4: Experimental results on original dataset

Method E1 E2 E3 E4 E5 E6 E7 All

IndependentP 0.914 0.996 0.998 0.818 0.998 0.667 0.997 0.962R 0.990 0.984 0.988 0.998 0.959 0.989 0.585 0.959F1 0.950 0.990 0.993 0.900 0.979 0.797 0.734 0.961

GloelP 0.997 1.000 0.998 0.931 1.000 0.675 0.997 0.974R 0.997 0.998 0.997 1.000 0.981 0.992 0.598 0.971F1 0.997 0.999 0.998 0.964 0.990 0.803 0.748 0.972

no use and possibly lead to negative contributions, which wasunfair for Independent results, we merely took into accountthe embedding vectors similarity in terms of mention-entitysimilarity calculation and altered the corresponding parame-ter setting. It is noteworthy that, for each corruption degree,we generated five corrupted corpus and reported the averageresults so as to increase the stability and persuasiveness ofthe outcomes.

The results of 50% corruption in Table 5 are generatedafter half of the entities’ corresponding mention names inlists 2,3,5,7 get replaced. For fair comparison, the parametersare optimized for separate methods. As can be seen, the gapbetween the results of Independent and Gloel widens. Gloelachieves better outcomes with overall F1 score at 90.9%,while the overall F1 value of previous method is 76.5%,hence validating the superiority of the proposed method.

We further conducted 25% and 75% corruption on thedataset and Fig. 5 dynamically depicts the F1 scores of thesetwo methods under corruption. With input texts getting moredifficult, the results of EL based solely on mention-entitycompatibility decline rapidly, while Gloel, a method basedon graph, still yields robust results with smaller decreases.

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1

0 % 2 5 % 5 0 % 7 5 %

F1

SC

OR

E

DEGREE OF CORRUPTION

Independent

Gloel

FIGURE 5: F1 score of Ind. and Gloel over corrupted dataset

The following instances are presented to show the im-provement made by Gloel and the remaining errors. Theimprovements mostly take place in documents where half ormore than half of the mentions are unambiguous, in whichcases the merits of Gloel can be best embodied. A case inpoint is in Example 2, whether the unambiguous mentionsCalifornia Institute of Technology and Stanford Universitycan help the disambiguation of mentions Oxford and Cam-bridge.Example 2: But Oxford and Cambridge saw significant in-creases in their total institutional income - up 24% and 11%

respectively while their nearest rivals, the California Instituteof Technology and Stanford University saw falls in income.

Nevertheless, in scenarios where nearly all mentions in thedocument are highly ambiguous, Gloel seems to be unable toincrease the accuracy, as is shown in Example 3.Example 3: Boston Celtics won over Toronto by 95-94.Other results: Detroit vs Miami, 112-103. Indiana vs Houston95-118.The bad performance of Gloel on this type of text can bejustified that, without sufficient texts, even human beingscan get confused, let alone an algorithm relying on the textinformation.

IV. RELATED WORKIn this section, we brief related work, and discuss the differ-ences and connections between list-only EL and traditionalEL.

A. LIST-ONLY ENTITY LINKINGOver recent years, in accordance with the emergence ofvarious text sources, EL tasks in new forms have been putforward. List-only EL task, first formally defined by Linet al. [1], is the task of mapping mentions to a group ofentity lists, rather than complete KBs. Lin et al. selectedseed mentions for each entity list to bridge the gap betweenmentions and non-informative target entities, and then con-ducted the independent linking process to determine finalresults. Noticing that they merely harnessed entity lists co-occurrences information for generating entity descriptions, inthis work, we further utilize the co-occurrences informationto model entity relatedness and integrate it in the entity graph,which yields a more robust and accurate EL framework whenconfronting difficult input texts.

There are other new forms of EL problems which aresimilar to the list-only task. One is the Target Entity Dis-ambiguation problem [2], [8]. The main disparity is that thefocus of Target Entity Disambiguation task lies in findingdocuments related to the entities given a entity list, whereasthe starting point of list-only EL task is to eliminate the ambi-guity in documents by using entity lists. Another similar taskis the Named Entity Disambiguation with Linkless KBs [9].Different from the mere entity lists in our task, there are stilltextual descriptions for entities in Linkless KBs.

B. KNOWLEDGE BASE ORIENTED ENTITY LINKINGEarlier work on EL focus on the situation where abundantinformation exists on the entity side. Specifically, KBs such

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TABLE 5: Experimental results on 50% corrupted dataset

Method E1 E2 E3 E4 E5 E6 E7 All

IndependentP 0.968 0.735 0.987 0.167 0.968 0.867 0.639 0.766R 0.856 0.994 0.624 0.944 0.287 0.318 0.961 0.764F1 0.909 0.845 0.765 0.284 0.443 0.465 0.768 0.765

GloelP 0.641 1.000 0.998 0.943 0.967 0.555 0.995 0.910R 0.997 0.966 0.899 0.769 0.985 0.992 0.332 0.907F1 0.781 0.982 0.946 0.847 0.976 0.712 0.497 0.909

as YAGO, Freebase and Wikipedia, offer rich semantic struc-tures among entities as well as detailed textual descriptions,thus resulting in robust and accurate linking procedure. KB-oriented EL work can generally be divided into independentand collective methods.

In the former approach, mentions are disambiguatedmerely according the similarity between mentions and en-tities, and the problem is transformed into candidate entitiesranking so as to obtain the most possible result. The similar-ity is mainly measured by lexical features such as bag-of-words of surrounding texts and statistical features such asprior popularities of entities. Then as for ranking process,unsupervised methods [10] calculate cosine similarities offeature vectors and output the results, whereas supervisedapproaches [11], [12] construct classifiers by training onannotated dataset, and the linking process is in the chargeof classifiers when inputs are given. Although methods ofthis kind can achieve good results, semantic coherenceswithin entities are neglected, which prove to be essential inimproving overall performances.

With respect to collective linking methods in conventionalEL task, most of them assume mentions in the same docu-ment are semantically coherent, which also should fit in thetextual topic of the whole document. Therefore, the resultingentities also are expected to have high relatedness and theproblem is in turn converted to find matching pairs max-imizing the coherence. Cucerzan [13] proposed to harnessWikipedia categories to model coherence among entities,while Milne and Witten [14] reckoned normalized GoogleDistance as another useful tool for measurement, whichwas utilized by Kulkarni et al. [15] to form integer linearprogramming problem so as to collectively obtain results.Hoffart et al. [16] defined keyphrase relatedness to captureentity coherence, and proposed to construct a mention-entitygraph, on which dense sub-graph generation algorithm wasput forward to determine the sub-graph containing one-to-one mention-entity matches. The method of re-formalizingthe linking problem by constructing mention-entity or entity-only graph distinguished itself among other works due toits capability to integrate both local similarity informationbetween mentions and entities, along with the coherenceinformation among entities. Based on this, several works [3]–[6] proposed and applied modified graph algorithm on thegraph, which improved the disambiguation accuracy and theadaptability to difficult texts. Overall, the collective linkingmethods generally perform better than the independent coun-terparts in terms of conventional KB-oriented EL.

C. DISCUSSION ON DIFFERENCES AND CONNECTIONSThere are indeed many similarities between these two linesof works, despite of the evident differences. The disparitymainly lies in the information on the entity side. Regardingconventional KB oriented EL, entities have rich and well-structured descriptions offered by KBs, in terms of bothtext description and internal links among entities [14]–[16].Thereafter, researchers merely need to filter valuable infor-mation to improve linking results. In stark contrast, withrespect to list-only scenarios, the mere information existingon the entity side is the co-occurrences among entity namestrings in the same entity list, which in turn requires informa-tion mining and enrichment. In this paper, to avoid help fromstructured or semi-structured knowledge source, the datasetitself is leveraged to harvest the relevant relations amongentities, thus fulfilling the entity information enrichment task.

Nevertheless, aside from information mining process, themethods utilized in conventional research can be appliedto this newly-defined problem and will achieve promisingresults. For instance, with disambiguation problem taking theform of graph, [3]–[6] can all be implemented.

Above all, the techniques developed in traditional EL alsoapply in list-only EL problem, and the extra work for thelatter is to mine information on the entity side.

V. CONCLUSIONList-only entity linking task, as a new form of traditional ELproblem, distinguishes itself by the sparse information onthe entity side. In this work, on the one hand, we proposeto utilize entity co-occurrences information to mine bothtextual description of entities and relations among entities, soas to enrich entity information. On the other hand, inspiredby conventional EL methods, we construct an entity graphto capture relations among entities, on which the newlyproposed algorithm Gloel is applied to obtain results. Similarto the situation in traditional EL, our approach, a collectiveEL method based on graph, outperforms independent EL onthe dataset we create for fair comparison.

For future work, we plan to investigate two aspects. One isto consider the situation where an entity appears in more thanone entity list. For instance, Washington, D.C. can appearin entity lists featured American Cities and CountryCapitals.

Another possible research direction is utilizing word em-bedding techniques and deep neural networks to better modelmention-entity compatibility and entity coherence. Specifi-cally, leveraging well-trained word embedding vectors as in-

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puts, Long Short-Term Memory (LSTM) [17] with attentionmechanism [18] could be used to summarize semantic mean-ings of the contexts around mentions and the representativetexts of entities, which can be further harnessed to calculatemore accurate compatibility score.

REFERENCES[1] Y. Lin, C. Lin, and H. Ji, “List-only entity linking,” in Proceedings

of the 55th Annual Meeting of the Association for ComputationalLinguistics, ACL 2017, Vancouver, Canada, July 30 - August 4,Volume 2: Short Papers, 2017, pp. 536–541. [Online]. Available:https://doi.org/10.18653/v1/P17-2085

[2] Y. Cao, J. Li, X. Guo, S. Bai, H. Ji, and J. Tang, “Name list only?target entity disambiguation in short texts,” in Proceedings of the 2015Conference on Empirical Methods in Natural Language Processing,EMNLP 2015, Lisbon, Portugal, September 17-21, 2015, 2015, pp.654–664. [Online]. Available: http://aclweb.org/anthology/D/D15/D15-1077.pdf

[3] Z. Guo and D. Barbosa, “Robust entity linking via random walks,” inProceedings of the 23rd ACM International Conference on Conferenceon Information and Knowledge Management, CIKM 2014, Shanghai,China,November 3-7, 2014, 2014, pp. 499–508. [Online]. Available:http://doi.acm.org/10.1145/2661829.2661887

[4] X. Han, L. Sun, and J. Zhao, “Collective entity linking in web text: agraph-based method,” in Proceeding of the 34th International ACM SIGIRConference on Research and Development in Information Retrieval,SIGIR 2011, Beijing, China, July 25-29, 2011, 2011, pp. 765–774.[Online]. Available: http://doi.acm.org/10.1145/2009916.2010019

[5] A. Alhelbawy and R. J. Gaizauskas, “Graph ranking for collective namedentity disambiguation,” in Proceedings of the 52nd Annual Meeting of theAssociation for Computational Linguistics, ACL 2014, June 22-27, 2014,Baltimore, MD, USA, Volume 2: Short Papers, 2014, pp. 75–80. [Online].Available: http://aclweb.org/anthology/P/P14/P14-2013.pdf

[6] M. Pershina, Y. He, and R. Grishman, “Personalized page rank for namedentity disambiguation,” in NAACL HLT 2015, The 2015 Conferenceof the North American Chapter of the Association for ComputationalLinguistics: Human Language Technologies, Denver, Colorado, USA,May 31 - June 5, 2015, 2015, pp. 238–243. [Online]. Available:http://aclweb.org/anthology/N/N15/N15-1026.pdf

[7] W. Shen, J. Wang, and J. Han, “Entity linking with a knowledgebase: Issues, techniques, and solutions,” IEEE Trans. Knowl. DataEng., vol. 27, no. 2, pp. 443–460, 2015. [Online]. Available:https://doi.org/10.1109/TKDE.2014.2327028

[8] C. Wang, K. Chakrabarti, T. Cheng, and S. Chaudhuri, “Targeteddisambiguation of ad-hoc, homogeneous sets of named entities,” inProceedings of the 21st World Wide Web Conference 2012, WWW 2012,Lyon, France, April 16-20, 2012, 2012, pp. 719–728. [Online]. Available:http://doi.acm.org/10.1145/2187836.2187934

[9] Y. Li, S. Tan, H. Sun, J. Han, D. Roth, and X. Yan, “Entity disambiguationwith linkless knowledge bases,” in Proceedings of the 25th InternationalConference on World Wide Web, WWW 2016, Montreal, Canada,April 11 - 15, 2016, 2016, pp. 1261–1270. [Online]. Available:http://doi.acm.org/10.1145/2872427.2883068

[10] R. C. Bunescu and M. Pasca, “Using encyclopedic knowledge for namedentity disambiguation,” in EACL 2006, 11st Conference of the EuropeanChapter of the Association for Computational Linguistics, Proceedings ofthe Conference, April 3-7, 2006, Trento, Italy, 2006. [Online]. Available:http://aclweb.org/anthology/E/E06/E06-1002.pdf

[11] M. Dredze, P. McNamee, D. Rao, A. Gerber, and T. Finin, “Entitydisambiguation for knowledge base population,” in COLING 2010, 23rdInternational Conference on Computational Linguistics, Proceedings ofthe Conference, 23-27 August 2010, Beijing, China, 2010, pp. 277–285.[Online]. Available: http://aclweb.org/anthology/C10-1032

[12] R. Mihalcea and A. Csomai, “Wikify!: linking documents to encyclopedicknowledge,” in Proceedings of the Sixteenth ACM Conferenceon Information and Knowledge Management, CIKM 2007, Lisbon,Portugal, November 6-10, 2007, 2007, pp. 233–242. [Online]. Available:http://doi.acm.org/10.1145/1321440.1321475

[13] S. Cucerzan, “Large-scale named entity disambiguation based onwikipedia data,” in EMNLP-CoNLL 2007, Proceedings of the 2007Joint Conference on Empirical Methods in Natural Language Processingand Computational Natural Language Learning, June 28-30, 2007,

Prague, Czech Republic, 2007, pp. 708–716. [Online]. Available:http://www.aclweb.org/anthology/D07-1074

[14] D. N. Milne and I. H. Witten, “Learning to link with wikipedia,”in Proceedings of the 17th ACM Conference on Informationand Knowledge Management, CIKM 2008, Napa Valley, California,USA, October 26-30, 2008, 2008, pp. 509–518. [Online]. Available:http://doi.acm.org/10.1145/1458082.1458150

[15] S. Kulkarni, A. Singh, G. Ramakrishnan, and S. Chakrabarti, “Collectiveannotation of wikipedia entities in web text,” in Proceedings of the 15thACM SIGKDD International Conference on Knowledge Discovery andData Mining, Paris, France, June 28 - July 1, 2009, 2009, pp. 457–466.[Online]. Available: http://doi.acm.org/10.1145/1557019.1557073

[16] J. Hoffart, M. A. Yosef, I. Bordino, H. Fürstenau, M. Pinkal, M. Spaniol,B. Taneva, S. Thater, and G. Weikum, “Robust disambiguation of namedentities in text,” in Proceedings of the 2011 Conference on EmpiricalMethods in Natural Language Processing, EMNLP 2011, 27-31 July2011, John McIntyre Conference Centre, Edinburgh, UK, A meetingof SIGDAT, a Special Interest Group of the ACL, 2011, pp. 782–792.[Online]. Available: http://www.aclweb.org/anthology/D11-1072

[17] R. Józefowicz, W. Zaremba, and I. Sutskever, “An empirical explorationof recurrent network architectures,” in Proceedings of the 32ndInternational Conference on Machine Learning, ICML 2015, Lille,France, 6-11 July 2015, 2015, pp. 2342–2350. [Online]. Available:http://jmlr.org/proceedings/papers/v37/jozefowicz15.html

[18] A. M. Rush, S. Chopra, and J. Weston, “A neural attention modelfor abstractive sentence summarization,” in Proceedings of the 2015Conference on Empirical Methods in Natural Language Processing,EMNLP 2015, Lisbon, Portugal, September 17-21, 2015, 2015, pp.379–389. [Online]. Available: http://aclweb.org/anthology/D/D15/D15-1044.pdf

WEIXIN ZENG received the Bachelor degreefrom National University of Defense Technology(NUDT), China, in 2017. He is currently workingtoward the Master degree at NUDT. His researchinterests include knowledge graph and entity link-ing.

XIANG ZHAO was born in 1986. He received thePh.D. degree from the University of New SouthWales, Australia, in 2014. He is currently an As-sistant Professor at the National University of De-fense Technology, China. He has published severalrefereed papers in international conferences andjournals, such as SIGMOD, SIGIR, VLDB Journaland TKDE. His research interests include graphdata management and knowledge graph construc-tion.

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JIUYANG TANG received the PhD degreefrom National University of Defense Technology(NUDT), China, in 2006. He is currently a pro-fessor with NUDT. His research interests includeknowledge graph and text analytics.

HAICHUAN SHANG received B.S. (2007) fromTsinghua Univ., China, and the Ph.D. (2010) fromthe University of New South Wales, Australia. Heis currently a Researcher at the National Insti-tute of Information and Communications Technol-ogy, and also the University of Tokyo, Japan. Hehas published several refereed papers in interna-tional conferences and journals, such as SIGMOD,VLDB/PVLDB, ICDE, EDBT and CIKM. Hisresearch interests include graph data management,

distributed databases and blockchain technology.

12 VOLUME 4, 2016

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Collective List-only Entity Linking: A Graph-based Approach...ABSTRACT List-only entity linking is the task of mapping ambiguous mentions in texts to target entities in a group of