Person Name Disambiguation by Bootstrapping

Presenter: Lijie Zhang

Advisor: Weining Zhang

Outlines

Introduction Motivation Two-stage Clustering Algorithm Experiments

People Name Disambiguation

Given a target name (query q ), search engine returns a set of web pages P={d1, d2, …, dn }

Task: cluster web pages P such that each cluster refers to a single person.

Example: People Name Disambiguation

People Name Disambiguation

A typical solution: Extract a set of features from each document returned by search

engine Cluster the documents based on some similarity metrics on sets

of features Two types of features

Strong features such as named entities (NEs), compound key words (CKWs), URLs

NE: Paul Allen, Microsoft (indicate the person Bill Gates) CKW: chief software architect (a concept strongly related to Bill

Gates) Very strong ability to distinguish between clusters.

Weak features: single words

People Name Disambiguation

Evaluation Metric: F measure Treat each cluster as if it were the result of a

query and each class as if it were the desired set of documents for a query

For class i and cluster j, Recall(i, j)= nij/ni, Precision(i, j)=nij/nj

F(i, j) = (2 * Recall(i, j) * Precision(i, j)) / ((Precision(i, j) + Recall(i, j))

Motivation

Problem of current systems: Using only strong features achieves high precision but low recall.

Proposed solution: two-stage clustering algorithm by bootstrapping to improve the recall value.1st stage: strong features2nd stage: weak features

Two-stage Clustering Algorithm

Input: one query string Output: a set of clusters

1. Preprocessing documents returned by search engine

2. First-stage clustering

3. Second-stage clustering

Preprocessing a Document

Covert HTML files to text files Remove HTML tagsKeep sentences

Extract text around query string Using a window size

Extract strong features (NEs, CKWs, URLs)

Extract Strong Features Use Stanford NER to identify NEs:

a set of sets of names including names of persons, organizations, and places

Compound Key Word (CKW) Features: a set of CKWs Extract compound words (CW): w1w2..wl

Score each CW: Determine CKW based on a threshold of scores.

Extract URLs from the original HTML files exclude URLs with high frequencies

Two-stage Clustering Algorithm

Input: one or more query strings Output: a set of clusters

1. Preprocessing documents returned by search engine

2.1st stage clustering

3. 2nd stage clustering

First stage clustering

1. Calculate the similarities between documents based on these features

2. Use standard hierarchical agglomerative clustering (HAC) algorithm for clustering

Document Similarities

Similarity for NE features and CKW features

avoids too small denominator values in the equation

Document Similarities

Similarity for URLs

Document Similarities

Similarity for NE:

Similarities for NE, CKW, and URL

First stage clustering

1. Calculate the similarities between documents based on these features

2. Use standard hierarchical agglomerative clustering (HAC) algorithm for clustering

HAC algorithm

Starts from one-in-one clustering, i.e. each document is a cluster

Iteratively merge the most similar cluster pairs, which similarity is above a threshold.

Cluster similarity:

Two-stage Clustering Algorithm

Input: one or more query strings Output: a set of clusters

1. Preprocessing documents returned by search engine

2.1st stage clustering

3. 2nd stage clustering

Second Stage Clustering

Goal: Cluster documents still in one-in-one clustering after the first stage clustering

Idea of bootstrapping algorithm: Given some seed instances, finds patterns useful to extract such

seed instances; Use these patterns to harvest new instances, and form the

harvested new instances new patterns are induced. Instances correspond to documents Patterns correspond to weak features: 1-gram, 2-gram in

experiment

Second Stage Clustering

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Experiments Setup Dataset: WePS-2

30 names, each has 150 pages The same page can refer to two or more entities;

Evaluation Metrics [5] Multiplicity precision and recall between document e and e’

C(e) is predicted cluster of e, L(e) is the cluster assigned to e by the gold standard

Example of Evaluation Metrics

L(1)={A,B} L(2)={A,B}C(1)={ct1, ct2} C(2)={ct1, ct2}

L(1)={A,B} L(2)={A,B}C(1)={ct1} C(2)={ct1, ct2}

L(1)={A,B} L(2)={A,B}C(1)={ct1,ct2,ct3} C(2)={ct1, ct2,ct3}

Experiments Setup

Evaluation Metrics Extended B-Cubed precision (BEP) and recall (BER)

Experiments Setup

Baselines:First stage clustering: all-in-one, one-in-one,

combined baseline (each doc belongs to one cluster from all-in-one and one from one-in-one).

Second stage clustering: TOPIC algorithm, CKW algorithm

Experiments Results

References[1] A. Bagga and B. Baldwin. Entity-based cross-document coreferencing using the vector

space model. In Proceedings of COLING-ACL 1998, pages 79–85, 1998.[2] C. Niu, W. Li, and R. K. Srihari. Weakly supervised learning for cross-document

person name disambiguation supported by information extraction. In Proceedings of 42nd Annual Meeting of the Association for Computational Linguistics (ACL-2004), pages 598–605, 2004.

[3] X. Liu, Y. Gong, W. Xu, and S. Zhu. Document clustering with cluster refinement and model selection capabilities. In Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, pages 191–198, 2002.

[4] X. Wan, M. L. J. Gao, and B. Ding. Person resolution in person search results: WebHawk. In Proceedings of CIKM2005, pages 163–170, 2005.

[5] E. Amigo, J. Gonzalo, J. Artiles, and F. Verdejo. A comparison of extrinsic clustering evaluation metrics based on formal constraints. Information Retrieval, 12(4), 2009.

[6] Minoru Yoshida, Masaki Ikeda, Shingo Ono, Issei Sato, Hiroshi Nakagawa. Person Name Disambiguation by Bootstrapping. In Proceedings of SIGIR, 2010.