CS 4705

Robust Semantics, Information Extraction, and Information

Retrieval

Problems with Syntax-Driven Semantics

• Syntactic structures often don’t fit semantic structures very well– Important semantic elements often distributed very

differently in trees for sentences that mean ‘the same’

I like soup. Soup is what I like.

– Parse trees contain many structural elements not clearly important to making semantic distinctions

– Syntax driven semantic representations are sometimes pretty verbose

V --> serves )},(),(),((,{ xeServedyeServerServingeIsayex

Alternatives?

• Semantic Grammars• Information Extraction Techniques• Information Retrieval --> Information Extraction

Semantic Grammars

• Alternative to modifying syntactic grammars to deal with semantics too

• Define grammars specifically in terms of the semantic information we want to extract– Domain specific: Rules correspond directly to entities

and activities in the domainI want to go from Boston to Baltimore on Thursday,

September 24th

– Greeting --> {Hello|Hi|Um…}– TripRequest Need-spec travel-verb from City to

City on Date

Predicting User Input

• Semantic grammars rely upon knowledge of the task and (sometimes) constraints on what the user can do, when– Allows them to handle very sophisticated phenomena

I want to go to Boston on Thursday.

I want to leave from there on Friday for Baltimore.

TripRequest Need-spec travel-verb from City on Date for City

Dialogue postulate maps filler for ‘from-city’ to pre-specified from-city

Drawbacks of Semantic Grammars

• Lack of generality– A new one for each application

– Large cost in development time

• Can be very large, depending on how much coverage you want

• If users go outside the grammar, things may break disastrouslyI want to leave from my house.

I want to talk to someone human.

Information Extraction

• Another ‘robust’ alternative• Idea is to ‘extract’ particular types of information

from arbitrary text or transcribed speech• Examples:

– Named entities: people, places, organizations, times, dates

– Telephone numbers

<Organization> MIPS</Organization> Vice President <Person>John Hime</Person>

• Domains: Medical texts, broadcast news, voicemail,...

Appropriate where Semantic Grammars and Syntactic Parsers are Not

• Appropriate where information needs very specific– Question answering systems, gisting of news or mail…– Job ads, financial information, terrorist attacks

• Input too complex and far-ranging to build semantic grammars

• But full-blown syntactic parsers are impractical– Too much ambiguity for arbitrary text– 50 parses or none at all– Too slow for real-time applications

Information Extraction Techniques

• Often use a set of simple templates or frames with slots to be filled in from input text– Ignore everything else– My number is 212-555-1212.– The inventor of the wiggleswort was Capt. John T.

Hart.– The king died in March of 1932.

• Context (neighboring words, capitalization, punctuation) provides cues to help fill in the appropriate slots

The IE Process

• Given a corpus and a target set of items to be extracted:– Clean up the corpus

– Tokenize it

– Do some hand labeling of target items

– Extract some simple features

• POS tags

• Phrase Chunks …

– Do some machine learning to associate features with target items or derive this associate by intuition

– Use e.g. FSTs, simple or cascaded to iteratively annotate the input, eventually identifying the slot fillers

Some examples

• Semantic grammars• Information extraction

Information Retrieval

• How related to NLP?– Operates on language (speech or text)– Does it use linguistic information?

• Stemming• Bag-of-words approach

– Does it make use of document formatting?• Headlines, punctuation, captions

• Collection: a set of documents• Term: a word or phrase• Query: a set of terms

But…what is a term?

• Stop list• Stemming• Homonymy, polysemy, synonymy

Vector Space Model

• Simple versions represent documents and queries as feature vectors, one binary feature for each term in collection

• Is t in this document or query or not?D = (t1,t2,…,tn)Q = (t1,t2,…,tn)• Similarity metric:how many terms does a query

share with each candidate document?

• Weighted terms: term-by-document matrixD = (wt1,wt2,…,wtn)Q = (wt1,wt2,…,wtn)

• How do we compare the vectors?– Normalize each term weight by the number of terms in

the document: how important is each t in D?

– Compute dot product between vectors to see how similar they are

– Cosine of angle: 1 = identity; 0 = no common terms

• How do we get the weights?– Term frequency (tf): how often does t occur in D?

– Inverse document frequency (idf): # docs/ # docs term t occurs in

– tf . idf weighting: weight of term i for doc j is product of frequency of i in j with log of idf in collection

i

i nNidf log

iji idftfw ji ,,

Evaluating IR Performance

• Precision: #rel docs returned/total #docs returned -- how often are you right when you say this document is relevant?

• Recall: #rel docs returned/#rel docs in collection -- how many of the relevant documents do you find?

• F-measure combines P and R RPPR

)(2

Improving Queries

• Relevance feedback: users rate retrieved docs• Query expansion: many techniques

– e.g. add top N docs retrieved to query

• Term clustering: cluster rows of terms to produce synonyms and add to query

IR Tasks

• Ad hoc retrieval: ‘normal’ IR• Routing/categorization: assign new doc to one of

predefined set of categories• Clustering: divide a collection into N clusters• Segmentation: segment text into coherent chunks• Summarization: compress a text by extracting

summary items• Question-answering: find a stretch of text

containing the answer to a question

Summary

• Many approaches to ‘robust’ semantic analysis– Semantic grammars targeting particular domains

Utterance --> Yes/No Reply

Yes/No Reply --> Yes-Reply | No-Reply

Yes-Reply --> {yes,yeah, right, ok,”you bet”,…}

– Information extraction techniques targeting specific tasks

• Extracting information about terrorist events from news

– Information retrieval techniques --> more like NLP