1 / 30

Robust Semantics, Information Extraction, and Information Retrieval

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’

Download Presentation

Robust Semantics, Information Extraction, and Information Retrieval

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Robust Semantics, Information Extraction, and Information Retrieval CS 4705

  2. 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

  3. 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 domain I 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

  4. Predicting User Input • Rely on knowledge of task and (sometimes) constraints on what the user can do • Can 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 to-city

  5. Priming User Input • Users will tend to use the vocabulary they hear from the system: lexicalentrainment (Clark & Brennan ’96) • Reference to objects: the scarey M&M man • Re-use of system prompt vocabulary/syntax: Please tell me where you would like to leave/depart from. Where would you like to leave/depart from? • Explicit training vs. implicit training • Training the user vs. retraining the system

  6. 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 them to have • If users go outside the grammar, things may break disastrously I want to leave from my house at 10 a.m. I want to talk to a person.

  7. Information Retrieval • How related to NLP? • Operates on language (speech or text) • Does it use linguistic information? • Stemming • Bag-of-words approach • Very simple analyses • 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

  8. But…what is a term? • Stop list • Stemming • Homonymy, polysemy, synonymy

  9. Vector Space Model • Simple versions represent documents and queries as feature vectors, one binary feature for each term in collection • Is a term t in this document or in this 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 matrix D = (wt1,wt2,…,wtn) Q = (wt1,wt2,…,wtn)

  10. How can we compare docs of different length • Normalize each term weight by the number of terms in the document to get : how important is each t in D? • How do we compare the vectors? • Compute dot product between query vector and each doc vector to see how similar they are • Cosine of angle betw vectors: 1 = identity; 0 = no common terms

  11. TfIdf • How can we improve the weights? • How important is each word in identifying the document it is in? • Term frequency (tfi,j): how often does i occur in Doc j? • Inverse document frequency (idf): # docs/ # docs term i occurs in • tf . idf weighting: weight of term i for doc j is product of frequency of i in j and its idf score in collection

  12. Evaluating IR Performance • Precision: #relevant docs returned/total #docs returned -- how often are you right when you say this document is relevant? • Recall: #relevant docs returned/#relevant docs in collection -- how many of the relevant documents do you find? • F-measure combines P and R • Are P and R equally important?

  13. Improving Queries • Relevance feedback: users rate retrieved docs • Query expansion: many techniques • add top N docs retrieved to query and resubmit expanded query • WordNet • Term clustering: cluster rows of terms in term-by-document matrix to produce synonyms and add to query

  14. 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 or eliminating less relevant items • Question-answering: find a span of text (within some window) containing the answer to a question

  15. Information Extraction • A ‘robust’ semantic method • Idea: ‘extract’ particular types of information from arbitrary text or transcribed speech • Examples: • Named entities: people, places, organizations, times, dates • <Organization> MIPS</Organization> Vice President <Person>John Hime</Person> • MUC evaluations • Domains: Medical texts, broadcast news (terrorist reports), …

  16. Appropriate where Semantic Grammars and Syntactic Parsers are not • Appropriate where information needs very specific and specifiable in advance • 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

  17. 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 • How to do better than everyone else?

  18. 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

  19. Domain-Specific IE from the Web (Patwardhan & Riloff ’06) • The Problem: • IE systems typically domain-specific – a new extraction procedure for every task • Supervised learning depends on hand annotation for training • Goals: • Acquire domain specific texts automatically on the Web • Identify domain-specific IE patterns automatically • Approach:

  20. Start with a set of seed IE patterns learned from a hand-labeled corpus • Use these to identify relevant documents on the web • Find new seed patterns in the retrieved documents

  21. MUC04 IE Task • Corpus: • 1700 news stories about terrorist events in Latin America • Answer keys about information that should be extracted • Problems: • All upper case • 50% of texts irrelevant • Stories may describe multiple events • Best results: • 50-70% precision and recall with hand-built components • 41-44% recall and 49-51% precision with automatically generated templates

  22. Procedure • Apply pre-defined syntactic patterns to a training corpus of documents for which relevant/irrelevant judgments known • Count how often partial lexicalizations of each (e.g. <subj> was killed) appear in relevant vs. irrelevant documents

  23. Rank patterns based on association with domain (frequency in domain documents vs. non-domain documents) • Manually review patterns and assign thematic roles to those deemed useful • From 40K+ patterns  291 • Now find similar web documents

  24. Domain Corpus Creation • Create IR queries by crossing names of 5 terrorist organizations (e.g. Al Qaeda, IRA) with 16 terrorist activities (e.g assinated, bombed, hijacked, wounded)  80 queries • Restricted to CNN, English documents • Eliminated TV transcripts • Yield from 2 runs: 6,182 documents • Cleaned corpus: 5,618 documents

  25. Learning Domain-Specific Patterns • Hypothesis: new extraction patterns co-occurring with seed patterns from training corpus will also be associated with terrorism • Generate all extraction patterns in CNN corpus (147,712) • Compute correlation of each with seed patterns based on frequency of co-occurrence in same sentence – keep those occurring more often that chance with some seed • Rank new patterns by their seed correlations

  26. Highly Ranked Patterns • Filter: Measure semantic affinity: how often does this pattern extract an entity of a particular category (e.g. victim, target)? • Compute semantic affinity for each extraction pattern wrt 6 categories: target, victim plus distractors: perpetrator, organization, weapon, other • E.g. Frequency of extracting target/frequency of extracting any of 6 categories weighted by log probability of target

  27. Remove patterns not strongly associated with desired classes: • Evaluate on MUC-4 • Baseline: • Recall 64%/Precision 43% on targets • Recall 50%/Precision 52% on victims

  28. Results for Web-Learned Patterns • Use 396 terrorism extraction patterns learned from MUC training set as seeds • Produce ranked list of new patterns from web using semantic affinity of 3.0 threshold • Chose top N (50-300) patterns to add to seed set • Performance:

  29. Combining IR and IE for QA • Information extraction: AQUA

  30. 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

More Related