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

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 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

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 disastrously I 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 matrix D = (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

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

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