Robust Semantics, Information
Extraction, and Information
Retrieval
CS 4705
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
{xe, y( Isa(e,Serving )  Server (e, y)  Served (e, x)}
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 prespecified 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 idfi  log N 
 ni 


– tf . idf weighting: weight of term i for doc j is product
of frequency of i in j with log of idf in collection
wi, j  tfi, j idfi
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 2(PR)
P 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