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					Semantics
Where are we in the “Big Picture”



      Speech              ASR


      Text          Morph Analysis


                        Parsing          Syntactic Parse



                  Semantic Interpreter
                                            Semantic
                                            Representation

                   Inference Engine



                        WORLD
                        of FACTS
Semantic Representation

  Syntactic representation,
  • phrases and tree structures
  • dependency information between words
  Semantic representation
  • What’s the purpose of this representation?
      –   Interface between syntactic information and the inference engine
  Requirements on the semantic representation
  • Supports inference
      –   Every CEO is wealthy and Gates is a CEO è Gates is wealthy
  •   Normalizes syntactic variations
      –   Delta serves NYC == NYC is served by Delta
  •   Has the capacity of representing the distinctions in language phenomena
      –   John believes Delta serves NYC ≠ Delta serves NYC
  •   Unambiguous representation
      –   John wants to eat someplace close to the university
Mechanisms for Expressing Meaning

 Linguistic means for expressing meaning
 • Words: lexical semantics and word senses
     –   Delta will serve NYC
     –   This flight will serve peanuts
     –   John will serve as CEO
 •   Syntactic information: predicate-argument structure
     –   John wants to eat a cake
     –   John wants Mary to eat a cake
     –   John wants a cake
 •   Prosodic information in Speech
     –   Legumes are a good source of vitamins



 •   Gesture information in multimodal communication
First-order Predicate Calculus: A refresher

 A formal system used to derive new propositions and verify their truth given a world.
 Syntax of FOPC
 • Formulae: quantifiers and connectives on predicates
 • Predicates: n-ary predications of facts and relations
 • Terms: constants, variables and functions
 World: Truth assignments to formulae
 Inference:
 • Modus ponens
   –   Every CEO is wealthy : ∀x CEO(x) è wealthy(x)
   –   Gates is a CEO : CEO(Gates)
   –   Derives: wealthy(Gates)
 Given a world, determining the truth value of a formula is a search process –
 backward chaining and forward chaining
 • Much like the top-down and bottom-up parsing algorithms.
Logic for Language

Representations for different aspects of language.
• Entities
    –   Delta, Gates, AT&T
•   Categories
    –   restaurants, airlines, students
•   Events
    –   I ate lunch. I ate at my desk à I ate lunch at my desk
•   Time (utterance time, reference time, event time)
    –   I ate lunch when the flight arrived
    –   I had eaten lunch when the flight arrived
•   Aspect
    –   Stative, activity, achievement and accomplishment
•   Quantification
    –   Every person loves some movie
•   Predication
    –   John is a teacher
•   Modal operators
    –   John believes Mary went to the movies
    Linking Syntax and Semantics

How to compute semantic representations from syntactic trees?
We could have one function for each syntactic tree that maps it to its semantic
representation.
• Too many such functions
• Not all aspects of the tree might be needed for its semantics
Meaning derives from
• The people and activities represented (predicates and arguments, or, nouns and verbs)
• The way they are ordered and related: syntax of the representation, which may also reflect
  the syntax of the sentence
Compositionality Assumption: The meaning of the whole sentence is composed of the
meaning of its parts.
• George cooks. Dan eats. Dan is sick.
• Cook(George) Eat(Dan) Sick(Dan)
• If George cooks and Dan eats, Dan will get sick.
(Cook(George) ^ eat(Dan)) à Sick(Dan)
The trick is to decide on what the size of the part should be.
•   Rule-by-rule hypothesis
Linking Syntax and Semantics – contd.


  Compositionality:
  • Augment the lexicon and the grammar (as we did with feature
    structures)
  • Devise a mapping between rules of the grammar and rules of
    semantic representation
  • For CFGs, this amounts to a Rule-to-Rule Hypothesis
  Each grammar rule is embellished with instructions on how to map the
  components of the rule to a semantic representation.
  S à NP VP {VP.sem(NP.sem)}
  Each semantic function is defined in terms of the semantic
  representation of choice.
  Syntax-Driven Semantics

             S : fly(birds)




NP : birds                    VP : fly



                              V : fly
N : birds
                                         There are still a few free parameters:
                                         a. What should the semantic representation of
  birds                         fly         each component be?
                                         b. How should we combine the component
                                            representations?
                                         Depends on what the final representation we want.
  A Simple Example

            McDonald’s serves burgers.
Associating constants with constituents
• ProperNoun à McDonald’s {McDonald’s}
• PlNoun à burgers {burgers}
Defining functions to produce these from input
• NP à ProperNoun {ProperNoun.sem}
• NP à PlNoun {PlNoun.sem}
• Assumption: meaning representations of children are passed up to
  parents for non-branching constituents
Verbs are where the action is
•   V à serves {∃(e,x,y) Isa(e,Serving) ^ Server(e,x) ^ Served(e,y)}
    where e = event, x = agent, y = patient
•   Will every verb have its own distinct representation?
    –   McDonald’s hires students.
    –   McDonald’s gave customers a bonus.
    –   Predicate(Agent, Patient, Beneficiary)
Once we have the semantics for each constituent, how do we combine
them?
• VP à V NP {V.sem(NP.sem)}
• Goal for VP semantics: E(e,x) Isa(e,Serving) ^ Server(e,x) ^
  Served(e,burgers)
• VP.sem must tell us
  – Which variables to be replaced by which arguments
  – How this replacement is done
  Lambda Notation


Extension to First Order Predicate Calculus lx P(x)
•       l + variable(s) + FOPC expression in those variables
Lambda binding
•       Apply lambda-expression to logical terms to bind lambda-
        expression’s parameters to terms (lambda reduction)
•       Simple process: substitute terms for variables in lambda expression
       lxP(x) (car) à P(car)
  Lambda Abstraction and Application
Abstraction: Make variable in the body available for binding.
• to external arguments provided by semantics of other constituents (e.g. NPs)
Application: Substitute the bound variable with the value
Semantic attachment for
• V à serves {V.sem(NP.sem)}
{∃(e,x,y) Isa(e,Serving) ^ Server(e,y) ^ Served(e,x)} converts to the lambda
  expression:
{lx ∃ (e,y) Isa(e,Serving) ^ Server(e,y) ^ Served(e,x)}
• Now ‘x’ is available to be bound when V.sem is applied to NP.sem of direct object
  (V.sem(NP.sem))
• l application binds x to value of NP.sem (burgers)
• Value of VP.sem becomes:
{∃(e,y) Isa(e,Serving) ^ Server(e,y) ^ Served(e,burgers)}
Lambda Abstraction and Application – contd.



 Similarly, we need a semantic attachment for Sà NP VP {VP.sem(NP.sem)} to add the subject
 NP to our semantic representation of McDonald’s serves burgers
 • Back to V.sem for serves
 • We need another l-abstraction in the value of VP.sem
 • Change semantic representation of V to include another argument to be bound
   later
 V à serves {lx ly ∃(e) Isa(e,Serving) ^ Server(e,y) ^ Served(e,x)}
 Value of VP.sem becomes:
 {ly ∃(e) Isa(e,Serving) ^ Server(e,y) ^ Served(e,burgers)}
 Value of S.sem becomes:
 {∃(e) Isa(e,Serving) ^ Server(e,McDonald’s) ^ Served(e,burgers)}
    Several Complications

For example, terms can be complex
A restaurant serves burgers.
• ‘a restaurant’: ∃x Isa(x,restaurant)
• E e Isa(e,Serving) ^ Server(e,< ∃x Isa(x,restaurant)>) ^
  Served(e,burgers)
• Allows quantified expressions to appear where terms can by
  providing rules to turn them into well-formed FOPC expressions
Issues of quantifier scope
Every restaurant serves burgers.
Every restaurant serves every burger.
Semantic representations for other constituents?
• Adjective phrases:
  –   Happy people, cheap food, purple socks
  –   intersective semantics
Nom à Adj Nom {lx Nom.sem(x) ^ Isa(x,Adj.sem)}
Adj à cheap {Cheap}
lx Isa(x, Food) ^ Isa(x,Cheap) …works ok …
But….fake gun? Local restaurant? Former friend? Would-be singer?
Ex Isa(x, Gun) ^ Isa(x,Fake)
    Doing Compositional Semantics

Incorporating compositional semantics into CFG requires:
• Right representation for each constituent based on the parts of that
  constituent (e.g. Adj)
• Right representation for a category of constituents based on other
  grammar rules, making use of that constituent (e.g. V.sem)
This gives us a set of function-like semantic attachments incorporated
into our CFG
•   E.g. Nom à Adj Nom {lx Nom.sem(x) ^ Isa(x,Adj.sem)}
A number of formalisms that extend CFGs to allow larger compositionality
domains.
Computing the Semantic Representation


 Two approaches:
 • Compute the semantic representation of each constituent as the parser
   progresses through the rules.
     –   Semantic representations could be used to rule out parses
     –   Wasted time in constructing semantics for unused constituents.
 •   Let the parser complete the syntactic parse and then recover the
     semantic representation.
     –   in a bottom-up traversal.
 Issues of ambiguous syntactic representation
 • Packing ambiguity
 • Underspecified semantics.
  Non-Compositional Language

Non-compositional modifiers: fake, former, local
Metaphor:
• You’re the cream in my coffee. She’s the cream in George’s coffee.
• The break-in was just the tip of the iceberg.
• This was only the tip of Shirley’s iceberg.
Idioms:
• The old man finally kicked the bucket.
• The old man finally kicked the proverbial bucket.
Deferred reference: The ham sandwich wants his check.
Solutions? Mix lexical items with special grammar rules? Or???
Lexical Semantics




      Lexical Semantics
Thinking about Words Again


Lexeme: an entry in the lexicon that includes
• an orthographic representation
• a phonological form
• a symbolic meaning representation or sense
Some typical dictionary entries:
• Red (‘red) n: the color of blood or a ruby
• Blood (‘bluhd) n: the red liquid that circulates in the heart,
  arteries and veins of animals
• Right (‘rIt) adj: located nearer the right hand esp. being on the
  right when facing the same direction as the observer
• Left (‘left) adj: located nearer to this side of the body than the
  right
Can we get semantics directly from online dictionary entries?
• Some are circular
• All are defined in terms of other lexemes
• You have to know something to learn something
What can we learn from dictionaries?
• Relations between words:
    –   Oppositions, similarities, hierarchies
    Homonomy
Homonyms: Words with same form – orthography and pronunciation --
but different, unrelated meanings, or senses (multiple lexemes)
• A bank holds investments in a custodial account in the client’s name.
• As agriculture is burgeoning on the east bank, the river will shrink even
  more
Word sense disambiguation: what clues?
Similar phenomena
• homophones - read and red
    –   same pronunciation/different orthography
•   homographs - bass and bass
    –   same orthography/different pronunciation
Ambiguity: Which applications will these
cause problems for?
A bass, the bank, /red/
General semantic interpretation
Machine translation
Spelling correction
Speech recognition
Text to speech
Information retrieval
   Polysemy

Word with multiple but related meanings (same lexeme)
• They rarely serve red meat.
• He served as U.S. ambassador.
• He might have served his time in prison.
What’s the difference between polysemy and homonymy?
Homonymy:
• Distinct, unrelated meanings
• Different etymology? Coincidental similarity?
Polysemy:
• Distinct but related meanings
• idea bank, sperm bank, blood bank, bank bank
• How different?
 – Different subcategorization frames?
 – Domain specificity?
 – Can the two candidate senses be conjoined?
 ?He served his time and as ambassador to Norway.
For either, practical task:
• What are its senses? (related or not)
• How are they related? (polysemy ‘easier’ here)
• How can we distinguish them?
Tropes, or Figures of Speech
Metaphor: one entity is given the attributes of another
(tenor/vehicle/ground)
• Life is a bowl of cherries. Don’t take it serious….
• We are the eyelids of defeated caves. ??
Metonymy: one entity used to stand for another (replacive)
• GM killed the Fiero.
• The ham sandwich wants his check.
Both extend existing sense to new meaning
• Metaphor: completely different concept
• Metonymy: related concepts
Synonymy
Substitutability: different lexemes, same meaning
• How big is that plane?
• How large is that plane?
• How big are you? Big brother is watching.
What influences substitutability?
• Polysemy (large vs. old sense)
• register: He’s really cheap/?parsimonious.
• collocational constraints:
 roast beef, ?baked beef
 economy fare ?economy price
  Finding Synonyms and Collocations Automatically from
  a Corpus


Synonyms: Identify words appearing frequently in similar contexts
Blast victims were helped by civic-minded passersby.
Few passersby came to the aid of this crime victim.
Collocations: Identify synonyms that don’t appear in some specific
similar contexts
Flu victims, flu suffers,…
Crime victims, ?crime sufferers, …
Hyponomy


General: hypernym (super…ordinate)
• dog is a hypernym of poodle
Specific: hyponym (under..neath)
• poodle is a hyponym of dog
Test: That is a poodle implies that is a dog
Ontology: set of domain objects
Taxonomy? Specification of relations between those objects
Object hierarchy? Structured hierarchy that supports feature
inheritance (e.g. poodle inherits some properties of dog)
Semantic Networks
Used to represent lexical relationships
• e.g. WordNet (George Miller et al)
• Most widely used hierarchically organized lexical database for
  English
• Synset: set of synonyms, a dictionary-style definition (or
  gloss), and some examples of uses --> a concept
• Databases for nouns, verbs, and modifiers
Applications can traverse network to find synonyms, antonyms,
hierarchies,...
• Available for download or online use
• http://www.cogsci.princeton.edu/~wn
    Using WN, e.g. in Question-Answering


Pasca & Harabagiu ’01 results on TREC corpus
• Parses questions to determine question type, key words (Who invented the
  light bulb?)
• Person question; invent, light, bulb
• The modern world is an electrified world. It might be argued that any of a number
  of electrical appliances deserves a place on a list of the millennium's most
  significant inventions. The light bulb, in particular, profoundly changed human
  existence by illuminating the night and making it hospitable to a wide range of
  human activity. The electric light, one of the everyday conveniences that most
  affects our lives, was invented in 1879 simultaneously by Thomas Alva Edison in
  the United States and Sir Joseph Wilson Swan in England.
Finding named entities is not enough
Compare expected answer ‘type’ to potential answers
• For questions of type person, expect answer is person
• Identify potential person names in passages retrieved by IR
• Check in WN to find which of these are hyponyms of person
Or, Consider reformulations of question: Who invented the light bulb
• For key words in query, look for WN synonyms
• E.g. Who fabricated the light bulb?
• Use this query for initial IR
Results: improve system accuracy by 147% (on some question types)
 Thematic Roles

∃ w,x,y,z {Giving(x) ^ Giver(w,x) ^ Givee(z, x) ^ Given(y,x)}
A set of roles for each event:
• Agent: volitional causer -- John hit Bill.
• Experiencer: experiencer of event – Bill got a headache.
• Force: non-volitional causer – The concrete block struck Bill on the
  head.
• Theme/patient: most affected participant – John hit Bill.
• Result: end product – Bill got a headache.
• Content: proposition of propositional event – Bill thought he should
  take up martial arts.
• Instrument: instrument used -- John hit Bill with a bat
• Beneficiary: qui bono – John hit Bill to avenge his friend
• Source: origin of object of transfer event – Bill fled from New
  York to Timbuktu
• Goal: destination of object -- Bill led from New York to Timbuktu
But there are a lot of verbs, with a lot of frames…
Framenet encoded frames for many verb categories
Thematic Roles and Selectional
Restrictions
Selectional restrictions: semantic constraint that a word
(lexeme) imposes on the concepts that go with it
George hit Bill with
            ….John/a gun/gusto.
Jim killed his philodendron/a fly/Bill.
?His philodendron killed Jim.
The flu/Misery killed Jim.
Thematic Roles/Selectional Restrictions

In practical use:
•       Given e.g. a verb and a corpus (plus FrameNet)
•       What conceptual roles are likely to accompany it?
•       What lexemes are likely to fill those roles?
Assassinate
Give
Imagine
Fall
Serve
Schank's Conceptual Dependency

Eleven predicate primitives represent all predicates
Objects decomposed into primitive categories and modifiers
But few predicates result in very complex representations of
simple things
∃x,y Atrans(x) ^ Actor(x,John) ^ Object(x,Book) ^ To(x,Mary)
  ^ Ptrans(y) ^ Actor(y,John) ^ Object(y,Book) ^ To(y,Mary)
John caused Mary to die vs. John killed Mary
Robust Semantics, Information Extraction, and
Information Retrieval
Problems with Syntax-Driven Semantics

Compositionality:
• Expects correspondence between syntactic and semantic structures.
    –   Mismatch between syntactic structures and semantic structures: certainly not rule-to-rule.
        (inadequacy of CFGs)
                  I like soup. Soup is what I like.
• Constituent trees contain many structural elements not clearly important to
  making semantic distinctions
    –   Resort to dependency trees.
•   Too abstract: Syntax driven semantic representations are sometimes very
    abstract.
    –   Nominal à Adjective Nominal λx Nominal.sem(x) AM(x,Adj.sem)
    –   Cheap restaurant, Italian restaurant, local restaurant
Robust Semantic processing: Trade-off
• Portability
• Expressivity
  Semantic Grammars

Before:
• CFG with syntactic categories with
• semantic representation composition overlaid.
Now:
• CFG with domain-specific semantic categories
• Domain specific: Rules correspond directly to entities and activities in the
  domain
I want to go from Boston to Baltimore on Thursday, September 24 th
• Greeting à {Hello|Hi|Um…}
• TripRequest à Need-spec travel-verb from City to City on Date
Note: Semantic grammars are still CFGs.
    Pros and Cons of Semantic Grammars

•   Semantic grammars encode task knowledge and constrains the range of
    possible user input.
     I want to go to Boston on Thursday.
     I want to leave from there on Friday for Baltimore.
     TripRequest à Need-spec travel-verb from City on Date for City
•   The semantic representation is a slot-filler frame-like representation –
    crafted for that domain.
•   Portability: Lack of generality
     – A new one for each application
     – Large cost in development time
•   Robustness: If users go outside the grammar, things may break
    disastrously
     I want to go from ah to Boston from Newark
•   Expressivity:
     – I want to go to Boston from Newark or New York
Information Extraction

Another ‘robust’ alternative
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), company
mergers, customer care voicemail,...
 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
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.
Generative Model:
• POS-style HMM model (with novel encoding)
• The/O king/O died/O in/O March/I of/I 1932/I in/O France/O
• T* = argmaxT P(W|T) * P(T)
Context
• neighboring words, capitalization, punctuation can be used as
  well.
    Discriminative Disambiguation Techniques

• Large set of features makes MLE estimation of the parameters unreliable.
P(T|W) = π P(ti | W, POS, Ortho)
         = P(ti | wi-k…wi+k, posi-k…posi+k, orthoi)
• Direct approach:
    – F (ti ,wi-k…wi+k, posi-k…posi+k, orthoi) = F(y,X)
    – F(y,X) =




•   Maximum Entropy Markov Models, Conditional Random Fields
ScanMail Transcription
gender F
age A
caller_name NA
native_speaker N
speech_pathology N
sample_rate 8000
label 0 804672 " [ Greeting: hi R__ ] [ CallerID: it's me ] give me a call [ um ] right
away cos there's [ .hn ] I guess there's some [ .hn ] change [ Date: tomorrow ] with
the nursery school and they [ um ] [ .hn ] anyway they had this idea [ cos ] since I
think J__'s the only one staying [ Date: tomorrow ] for play club so they wanted to
they suggested that [ .hn ] well J2__actually offered to take J__home with her
and then would she would meet you back at the synagogue at [ Time: five thirty ] to
pick her up [ .hn ] [ uh ] so I don't know how you feel about that otherwise Miriam
and one other teacher would stay and take care of her till [ Date: five thirty
tomorrow ] but if you [ .hn ] I wanted to know how you feel before I tell her one
way or the other so call me [ .hn ] right away cos I have to get back to her in about
an hour so [ .hn ] okay [ Closing: bye [ .nhn ] [ .onhk ] ]"
duration "50.3 seconds"
  SCANMail
Access Devices
PC
Pocket PC
Dataphone
Voice Phone
Flash E-mail
Word Sense Disambiguation




      Word Sense Disambiguation
Disambiguation via Selectional Restrictions


 A step toward semantic parsing
 • Different verbs select for different thematic roles
     wash the dishes (takes washable-thing as patient)
     serve delicious dishes (takes food-type as patient)
 Method: rule-to-rule syntactico-semantic analysis
 • Semantic attachment rules are applied as sentences are
   syntactically parsed
     VP --> V NP
     Và serve <theme> {theme:food-type}
 •   Selectional restriction violation: no parse
Requires:
• Write selectional restrictions for each sense of each predicate –
  or use FrameNet
    –   serve alone has 15 verb senses
•   Hierarchical type information about each argument (a la
    WordNet)
    –   How many hypernyms does dish have?
    –   How many lexemes are hyponyms of dish?
But also:
• Sometimes selectional restrictions don’t restrict enough (Which
  dishes do you like?)
• Sometimes they restrict too much (Eat dirt, worm! I’ll eat my
  hat!)
Can we take a more statistical approach?


 How likely is dish/crockery to be the object of serve? dish/food?
 A simple approach (baseline): predict the most likely sense
 • Why might this work?
 • When will it fail?
 A better approach: learn from a tagged corpus
 • What needs to be tagged?
 An even better approach: Resnik’s selectional association (1997, 1998)
 •   Estimate conditional probabilities of word senses from a corpus
     tagged only with verbs and their arguments (e.g. ragout is an object
     of served -- Jane served/V ragout/Obj
How do we get the word sense probabilities?
• For each verb object (e.g. ragout)
    – Look up hypernym classes in WordNet
    – Distribute “credit” for this object sense occurring with this verb among
      all the classes to which the object belongs
    Brian served/V the dish/Obj
    Jane served/V food/Obj
    – If ragout has N hypernym classes in WordNet, add 1/N to each class
      count (including food) as object of serve
    – If tureen has M hypernym classes in WordNet, add 1/M to each class
      count (including dish) as object of serve
•   Pr(Class|v) is the count(c,v)/count(v)
•   How can this work?
    – Ambiguous words have many superordinate classes
    John served food/the dish/tuna/curry
    – There is a common sense among these which gets “credit” in each
      instance, eventually dominating the likelihood score
To determine most likely sense of ‘bass’ in Bill served bass
• Having previously assigned ‘credit’ for the occurrence of all hypernyms of
  things like fish and things like musical instruments to all their hypernym
  classes (e.g. ‘fish’ and ‘musical instruments’)
• Find the hypernym classes of bass (including fish and musical
  instruments)
• Choose the class C with the highest probability, given that the verb is
  serve
Results:
• Baselines:
    –   random choice of word sense is 26.8%
    –   choose most frequent sense (NB: requires sense-labeled training corpus) is
        58.2%
•   Resnik’s: 44% correct with only pred/arg relations labeled
Machine Learning Approaches
Learn a classifier to assign one of possible word senses for each
word
• Acquire knowledge from labeled or unlabeled corpus
• Human intervention only in labeling corpus and selecting set
  of features to use in training
Input: feature vectors
• Target (dependent variable)
• Context (set of independent variables)
Output: classification rules for unseen text
Supervised Learning


Training and test sets with words labeled as to correct sense
(It was the biggest [fish: bass] I’ve seen.)
• Obtain values of independent variables automatically (POS,
  co-occurrence information, …)
• Run classifier on training data
• Test on test data
• Result: Classifier for use on unlabeled data
Input Features for WSD
POS tags of target and neighbors
Surrounding context words (stemmed or not)
Punctuation, capitalization and formatting
Partial parsing to identify thematic/grammatical roles and relations
Collocational information:
• How likely are target and left/right neighbor to co-occur
Co-occurrence of neighboring words
• Intuition: How often does sea or words with bass
•   How do we proceed?
    –   Look at a window around the word to be disambiguated, in training
        data
    –   Which features accurately predict the correct tag?
    –   Can you think of other features might be useful in general for WSD?
Input to learner, e.g.
Is the bass fresh today?
[w-2, w-2/pos, w-1,w-/pos,w+1,w+1/pos,w+2,w+2/pos…
[is,V,the,DET,fresh,RB,today,N...
Types of Classifiers


 Naïve Bayes
 • ŝ =         p(s|V), or
 • Where s is one of the senses possible and V the input vector
   of features
 • Assume features independent, so probability of V is the
   product of probabilities of each feature, given s, so

 •   and p(V) same for any ŝ
 •   Then
Rule Induction Learners
(e.g. Ripper)

 Given a feature vector of values for independent variables
 associated with observations of values for the training set
 (e.g. [fishing,NP,3,…] + bass2)
 Produce a set of rules that perform best on the training data,
 e.g.
 • bass2 if w-1==‘fishing’ & pos==NP
 • …
                     Decision Lists
Like case statements applying tests to input in turn
fish within window     --> bass1
striped bass           --> bass1
guitar within window --> bass2
bass player            --> bass1
…
• Ordering based on individual accuracy on entire training set
   based on log-likelihood ratio
Bootstrapping I
• Start with a few labeled instances of target item as seeds
  to train initial classifier, C
• Use high confidence classifications of C on unlabeled data
  as training data
• Iterate
Bootstrapping II
• Start with sentences containing words strongly associated
  with each sense (e.g. sea and music for bass), either
  intuitively or from corpus or from dictionary entries
• One Sense per Discourse hypothesis
Unsupervised Learning

Cluster feature vectors to ‘discover’ word senses using some similarity
metric (e.g. cosine distance)
• Represent each cluster as average of feature vectors it contains
• Label clusters by hand with known senses
• Classify unseen instances by proximity to these known and labeled
  clusters
Evaluation problem
• What are the ‘right’ senses?
• Cluster impurity
• How do you know how many clusters to create?
• Some clusters may not map to ‘known’ senses
Dictionary Approaches

Problem of scale for all ML approaches
• Build a classifier for each sense ambiguity
Machine readable dictionaries (Lesk ‘86)
• Retrieve all definitions of content words occurring in context of
  target (e.g. the happy seafarer ate the bass)
• Compare for overlap with sense definitions of target entry (bass2: a
  type of fish that lives in the sea)
• Choose sense with most overlap
Limits: Entries are short --> expand entries to ‘related’ words

				
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posted:3/21/2014
language:English
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