Lexical Semantics
COMP-550
    Oct 17, 2017
Outline
Semantics
Lexical semantics
Lexical semantic relations
WordNet
Word Sense Disambiguation
  • Lesk algorithm
  • Yarowsky’s algorithm
                             2
Semantics
What is ”Semantics”?
The study of meaning in language
  “When I use a word”, Humpty Dumpty said in rather a scornful tone,
  “it means just what I choose it to mean – neither more nor less.”
                                     Lewis Carroll, Alice in Wonderland
What does meaning mean?
  • Relationship of linguistic expression to the real world
  • Relationship of linguistic expressions to each other
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This Lecture
We’ll start by focusing on the meaning of words—
lexical semantics.
Later on:
   • meaning of phrases and sentences
   • how to construct that from meanings of words
                                                    4
From Language to the World
What does telephone mean?
   • Picks out all of the objects in the world that are
     telephones (its referents)
Its extensional definition
                                                not telephones
                   telephones
                                                                 5
Relationship of Linguistic Expressions
How would you define telephone? e.g, to a three-year-
           old, or to a friendly Martian.
                                                        6
Dictionary Definition
http://dictionary.reference.com/browse/telephone
Its intensional definition
   • The necessary and sufficient conditions to be a telephone
This presupposes you know what “apparatus”, “sound”,
“speech”, etc. mean.
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Lexical Semantics Jargon
Lexeme: Pairing of a particular form (orthographic or
phonological) with its meaning.
   For example, the lexeme BANK (noun) consists of bank and
   banks, but not banker. BANKER is a lexeme of its own!
Lexicon: Finite list of lexemes
Lemma: The grammatical form that is used to represent
a lexeme.
   The lemma for sing, sang, sung is sing. The specific form (e.g.
   sang) is called wordform.
Lemmatization: The process of mapping a wordform to
a lemma.
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Sense and Reference (Frege, 1892)
Frege was one of the first to distinguish between the
sense of a term, and its reference.
Same referent, different senses:
   Venus
   the morning star
   the evening star
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Word Senses
The meaning of a lemma can vary enormously given
the context:
   • A bank can hold investments in a custodial account in the
     client’s name.
   • As agriculture burgeons on the east bank, the river shrink
     even more.
A word sense (or simply sense) is a discrete
representation of one aspect of the meaning of a word.
Next: Relations between different senses (and generally
words)
Later: How to disambiguate between varying senses?
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Lexical Semantic Relations
How specifically do terms relate to each other? Here
are some ways:
   Hypernymy/hyponymy
   Synonymy
   Antonymy
   Homonymy
   Polysemy
   Metonymy
   Synecdoche
   Holonymy/meronymy
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Hypernymy/Hyponymy
ISA relationship
Hyponym            Hypernym
monkey             mammal
Montreal           city
red wine           beverage
                              12
Synonymy and Antonymy
Synonymy
  (Roughly) same meaning
  offspring descendent spawn
  happy joyful merry
Antonymy
  (Roughly) opposite meaning
  synonym antonym
  happy sad
  descendant ancestor
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Homonymy
Same form, different (and unrelated) meaning
Homophone – same sound
   • e.g., son vs. sun
Homograph – same written form
   • e.g., lead (noun) vs. lead (verb)
                                               14
Polysemy
Multiple related meanings
   S: (n) newspaper, paper (a daily or weekly publication on
   folded sheets; contains news and articles and
   advertisements) "he read his newspaper at breakfast"
   S: (n) newspaper, paper, newspaper publisher (a business
   firm that publishes newspapers) "Murdoch owns many
   newspapers"
   S: (n) newspaper, paper (the physical object that is the
   product of a newspaper publisher) "when it began to rain he
   covered his head with a newspaper"
   S: (n) newspaper, newsprint (cheap paper made from wood
   pulp and used for printing newspapers) "they used bales of
   newspaper every day"
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Homonymy vs Polysemy
Homonymy: unrelated             Polysemy: related meaning
  S: (n) position, place (the particular portion of space occupied by
  something) "he put the lamp back in its place"
  S: (n) military position, position (a point occupied by troops for
  tactical reasons)
  S: (n) position, view, perspective (a way of regarding situations or
  topics etc.)"consider what follows from the positivist view"
  S: (n) position, posture, attitude (the arrangement of the body and
  its limbs) "he assumed an attitude of surrender"
  S: (n) status, position (the relative position or standing of things or
  especially persons in a society) "he had the status of a minor"; "the
  novel attained the status of a classic"; "atheists do not enjoy a
  favorable position in American life"
  S: (n) position, post, berth, office, spot, billet, place, situation (a
  job in an organization) "he occupied a post in the treasury"
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Metonymy
Substitution of one entity for another related one
   We ordered many delicious dishes at the restaurant.
   I worked for the local paper for five years.
   Quebec City is cutting our budget again.
   The loonie is at a 11-year low.
Synecdoche – a specific kind of metonymy involving
whole-part relations
   All hands on deck!
   Don’t be a <censored body part>
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Holonymy/meronymy
Some kind of whole/part relationship
Subtypes                Holonym        Meronym
   groups and members    class         student
   whole and part        car           windshield
   whole and substance   chair         wood
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Quiz
Classify the following examples in terms of what lexical
semantic relation they exhibit
   cold             freezing
   they’re          their
   hair             head
   enemy            friend
   cut (hair)       cut (bread)
   George Clooney   actor
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WordNet (Miller et et., 1990)
WordNet is a lexical resource organized by synsets
   • Nodes: synsets
   • Edges: lexical semantic relation between two synsets
Separate hierarchy for different parts of speech
   • Nouns, verbs, adjectives, adverbs
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A Synset Entry
S: (n) hand, manus, mitt, paw (the (prehensile) extremity of the superior
limb) "he had the hands of a surgeon"; "he extended his mitt"
    direct hyponym / full hyponym
         S: (n) fist, clenched fist (a hand with the fingers clenched in the palm (as for hitting))
         S: (n) hooks, meat hooks, maulers (large strong hand (as of a fighter))"wait till I get my
         hooks on him"
         S: (n) right, right hand (the hand that is on the right side of the body) "he writes with his
         right hand but pitches with his left"; "hit him with quick rights to the body"
         S: (n) left, left hand (the hand that is on the left side of the body) "jab with your left"
    part meronym
    direct hypernym / inherited hypernym / sister term
    part holonym
         S: (n) arm (a human limb; technically the part of the superior limb between the shoulder
         and the elbow but commonly used to refer to the whole superior limb)
         S: (n) homo, man, human being, human (any living or extinct member of the family
         Hominidae characterized by superior intelligence, articulate speech, and erect carriage)
    derivationally related form
http://wordnetweb.princeton.edu/perl/webwn?o2=&o0=1&o8=1&o1=1&o7=
&o5=&o9=&o6=&o3=&o4=&s=hand&i=8&h=1100000000000000000000000#
c
                                                                                                         21
WordNet Has an NLTK Interface
>>> from nltk.corpus import wordnet
Some useful functions:
   >>> wordnet.synsets(<query_term>)
   >>> wordnet.synset(<synset_name>)
Remember you can use dir and help to get a list of
functions in Python.
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Word Sense Disambiguation
Figuring out which word sense is expressed in context
   His hands were tired from hours of typing.
   à hand.n.01
   Due to her superior education, her hand was flowing and
   graceful.
   à hand.n.03
General idea: use words in the context to disambiguate.
Which words above would help with this?
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Possible Computational Approaches
A heuristic algorithm
   • Lesk’s algorithm
Supervised machine learning
   • Possible, but requires a lot of work to annotate word
     sense information that we want to avoid
Unsupervised, or minimally supervised machine
learning
   • Yarowsky’s algorithm
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Lesk’s Algorithm (1986)
More like a family of algorithms which, in essence,
choose the sense whose dictionary definition shares
the most words with the target word’s neighborhood.
Steps to disambiguate word 𝑤:
   1. Construct a bag of words representation of the context, 𝐵
   2. For each candidate sense 𝑠$ of word 𝑤:
      • Calculate a signature of the sense by taking all of the words
        in the dictionary definition of 𝑠$
      • Compute Overlap(𝐵, signature(𝑠$ ))
   3. Select the sense with the highest overlap score
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Financial Bank or Riverbank?
                          Construct from definitions of
                          all senses of context words
                                                  26
Model Variations
Which dictionary to use? NLTK?
Use only dictionary definitions? Or include example
sentences?
Ignore uninformative stopwords (e.g., the, a, of)?
Lemmatize when considering matches (tomatoes
matches tomato)?
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Exercise
Run the Lesk algorithm using NLTK/WordNet. Ignore
stop words, include examples, count lemma overlap.
Consider only the top two senses of bank.
   1. I’ll deposit the cheque at the bank.
   2. The bank overflowed and water flooded the town.
                                                        28
Yarowsky’s Algorithm (1995)
A method based on bootstrapping
Goal: Learn a classifier for a target word
Steps:
   1. Gather a data set with target word to be disambiguated
   2. Automatically label a small seed set of examples
   3. Repeat the following for a while:
      • Train a supervised learning algorithm from the seed set
      • Apply the supervised model to the entire data set
      • Keep the highly confident classification outputs to be the
        new seed set
   4. Use the last model as the final model
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Yarowsky’s Example
Step 1: Disambiguating plant
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Step 2: Initial Seed Set
Sense A:
   • plant as in a lifeform
Other data
Sense B:
   • plant as in a factory
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Step 3: Train a Classifier
He went with a decision-list classifier (we didn’t cover
this one in class)
Note how new collocations are found for each sense
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Step 3: Change Seed Set
Use only the cases where classifier is highly confident
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Results
96% on binary word sense distinctions
Same result as with supervised methods, but with
minimal amounts of annotation effort!
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Notes on Yarowski’s Algorithm
The key to any bootstrapping approach lies in its ability
to create a larger training set from a small set of seeds:
   • Need an accurate initial set of seeds
   • Need a good confidence metric for picking good new
     examples to add to the training set
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