1. Unit-4 Lexical Semantics M.B.Chandak, HoD CSE,

2. Contents Semantics Word Sense Ambiguity Basic Logic Form Language
Encoding Ambiguity in Logic Form
Verb and States in Logic Form
Thematic roles
Selection preferences and restriction

3. Contents and Outcome
Lexical semantics and word-sense disambiguation. Compositional semantics. Semantic Role Labeling and Semantic Parsing.
Outcome
Ability to perform semantic parsing and word sense disambiguation.

4. Lexical Semantics Study of Word meaning
Lexicon: Word, Token or smallest unit of language: Lemma or Stem
Majorly for understanding lexical semantics, words are represented using “Lemma”.
The process of mapping word to lemma is called as Lemmatization.

5. Lexical Semantics
To understand semantics/meaning of lexicon, context reference is important.
For example: Word “Bank”  Organization or sloping mound.
Homonyms: Word Sense: One word with multiple sense.
Two types of relationship between senses:
Synonyms and Antonyms

6. Lexical Semantics Synonyms and Antonyms
Synonyms are words having same meaning in some or all context.
They can replace each other.
Antonyms: Words having opposite meaning.

7. Thematic Roles
It is data structure used to represent semantic constructs of any sentence.
Thematic roles identifies major activities of any sentence to remove ambiguity.
Thematic roles are useful in answering questions related to sentence or group of sentences.
Example: Who, What, How, When, Where, Which etc.
The main use of Thematic roles in is designing question-answer systems.

8. Thematic Roles: Types Consider Examples:
John broke the window with hammer
The hammer broke the window
The window broke
Three types of Semantic/Thematic Roles: AGENT, THEME, INSTRUMENT
Agent=Noun John
Theme=Entity  Window
Instrument  Mean by which action is performed.

9. Thematic Roles
Agent  Participant, which initiate the action  Generally Noun
The Tiger jumped on deer
John baked the cake
Theme  Answer to question “What”
The entity moved by action or whose location is described. The “thing” affected by event
David kicked the ball high
The book is in the library rack
The cloud appeared over the horizon

10. Thematic Roles Instrument  Mean by which action is performed
John broke window with hammer
John wrote letter with blue pen
It is a force/tool causing an event
Generally subject of preposition “with”
Result  The entity undergoing effect of some action or change of state.
The sun melted the ice

11. Thematic Roles Beneficiary  [For Whom]
The entity for “whose” benefit action was performed.
David brought car for Mary
I gave book to John for Susan
Experiencer  Person involved in perception
Physical / psychological state
David saw the tiger
Henry believes that France will win

12. Thematic Roles Goal  The destination of an object of transfer event.
I want to go to movie
Susan was awarded with prize
Source  The origin of object of transfer event.
David saw tiger in forest.
Henry got Ipad from his friend

13. Using Wordnet Step1: To Import Wordnet
>>> from nltk.corpus import wordnet
Step2: To pass the word for finding senses
>>> syns=wordnet.synsets("program”)
>>> print(syns)
[Synset('plan.n.01'), Synset('program.n.02'), Synset('broadcast.n.02'), Synset(‘platform.n.02'), Synset('program.n.05'), Synset('course_of_study.n.01'), Synset('program.n.07'), Synset('program.n.08'), Synset('program.v.01'), Synset('program.v.02')]
>>> print(syns[0].lemmas()) [Lemma('plan.n.01.plan'), Lemma('plan.n.01.program'), Lemma('plan.n.01.programme')]

14. Using Wordnet >>> syns=wordnet.synsets("book")
>>> print(syns1) [Synset('book.n.01'), Synset('book.n.02'), Synset('record.n.05'), Synset('script.n.01'), Synset('ledger.n.01'), Synset('book.n.06'), Synset('book.n.07'), Synset('koran.n.01'), Synset('bible.n.01'), Synset('book.n.10'), Synset('book.n.11'),Synset('book.v.01'), Synset('reserve.v.04'), Synset('book.v.03'), Synset('book.v.04')]
print(syns[0].lemmas()[0].name())
print(syns[0].defintion())

15. Using Lemmatizer and stemmer
from nltk.stem import WordNetLemmatizer
lemmatizer=WordNetLemmatizer()
print(lemmatizer.lemmatize("better", pos='a'))
print(lemmatizer.lemmatize("celebrating", pos='v'))
from nltk.stem import PorterStemmer
stm = PorterStemmer()
stm.stem('buses')
lemmatizer.lemmatize('buses)
lemmatizer.lemmatize('making', pos='v‘)  make