Foundations of Natural Language Processing
Lecture 14
Lexical Semantics:
Word senses, relations, and classes
Alex Lascarides
(slides by Lascarides, Schneider, Koehn, Goldwater)
6 March 2020
Alex Lascarides FNLP Lecture 14 6 March 2020
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Lexical Semantics: Understanding Word Meanings in Natural Language Processing, Exercises of Painting
A lecture note from the Foundations of Natural Language Processing (FNLP) course, specifically Lecture 14, which focuses on lexical semantics. The lecture discusses the importance of understanding word meanings in building a question answering system and introduces various challenges in lexical semantics, such as word senses, synonyms, hyponyms, and polysemy. The lecture also presents WordNet as a solution to some of these problems.
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Foundations of Natural Language Processing
Lecture 14
Lexical Semantics:
Word senses, relations, and classes
Alex Lascarides (slides by Lascarides, Schneider, Koehn, Goldwater)
6 March 2020
Alex Lascarides FNLP Lecture 14 6 March 2020
Meaning
- One grand goal of artificial intelligence is to understand what people mean when they talk.
- But how do we know if we succeeded?
What is meaning? What is understanding?
- These are deep philosophical questions
- NLP usually takes a more pragmatic view: can the computer behave as though it understands (in order to do what we want)?
- Dialogue systems (e.g., Eliza)
- Machine translation
- Question answering
- What issues will we face in building such systems?
A Concrete Goal
- We would like to build
- a machine that answers questions in natural language.
- may have access to knowledge bases
- may have access to vast quantities of English text
- Basically, a smarter Google
- This is typically called Question Answering
Why is lexical semantics important for building such a system?
Example Question
- Question
When was Barack Obama born?
- Text available to the machine
Barack Obama was born on August 4, 1961
- This is easy.
- just phrase a Google query properly: "Barack Obama was born on *"
- syntactic rules that convert questions into statements are straight-forward
Example Question (2)
- Question
What plants are native to Scotland?
- Text available to the machine
A new chemical plant was opened in Scotland.
- What is hard?
- words may have different meanings (senses)
- we need to be able to disambiguate between them
Example Question (4)
- Question
Which animals love to swim?
- Text available to the machine
Polar bears love to swim in the freezing waters of the Arctic.
- What is hard?
- words can refer to a subset (hyponym) or superset (hypernym) of the concept referred to by another word
- we need to have database of such A is-a B relationships, called an ontology
Example Question (5)
- Question
What is a good way to remove wine stains?
- Text available to the machine
Salt is a great way to eliminate wine stains
- What is hard?
- words may be related in other ways, including similarity and gradation
- we need to be able to recognize these to give appropriate responses
WordNet
- Some of these problems can be solved with a good ontology, e.g., WordNet
- WordNet (English) is a hand-built resource containing 117,000 synsets: sets of synonymous words (See http://wordnet.princeton.edu/)
- Synsets are connected by relations such as
- hyponym/hypernym (IS-A: chair-furniture)
- meronym (PART-WHOLE: leg-chair)
- antonym (OPPOSITES: good-bad)
- globalwordnet.org now lists wordnets in over 50 languages (but variable size/quality/licensing)
Word Sense Ambiguity
- One word form, same category, but more than one sense (homonyms):
I put my money in the bank. vs. He rested at the bank of the river. I like playing squash vs. I like drinking squash
- More generally, words can have multiple (related or unrelated) senses
- Words often exhibit sense ambiguities that fall into (semi-)predictable patterns (polysemy): see next slides (from Hugh Rabagliati in PPLS).
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How many senses?
- 5 min. exercise: How many senses does the word interest have?