The Presentation contains about Word Sense Diassambiguation. I had tried to explain about the Word Sense in terms of Python language. But it can be also done using nltk.

1. Word Sense Disambiguation

2. ● Word Sense Disambiguation (WSD) is one of the core challenging area
for researchers since several decades and it plays a crucial role in all
natural language processing (NLP) applications viz. Information
Retrieval, Information Extraction, Question Answering, Text Mining,
Machine Translation etc.
● Researchers defined WSD as to identify the actual meaning of a word
based on the context in which it occurs. Whereas in linguistic, context is
defined as the text in which a word or passage appears and which helps
ascertain its meaning.
● Hence, context of a word depends on different part of speech (POS) of a
sentence i.e. Noun, Verb, pronoun, adjective and adverb. The novel
approach for context based word sense disambiguation using soft sense
disambiguation, map-reduce, knowledge based multimodal algorithm
and WordNet.
Word Sense Disambiguation

3. Lexical Analysis
● A lexical entry consists of a headword (also
● known as a lemma) along with additional
information, such as the part-of-speech and
the sense definition.
● Two distinct words having the same spelling
are called homonyms.
● The simplest kind of lexicon is nothing more
than a sorted list of words.

4. Lexical Resources
● A lexicon, or lexical resource, is a collection of
words and/or phrases along with associated
information, such as part-of-speech and sense
definitions.
● Lexical resources are secondary to texts, and
are usually created and enriched with the help
of texts.
● For example, if we have defined a text my_text
, then vocab = sorted(set(my_text)) builds

5. Lexical Entry
● Saw – verb – psat tense of see
● Saw – Noun – cutting instrument