Просмотр исходного текста страницы Lecture 6. Synonyms and near-synonyms detection

== Examples ==

* '''Synonyms''': Netherlands and Holland, buy and purchase
* '''Near synonyms''': pants, trousers and slacks, mistake and error

== Approaches to synonyms and near-synonyms detection ==

* Thesaurus-based approach
* Distributional semantics
* Context-based approach
* word2vec
* Web search-based approach

=== Synonyms in WordNet ===

Given a word, look for synonyms in every synset.

==== WordNet NLTK interface ====

<code>
In[1]: for i,j in enumerate(wn.synsets('error')):

In[2]: print "Meaning",i, "NLTK ID:", j.name()

In[3]: print "Definition:",j.definition()

In[4]: print "Synonyms:", ", ".join(j.lemma names())
</code>

Wordnet Web interface: [http://wordnetweb.princeton.edu/perl/webwn|http://wordnetweb.princeton.edu/perl/webwn]

=== Distributional semantics ===

[[Файл:L6p1.jpg|500px|слева]]

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 <br> <br> <br> <br> <br> <br>  

==== Exercise 6.1 ==== 

Calculate PPMI for Table 1.

==== Exercise 6.2 ==== 

Input: def.txt or your own text

Output 1: term-context matrix

Output 2: term-term similarity matrix (use cosine similarity)

Output 3: 2D visualization by means of LSA

Hint: use cfd = nltk.ConditionalFreqDist((term, context) for ...) for computing conditional frequency dictionary

Hint: use R for SVD and visualization

=== word2vec [Mikolov, Chen, Corrado, Dean, 2013] ===

Very complex machine learning (deep learning) applied to term-context matrices. 

There are two regimes:
* CBOW predicts the current word based on the context
* Skip-gram predicts surrounding words given the current word

word2vec project page: [https://code.google.com/p/word2vec/|https://code.google.com/p/word2vec/]
demo: [http://rare-technologies.com/word2vec-tutorial/|http://rare-technologies.com/word2vec-tutorial/]

Example: vec(Madrid) - vec(Spain) + vec(France) = vec(Paris)

=== Context-based approach (1) [Lin, 1998] ===

=== Web or corpus search approach ===