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Ekaterina Chernyak, Dmitry Ilvovsky

== 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]]

 
 

==== 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] ===

==== Dependency triple [Lin, 1998] ====

A dependency triple (w, r, w') consists of two words and the grammatical relationship between them in the input sentence.

I have a brown dog: (have subj I), (I subj-of have), (dog obj-of have), (dog adj-mod brown), (brown adj-mod-of dog), (dog det a), (a det-of dog)

||w, r, w'|| — frequency of (w, r, w')

||w, r, * || — total occurrences of w-r relationships

|| *, *, *|| — total number of dependency triples

Mutual information between w, w':

[[Файл:L6p2.jpg|300px|слева]]

[[Файл:L6p3.jpg|450px|слева]]

 

Results: '''brief(noun)''' — affidavit 0.13, petition 0.05, memo-randum 0.05, motion 0.05, lawsuit 0.05, depo-sition 0.05, slight 0.05, prospectus 0.04, docu-ment 0.04 paper 0.04
* some sort of dependency parsing is required
* no difference between synonyms and antonyms (win / loose the game)

=== Web or corpus search approach ===

==== Hearst patterns [Hearst, 1998] ====

Lexico-syntactic patterns to recognize hyponymy:
* such NP as NP, NP and / or NP;
* NP such as NP, NP and / or NP;
* NP, NP or other NP;
* NP, NP and other NP;
* NP, including NP, NP and / or NP;
* NP, especially NP, NP and / or NP;

Text ⇒ pattern ⇒ concordance

To rank concordances: PatternSim [Panchenko, Morozova, Naets]

Concordance ranking:

Input: Terms C, Corpus D

Output: Similarity matrix S(C x C)

K ← extract concord(D);

Klem ← lemmatize concord(K);

KC ← filter concord(Klem; C);

S ← get extractionfreq(C;K);

S ← rerank(S; C;D);

S ← normalize(S);

return S

Example ranking '''Efreq-Cfreq''':

[[Файл:L6p4.jpg|250px|слева]]

 

P(wi) — frequency of (wi) P(wi, wj) — extraction probability of (wi,wj)
demo: [http://serelex.cental.be/|http://serelex.cental.be/]

Lecture 6. Synonyms and near-synonyms detection - История изменений

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