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

Katya в 19:49, 5 ноября 2016

2016-11-05T19:49:40Z

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			Ekaterina Chernyak, Dmitry Ilvovsky

	== Examples ==		== Examples ==

Polidson: /* Web or corpus search approach */

2015-09-03T00:41:01Z

Web or corpus search approach

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	=== Web or corpus search approach ===		=== 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);

			K<sub>lem </sub> ← 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]]

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

			P(w<sub>i</sub>) — frequency of (w<sub>i</sub>) P(w<sub>i</sub>, w<sub>j</sub>) — extraction probability of (w<sub>i</sub>,w<sub>j</sub>)
			demo: [http://serelex.cental.be/\|http://serelex.cental.be/]

Polidson: /* Dependency triple [Lin, 1998] */

2015-09-03T00:32:36Z

Dependency triple [Lin, 1998]

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	[[Файл:L6p3.jpg\|~~300px~~\|слева]]		[[Файл:L6p3.jpg\|450px\|слева]]

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

	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		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

Polidson: /* Context-based approach (1) [Lin, 1998] */

2015-09-03T00:31:58Z

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

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	=== Context-based approach (1) [Lin, 1998] ===		=== 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\|300px\|слева]]

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

			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 ===		=== Web or corpus search approach ===

Polidson: /* Distributional semantics */

2015-09-03T00:23:14Z

Distributional semantics

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	=== Distributional semantics ===		=== Distributional semantics ===

			[[Файл:L6p1.jpg\|500px\|слева]]

			<br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br>
			<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] ===		=== word2vec [Mikolov, Chen, Corrado, Dean, 2013] ===

Polidson: Новая страница: «== Examples == * '''Synonyms''': Netherlands and Holland, buy and purchase * '''Near synonyms''': pants, trousers and slacks, mistake and error == Approaches to…»

2015-08-23T22:49:56Z

Новая страница: «== Examples == * '''Synonyms''': Netherlands and Holland, buy and purchase * '''Near synonyms''': pants, trousers and slacks, mistake and error == Approaches to…»

Новая страница

== 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 ===

=== 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 ===