Lecture 3. POS tagging. Key word and phrase extraction — различия между версиями
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== POS taggers == | == POS taggers == | ||
+ | |||
+ | * Corpus- or dictionary-based VS rule-based | ||
+ | * Ngram-based taggers: | ||
+ | ** unigram tagging: assign the most frequent tag | ||
+ | ** ngram tagging: look at the context of n previous words (requires a lot of training data) | ||
+ | * Trade-off between the accuracy and the coverage: combine different taggers | ||
+ | |||
+ | ===NLTK POS default tagger=== | ||
+ | |||
+ | <code> | ||
+ | |||
+ | In[1]: from nltk.tag import pos tag | ||
+ | |||
+ | In[2]: print pos tag(['ship']) | ||
+ | |||
+ | Out[1]: [('ship', 'NN')] | ||
+ | |||
+ | In[3]: print pos tag(['shipping']) | ||
+ | |||
+ | Out[2]: [('shipping', 'VBG')] | ||
+ | </code> | ||
+ | |||
+ | See [http://www.nltk.org/api/nltk.tag.html|http://www.nltk.org/api/nltk.tag.html] for more details on | ||
+ | learning taggers. | ||
== Exercise 3.1 Genre comparison == | == Exercise 3.1 Genre comparison == |
Версия 00:49, 24 августа 2015
Содержание
- 1 Part of speech (POS)
- 2 POS ambiguation
- 3 POS taggers
- 4 Exercise 3.1 Genre comparison
- 5 Key word and phrase extraction
- 6 Supervised methods for key word and phrase extraction
- 7 Unsupervised methods for key word and phrase extraction from a single text
- 8 Bigram association measures
- 9 TextRank: using graph centrality measures for key word and phrase extraction (1) [Mihalcea, Tarau, 2004]
- 10 Unsupervised methods for key word and phrase selection from a text in a collection
- 11 Variants of TF and IDF weights
- 12 TF-IDF in NLTK
- 13 TF-IDF alternatives
- 14 Using TF-IDF to measure text similarity
Part of speech (POS)
Part of speech [Manning, Shuetze, 1999]
Words of a language are grouped into classes which show similar syntactic behavior. These word classes are called parts of speech (POS). Three important parts of speech are noun, verb, and adjective. The major types of morphological process are in ection, derivation, and compounding.
There are around 9 POS according to different schools:
- Nouns (NN, NP), pronouns (PN, PRP), adjectives (JJ): number, gender, case
- Adjective (JJ): comparative, superlative, short form
- Verbs (VB): subject number, subject person, tense, aspect, modality, participles, voice
- Adverbs (RB), prepositions (IN), conjunctions (, CS), articles (AT)
and particles (RP): nothing
POS ambiguation
Ship (noun or verb?)
- a luxury cruise ship
- Both products are due to ship at the beginning of June
- A new engine was shipped over from the US
- The port is closed to all shipping
Contest (noun or verb?)
- Stone decided to hold a contest to see who could write the best song.
- She plans to contest a seat in Congress next year.
POS taggers
- Corpus- or dictionary-based VS rule-based
- Ngram-based taggers:
- unigram tagging: assign the most frequent tag
- ngram tagging: look at the context of n previous words (requires a lot of training data)
- Trade-off between the accuracy and the coverage: combine different taggers
NLTK POS default tagger
In[1]: from nltk.tag import pos tag
In[2]: print pos tag(['ship'])
Out[1]: [('ship', 'NN')]
In[3]: print pos tag(['shipping'])
Out[2]: [('shipping', 'VBG')]
See [1] for more details on learning taggers.