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Center for Computing Research National Polytechnic Institute Mexico. Acquiring Selectional Preferences from Untagged Text for Prepositional Phrase Attachment Disambiguation. Hiram Calvo and Alexander Gelbukh Presented by Igor A. Bolshakov. Introduction.

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Center for Computing ResearchNational Polytechnic InstituteMexico

Acquiring Selectional Preferences from Untagged Text for Prepositional Phrase Attachment Disambiguation

Hiram Calvo and Alexander Gelbukh

Presented by Igor A. Bolshakov

  • Entities must be identified adequately for database representation:
    • See the cat with a telescope
    • See [the cat] [with a telescope] 2 entities
    • See [the cat with a telescope]1 entity
  • Problem is known as Prepositional Phrase (PP) attachment disambiguation.
existing methods 1
Existing methods - 1
  • Accuracy when using treebank statistics:
    • Ratnaparkhi et al.,Brill and Resnik: up to 84%
    • Kudo and Matsumoto: 95.8%
      • Needed weeks for training
    • Lüdtke and Sato: 94.9%
      • Only 3 hours for training
  • But there are no treebanks for many languages!
existing methods 2
Existing methods - 2
  • Based on Untagged text:
    • Calvo and Gelbukh, 2003: 82.3% accuracy
    • Uses the web as corpus:
      • Slow (up to 18 queries for each PP attachment ambiguity)
  • Does this method work with very big local corpora?
using a big local corpus
Using a big local corpus
  • Corpus
    • 3 years of publication of 4 newspapers
    • 161 million words
    • 61 million sentences
  • Results:
    • Recall: 36% Precision: 67%
    • Dissapointing!
what do we want
What do we want?
  • To solve PP attachment disambiguation with
    • Local corpora, not web
    • No treebanks
    • No supervision
    • High precision and recall
  • Solution proposed:
    • Selectional Preferences
selectional preferences
Selectional Preferences
  • The problem of

I see a cat with a telescope

turns into

I see {animal} with {instrument}

sources for noun semantic classification
Sources for noun semantic classification
  • Machine-Readable dictionaries
  • WordNet ontology
    • We use the top 25 unique beginner concepts of WordNet
  • Examples: mouse is-a {animal}, ranch is-a {place}, root is-a part}, reality is-a {atrtibute}, race is-a {grouping}, etc.
extracting selectional preferences
Extracting Selectional Preferences
  • Text is shallow parsed
  • Subordinate sentences are separated
  • Patterns are searched

1. Verb NEAR Preposition NEXT_TO Noun

2. Verb NEAR Noun

3. Noun NEAR Verb

4. Noun NEXT_TO Preposition NEXT_TO Noun

  • All Nouns are classified
  • Consider this toy-corpus:
    • I see a cat with a telescope
    • I see a ship in the sea with a spyglass

The following patterns are extracted:

    • See,cat see,{animal}
    • See,with,telescope see,with,{instrument}
    • Cat,with,telescope {animal},with,{instrument}
    • See,ship see,{thing}
    • See,in,sea see,in,{place}
    • See,with,spyglass see,with,{instrument}
    • Ship,in,sea {thing},in,{place}
  • See, with, {instrument} has two occurrences
  • {Animal}, with, {instrument} has one occurrence
  • Thus,
    • See with {instrument} is more probable than {animal} with {instrument}
  • Now, with a real corpus, we apply the following formula:
  • X can be a specific verb or a noun’s semantic class (see or {animal})
  • P is a preposition (with)
  • C2 is the class of the second noun {instrument}
  • From the corpus of 161 million words of Spanish Mexican newspaper the system obtained:
  • 893,278 selectional preferences for 5,387verbs, and
  • 55,469noun patterns (like {animal} with {instrument})
  • We tested the obtained Selectional Preferences doing PP attachment disambiguation on 546 sentences from the LEXESP corpus (in Spanish).
  • Then we compared manually with the correct PP attachments.
  • Results: precision 78.2%, recall: 76.0%
  • Results not as good as those obtained by other methods (up to 95%)
  • But we don’t need any costly resources, such as:
    • Treebanks
    • Manually anotated corpora
    • Web as corpus
future work
Future Work
  • To use not only 25 fixed semantic classes (top concepts) but the whole hierarchy
  • To use a WSD module
    • Currently if a word belongs to more than one class, all classes are taken into accoutb
thank you

Thank you!