1 / 22

Collocations and Terminology

Collocations and Terminology. Vasileios Hatzivassiloglou University of Texas at Dallas. Collocations. Frank Smadja, “Retrieving Collocations from Text”, Computational Linguistics , 1993 Recurrent combinations of words that co-occur more often than chance, often with non-compositional meaning

garren
Download Presentation

Collocations and Terminology

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Collocations and Terminology Vasileios Hatzivassiloglou University of Texas at Dallas

  2. Collocations • Frank Smadja, “Retrieving Collocations from Text”, Computational Linguistics, 1993 • Recurrent combinations of words that co-occur more often than chance, often with non-compositional meaning • Technical and non-technical

  3. Examples of collocations • The Dow Jones average of industrials • The Dow average • The Dow industrials • *The Jones industrials • The Dow Jones industrial • *The industrial Dow • *The Dow industrial

  4. Collocation properties • Arbitrary (dialect dependent) • ride a bike, set the table • Domain dependent • dry suit, wet suit • Recurrent • Cohesive • Part of a collocation primes for the rest

  5. Applications • Lexicography • Grammatical restrictions (compare with/to but associate with) • Generation • Translation

  6. Types of collocations • Predicative relations • make a decision, hostile takeover • flexible (syntactic variability, intervening words) • Rigid word groups • over the counter market • Phrases with open slots • fluency in a domain

  7. Issues in finding collocations • Possibly more than two words • Need measure that extends beyond the binary case • Possibly intervening words • Possibly morphological and syntactic variation • Semantic constraints (cf. doctors-dentists and doctors-hospitals)

  8. Xtract stage one • For a given word, find all collocates at positions -5 to +5 • Three criteria: • strength (normalized frequency); 95% rejection vs. expected 68% under normal distribution • position histogram must not be flat • select peak from histogram

  9. Xtract stage two • Start from word pairs • Look at each position in between, to the left, and to the right • Keep words that appear very often • If that fails, keep parts of speech that satisfy this criterion

  10. Xtract stage three • Applied to pairs of words • Requires (partial) parsing • Examines the syntactic relationship between words and keeps those pairs with consistent relationships (e.g., verb-object)

  11. Evaluation • Ask lexicographer to evaluate output • 40% precision after stages one and two • 80% precision after stage three • 94% conditional recall

  12. Terminology • Béatrice Daille, “Study and Implementation of Combined Techniques for Automatic Extraction of Terminology”, ACL Balancing Act workshop, 1994 • Terms refer to concepts • Terms key for populating a domain ontology • Terms are typically nominal compounds of certain structure, e.g., NN, N of N

  13. Defining terms • Unique reference • Unique translation • Term extension by • modification (e.g., addition of an adjective) • substitution • extension of structure • coordination

  14. Algorithm • Apply syntactic constraints to match pairs of words in a candidate term • Filter by application of an association measure • Measures examined: pointwise mutual information, Φ2 (chi-square), log-likelihood ratio

  15. Observations • Compare with reference list • Frequency a strong predictor • Log-likelihood ratio works best • Additional criteria: • diversity of the distribution of each word • distance between the two words (determines flexibility but not term status)

  16. Justeson and Katz • Justeson and Katz, “Technical Terminology: Some Linguistic Properties and an Algorithm for Identification in Text”, Natural Language Engineering, 1995.

  17. Analysis • Examined association measures • Well-known problems: • eliminating general-language constructs (e.g., collocations) • what to do with single word terms?

  18. Observations • Frequency works well • But a stronger predictor is P(k>1) compared to P(k≥1) in the same document • Use syntactic patterns to propose terms, then check if they reappear in the same document • Require this across multiple documents

  19. Term Expansion • Jacquemin, Klavans, and Tzoukermann, “Expansion of Multi-Word Terms for Indexing and Retrieval Using Morphology and Syntax”, ACL 1997. • Need to expand a given list of terms, especially for scientific domains

  20. Term variation • Syntactic (same words, different structure) • Morphosyntactic (derivational forms of words) • Semantic (synonyms are used) • In IR, normalization through stemming and removal of stop words

  21. Approach • Process corpus matching new candidate terms to old ones via unification • Matching based on • inflectional morphology (transducer) • derivational morphology (rule-based) • syntactic transformations • additions of words

  22. Results • Manual inspection of several thousand proposed terms • Precision of 89% • Effectiveness in indexing increases by a factor of three when using the variants (P/R from 99.7/72 to 97/93)

More Related