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Retrieving Collocations from Text: Xtract

Written for the Research library (claude-opus-5-5) from the paper text, in our own words: the license "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)" does not allow redistributing the text itself. Original — aclanthology.org/J93-1007.pdf

Summary

Collocations are word combinations that recur more often than chance would predict and whose choice is lexically arbitrary, e.g. "Dow Jones industrials" versus the unacceptable "Jones industrials". They matter for translation, language generation and non-native writers, yet few compiled resources list them. Earlier statistical extraction methods returned word associations without functional (syntactic) information and often produced spurious pairs that reflected quirks of the training corpus. Smadja presents Xtract, a lexicographic tool that extracts collocations in three stages. First, it uses statistics alone to find significant word pairs, which may occur in either order and with intervening words. Second, it extends these pairs into multi-word combinations and complex expressions. Third, it combines parsing with statistics to assign syntactic labels to the pairs and filter out bad ones. The system was run on a 10-million-word corpus of stock market news. A lexicographic evaluation estimated its precision at 80%. The work shows how adding syntactic filtering to corpus statistics yields richer, more reliable collocational resources.

Key points

  • The paper describes four properties of collocations: they are arbitrary, domain-dependent, recurrent, and cohesive lexical clusters. It builds on Benson's (1990) definition of a collocation as an arbitrary, recurrent word combination.
  • Stage 1 is related to Church and Hanks (1989), but its statistics carry more information. Stage 2 yields output similar to Choueka, Klein, and Neuwitz (1983) using simpler techniques.
  • The parsing-based filter in stage 3 raised precision from 40% to 80%, with 94% recall.
  • Xtract retrieves predicative collocations of several syntactic types, including noun–adjective, subject–verb, verb–object, verb–adverb, verb–particle and verb–verb. An example from the stock news is "advancers outnumbered".
  • The main test corpus was 10 million words of stock market news reports. Earlier partial results were published in Smadja and McKeown (1990).

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Cited by

165 papers of our library cite it (of the 140673 whose bibliographies we read); 5 of them have a page here, 160 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work.

@misc{smadja1993retrieving,
  title = {{Retrieving Collocations from Text: Xtract}},
  author = {Smadja, Frank},
  year = {1993},
  url = {https://aclanthology.org/J93-1007/}
}

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