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Multilingual Robust Anaphora Resolution

Summary

Pronoun resolution systems of the 1990s usually depended on rich syntactic, semantic and domain knowledge, which is costly to build and nearly always targeted English only. This paper adapts the authors' earlier practical approach (Mitkov 1998a) into a lightweight, parser-free method for pronouns in technical manuals and tests it in three languages: English, Polish and Arabic. The text is first split into sentences, POS-tagged and chunked into noun phrases. Candidate antecedents from the current and two preceding sentences must pass a gender and number agreement check. Each surviving candidate is then scored by a set of empirically derived "antecedent indicators," such as definiteness, givenness, indicating verbs, term preference and referential distance. The highest total score determines the antecedent, and fixed tie-breaking rules handle equal scores. Adapting the method to Polish and Arabic took only small, language-specific changes, and the unmodified version already performed reasonably well. Success rates reached about 90% or higher, which suggests that cheap, knowledge-poor methods can serve as a practical multilingual baseline, including for morphologically rich languages.

Key points

  • English achieved an overall success rate of 89.7%, while Polish and Arabic reached 93.3% and 95.2% respectively. The English evaluation was the most extensive.
  • For Arabic, the unadapted ("Arabic direct") version resolved 57 of 63 anaphors (90.5%), and the adapted version ("Arabic improved") resolved 60 of 63. The adapted version changed the non-prepositional phrase and referential distance indicators, and its critical success rate rose from 78.6% to 89.3%.
  • The Polish test set came from online technical manuals and contained 180 pronouns, 120 of them exophoric (mostly zero pronouns). The unmodified version scored 90%, and the critical success rate was 86.2%.
  • The two Polish baselines performed far worse: "Baseline Subject" reached 23.7% and "Baseline Most Recent" reached 68.4%.
  • Each indicator scores a candidate between −1 and 2. The most discriminative indicators (immediate reference, sequential instructions, collocation) were also among the least frequently triggered.

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

Topics: Coreference resolution

Cited by

6 papers of our library cite it (of the 140673 whose bibliographies we read); 3 of them have a page here, 3 not yet.

Cite

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

@inproceedings{mitkov1998multilingual,
  title = {{Multilingual Robust Anaphora Resolution}},
  author = {Mitkov, Ruslan and Belguith, Lamia and Stys, Malgorzata},
  booktitle = {Proceedings of the Third Conference on Empirical Methods for Natural Language Processing},
  year = {1998},
  url = {https://aclanthology.org/W98-1502/}
}

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