Focusing for Pronoun Resolution in English Discourse: An Implementation

What it is about

The report implements the focusing algorithms that Candy Sidner described in her 1979 MIT thesis but did not implement. Focusing is a discourse model: a discourse focus, an ordered list of potential foci, and a stack of old foci, updated after each sentence. The program resolves third-person pronouns by searching this structure in a fixed order, using syntactic, semantic and simple inference criteria, and is run on several discourse segments (the texts are in its appendix). The resulting program resolves 79 of 102 pronouns correctly, a 77% success rate, with columns for each rule variant (for example, a modified recency rule applied only to third-person agent pronouns). The report also reviews Hobbs' naive syntactic algorithm, which it cites as correct in 81.8% of 100 examples, and Hobbs' distance statistics, in which 90.3% of antecedents fall in the first candidate set (the current sentence, or the current and previous sentences, depending on the pronoun's position). The authors conclude that focusing is computationally realizable and that a full system needs inference and discourse cues such as stress.

Ideas

• I1. Implement Sidner's focus stack in coref/src/sieve.rs as an explicit, inspectable state: after each sentence, record the focus, the potential foci and the old foci as BTreeMap-ordered lists, so each pronoun decision can print why it chose its antecedent.
• I2. Use the 77% result as a baseline check: Mova's pronoun resolution on a set of short segments of the same kind should be reported per pronoun (he, she, it, they) with the same occurrence and resolved columns.
• I3. Recency prior from the paper's distance counts: a feature in coref/ that prefers antecedents in the current sentence, then the previous one, with a negative control in which the candidates are shuffled. A gain should disappear under the shuffle.

For Mova

Code: coref/src/sieve.rs (focus state, pronoun rules), coref/src/mention.rs (candidate antecedents from UD heads), prag/src/frames.rs (subject and topic of the previous clause as a focus cue). Chapters: 12. Pronouns, determiners and numerals for the pronoun forms, 35. Reference and coreference for the focus model, 36. Discourse relations and coherence. Measure: per-pronoun resolved counts on the report's own discourse segments (as a reproduction, not a tuning set), run twice, with the shuffle control.

Topics: Discourse analysis · Coreference resolution

Cite

The paper itself, not this card. BibTeX from the library's record of the work. arXiv cmp-lg/9409005

@misc{ersan1994focusing,
  title = {{Focusing for Pronoun Resolution in English Discourse: An Implementation}},
  author = {Ersan, Ebru and Akman, Varol},
  year = {1994},
  eprint = {cmp-lg/9409005},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/cmp-lg/9409005}
}

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