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Joint Inference and Disambiguation of Implicit Sentiments via Implicature Constraints

Summary

Writers often signal opinions indirectly. Praising an event that harms something implies a negative view of the thing harmed and a positive view of whoever caused the event. This paper targets such implicit sentiments in sentences with goodFor/badFor (gfbf) events, which help or hurt a theme entity. Rather than chaining steps in a pipeline, the authors create local detectors for four sources of ambiguity: the polarity of the gfbf event, whether a reverser is present, the identification of the agent and theme among candidate noun phrases, and the writer's sentiment toward those entities. Their scores feed an unsupervised Integer Linear Programming (ILP) model. The implicature rules of Deng and Wiebe (2014) act as soft constraints, so violations are penalized rather than forbidden. On the gfbf-annotated corpus of Deng et al. (2013), with gold gfbf spans as input, joint inference raises sentiment F-measure by more than 10 points over the baselines and gfbf polarity accuracy by about 7 points. The other components lose nothing. The work matters because it shows that inference over events can recover opinions that lexicon-based detectors miss, while resolving several event-level ambiguities at once.

Key points

  • The constraints rest on one core regularity. Within a gf event, the writer feels the same way about agent and theme. Within a bf event, the feelings are opposite.
  • Agent and theme candidates come from two sources: SENNA semantic role labels (A0/A1/A2) and a dependency-based nearest-noun heuristic. Local sentiment scores rely on MPQA lexicon clues.
  • Sentiment results on the subset with correct spans:
    • Local detector: F 0.4384.
    • ILP: F 0.6421.
    • ILP+coref: F 0.6945.
    • Recall gains are significant under McNemar's test (p < .001).
  • gfbf polarity accuracy rises from 70.68% to 77.25% (ILP) and 77.47% (ILP+coref). Agent/theme span accuracy improves slightly, from 66.67% to 68.54%. Reverser accuracy is about 89% for ILP versus 88.07% for the baseline.
  • The gain over the local detector is 2.43 F points larger than in Deng and Wiebe (2014), even though that earlier system used manually annotated gfbf information.

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: Implicature

Cited by

12 papers of our library cite it (of the 140673 whose bibliographies we read); 1 of them have a page here, 11 not yet.

Cite

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

@inproceedings{deng2014joint,
  title = {{Joint Inference and Disambiguation of Implicit Sentiments via Implicature Constraints}},
  author = {Deng, Lingjia and Wiebe, Janyce and Choi, Yoonjung},
  booktitle = {Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers},
  year = {2014},
  url = {https://aclanthology.org/C14-1009/}
}

References

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