Surprisal and Satisfaction: Towards an Information-theoretic Characterization of Presuppositions with a Diachronic Application

What it is about

A pilot study of presuppositions in Early Modern English (1500 to 1710). Six triggers are used: three additive particles (too, also, even) and three verbs with clausal complements (know, find, see). Each token is labelled by hand as given (overtly satisfied in the text), inferred, or new. The authors also compute an average surprisal for each associate of the trigger, using a count model over 50-year slices with a three-word context, on a syntactically parsed corpus. For the factive verbs, new uses are more surprising than given ones, and the share of new uses for know rises from E1 to E3. The additive particles do not show this surprisal gap. They are overtly satisfied in most tokens (Table 1; the table layout is garbled in the extracted text). The sample is small, and the authors say the surprisal measure needs much improvement.

Ideas

• I1. prag/src/frames.rs: add a deterministic satisfaction label (given, inferred, new) to each presupposition frame. Given: the associate is already stated earlier in the same text. Inferred: a bridging rule links it. New: otherwise. Print the label counts per text.
• I2. A count-based surprisal for each associate: the sum of -log2 of smoothed word-bigram probabilities, with counts in BTreeMap over open texts from tools/gutenberg, sliced by 50 years. Compare given and new associates per trigger. Run twice and check the output is identical.
• I3. Use the additive result as a control. If Mova finds the same given/new gap for too and also, check the labels first; the gap may come from the labelling, not from the language.
• I4. Make the trigger list closed and explicit: six triggers, and for the verbs a check that a clausal complement follows. Measure trigger precision on a hand-checked set.

For Mova

The period is in 1. The period, the register and the texts Mova reads. The presupposition triggers belong in 33. Implicature and presupposition, and the old forms in 10. Vocabulary: archaic words and false friends. The code is prag/src/frames.rs and prag/src/infer.rs. Measures: agreement of the labels with 100 hand-labelled tokens, the surprisal gap per label, and the run-twice check. Data: the paper uses PPCEME, which is not open, so for Mova any corpus must be open texts only.

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.22028/d291-31861

@inproceedings{gergel2017surprisal,
  title = {{Surprisal and Satisfaction: Towards an Information-theoretic Characterization of Presuppositions with a Diachronic Application}},
  author = {Gergel, Remus and Kopf-Giammanco, Martin and Masloh, Julia},
  booktitle = {Proceedings of the 12th International Conference on Computational Semantics (IWCS) –- Short papers},
  year = {2017},
  doi = {10.22028/d291-31861},
  url = {https://doi.org/10.22028/d291-31861}
}

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