Integrating Aspectually Relevant Properties of Verbs into a Morphological Analyzer for English
What it is about
The paper is a short design note, with no evaluation. It shows how lexical semantic classes of English verbs can be written into a finite-state morphological analyser (xfst and foma, with a lexc lexicon). The base analyser has one inflectional class for regular verbs and a list of strong verbs. Vendler's four aspect classes are rejected for this purpose: they classify verb phrases, not verbs, and one verb can belong to several of them. The author uses the Levin and Rappaport Hovav view instead, where a dynamic verb lexicalises either a scale (result) or a manner, so the classes are disjoint. Each class becomes a separate sub-lexicon of the regular verbs, with the same continuation lexicon, and irregular verbs get a semantic tag. Dependency-style valency tags (for example subject-verb-object) are attached to whole classes, and one surface form can yield alternative analyses. The paper ends by noting that the method becomes hard for inflectionally rich languages.
Ideas
- I1. Disjoint verb classes in
world/src/dictionary.rs: tag each verb as manner or result (the school dictionary already has word senses), using the disjoint split and not Vendler's overlapping one. Check that every verb gets exactly one class. - I2. Use the manner/result split in the frame reader (
prag/src/frames.rs): manner verbs allow an unspecified object ("she swept"), result verbs need one. A rule that reads the class and the object's presence gives an explanation for each reading. - I3. Regression gate for the morphology: when the regular verbs are split into classes in
en/src/morph.rs, the generated forms must be byte-identical to the unsplit lexicon for every verb. Run it twice. - I4. Negative control: give each verb a random class and show that the frame-reader accuracy falls. If it does not, the class is not used.
For Mova
The chapters are 13. Verbs (verb classes) and 28. Voice and the passive (argument realisation). The code is en/src/morph.rs (class sub-lexicons), world/src/dictionary.rs (the class tag) and prag/src/frames.rs (the object rule). The paper gives no numbers, so the measures are the identity test on inflected forms and the negative control above. The paper is NC-licensed: use the design only and build any class data from Mova's own rules.
Cite
@inproceedings{bontcheva2012integrating,
title = {{Integrating Aspectually Relevant Properties of Verbs into a Morphological Analyzer for English}},
author = {Bontcheva, Katina},
booktitle = {Proceedings of the 10th International Workshop on Finite State Methods and Natural Language Processing},
year = {2012},
url = {https://aclanthology.org/W12-6205/}
}References
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