The Derivation of a Grammatically Indexed Lexicon from the Longman Dictionary of Contemporary English
What it is about
The authors build a large lexicon with grammatical feature entries for a morphological and syntactic analyser (GPSG-based, from the Alvey toolkit project) by deriving it from the machine-readable Longman Dictionary of Contemporary English (LDOCE). LDOCE's grammar codes (for example T5 for a transitive verb with a that-clause, or V3 for an object plus infinitive with "to") are mapped by rules into feature-structure templates. Verb senses are then given a semantic type (subject raising, object raising, or equi) from the set of codes in the same sense. The software supports an interactive cycle: a morphological generator produces the surface forms of a base entry, and the user accepts or edits them. The dictionary's codes are hand-made and contain errors, such as a missing sentential-complement code for "consider" and senses split in the wrong place for "acknowledge". On a list of 139 hand-classified verbs the overall error rate of the code-to-entry process is about 14% (read from an OCR text), and most errors come from the rule for raising verbs. Over the 7,965 LDOCE verb entries, 719 senses are classed as possible predicate complements.
Ideas
- I1. Sense-level frames in
world/src/dictionary.rs: store the complement frame per word sense, not per entry, because the paper shows that frames in one entry are often split across senses. Check each sense with its own frame when the pupil dictionary gives the sense. - I2. Rule-based verb classes with a measured error rate: write the raising and equi rules over Mova's own curated verb list (about 140 verbs), report the error rate and the verbs misclassified, and fix rules only with a written reason. The paper's 14% and its main error source (raising read as equi) are the baseline to beat.
- I3. Interactive generation as a test: for each new base entry, generate the surface forms with
en/src/morph.rsand show them for review, so that a wrong inflection is caught when the entry is made, not in a parse. - I4. Negative control: shuffle the complement codes across verb senses and check that the class error rate rises well above the unshuffled value; a rule that does not depend on the codes is not classifying anything.
For Mova
The chapters are 13. Verbs (verb complementation) and 23. Complementation (equi and raising). The code is world/src/dictionary.rs (sense-level frames), en/src/morph.rs (surface forms) and en/src/dict.rs. Measure: class error rate on the hand-classified verb list, run twice. The paper's licence is NC: use the design only, and build the verb classes from Mova's own rules and the public UD data.
Cite
@inproceedings{boguraev1987derivation,
title = {{The Derivation of a Grammatically Indexed Lexicon from the Longman Dictionary of Contemporary English}},
author = {Boguraev, Bran and Briscoe, Ted and Carroll, John and Carter, David and Grover, Claire},
booktitle = {25th Annual Meeting of the Association for Computational Linguistics},
year = {1987},
doi = {10.3115/981175.981202},
url = {https://doi.org/10.3115/981175.981202}
}References
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