Lexicon and grammar in probabilistic tagging of written English
Note: the text file is an automatic OCR of the scanned paper, badly damaged in many places (tables and tree figures are unreadable). The summary below rests on the readable prose only.
What it is about
The paper describes the UCREL system at Lancaster, built with IBM support. Its word tagger, CLAWS, assigns part-of-speech tags by a lexicon of about 8,000 word forms, each with tags marked by three levels of probability, and a suffix list for unknown words. Tag selection uses first-order tag-pair probabilities from a tagged corpus. The author reports that CLAWS gives the right tag to over 96% of running words, leaving 3 to 4% for post-editing. The lexicon was extended to about 26,500 entries, from a list of 15,000 words tagged automatically and post-edited. Verbs were subcategorised: about 2,500 verbs coded from concordances, with an initial scheme of eleven verb subcategories. A grammar of about 10,000 context-free productions was built from a parsed databank of about 45,000 words, and a "skeleton parsing" scheme inserts only the uncontroversial brackets, which limits the parses the grammar must consider.
Ideas
- I1.
en/src/ptag.rsanden/src/tag.rs: a suffix list for unknown words with tag probabilities, as an explicit table (for example "-ide", "-ade", "-ee" map to NN with a stated weight). Measure tagging accuracy on unknown words only, on a held-out set. - I2. Keep the three-level plausibility marker: store for each lexicon word a level for each tag (common, possible, rare), in a BTreeMap, and use the level as the cost in the tag decision. Report the share of tokens decided by the marker alone.
- I3. Verb subcategories from evidence: a count-based frame table per verb, drawn from UD trees (object, prepositional, clausal complement), with the frame tested against a lexicographer's list. Use the paper's eleven-class scheme as the first pass in 13. Verbs.
- I4. Skeleton parsing as a method for
gparse: apply only high-confidence arcs first, then count how many alternative parses remain, before any statistic is used to choose among them. Report the reduction on a held-out sentence set.
For Mova
The chapters are 13. Verbs for subcategorisation and 20. Syntax: the Universal Dependencies frame and 21. The simple clause for the skeleton idea. The code points are en/src/ptag.rs and en/src/tag.rs for the tagger and suffix list, en/src/dict.rs for the marked lexicon, and en/src/parse.rs or gparse for the skeleton parse. The measure: unknown-word tagging accuracy with and without the suffix list, and the number of remaining parses after skeleton pruning, run twice for determinism.
Cite
@inproceedings{beale1988lexicon,
title = {{Lexicon and grammar in probabilistic tagging of written English}},
author = {Beale, Andrew David},
booktitle = {26th Annual Meeting of the Association for Computational Linguistics},
year = {1988},
doi = {10.3115/982023.982049},
url = {https://doi.org/10.3115/982023.982049}
}References
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