Toolkit for dictionary development
Revision as of 18:45, 4 July 2011 by Francis Tyers (talk | contribs)
We all do similar things when making dictionaries. Make a load of scripts that we hack to do a specific job, then throw them away a the end. It would be nice to have some maintained scripts that could lighten the load.
- When we're making a new dictionary, what resources do we have and use ?
- Descriptive grammar of some variety (best case, reference grammar, worst case collection of 'teach yourself stuff from the web')
- Glosses (either from the web, or from the grammar)
- Monolingual corpus
- Frequency list
- Partially made analyser
- Spellchecker (list of validated surface forms)
- Bilingual wordlists
- "full form" lists
- partial-full form lists (e.g. category but not gender)
- Wikipedia (e.g. to get lists of categorised proper names, and to categorise proper names)
- Wiktionary
- What kind of things might we want to do ?
- Assign possible categories to surface forms from the corpus
- e.g. DET *UNK* ADJ → DET N ADJ
- these classes/categories could be extracted somehow from a well-trained tagger.
- Assign possible features to surface forms in the corpus
- e.g. DET *UNK* ADJ.M.SG → DET N.M.SG ADJ.M.SG
- DET *UNK* ADJ.MF.PL → DET N.GD.PL ADJ.MF.PL
- Relate forms in the corpus between each other by means of paradigms
- Sometimes you might get conflicts, in this case the paradigm<->stem combinations could get a score which is related to the predictive power according to the corpus.
- features that are known to be ambiguous could get lower scores (this can be calculated from the partial dictionary)
- Sometimes you might get conflicts, in this case the paradigm<->stem combinations could get a score which is related to the predictive power according to the corpus.
- Sometimes you can just get good candidates from an ending + paradigm, e.g. all words that end in -ió, -joni, etc.
- Blacklist words from the corpus / hitparade, e.g. barbarismes.
Making a dictionary can be an iterative process, generate some candidates, add them to the dictionary, run the scripts again because you have more context.
- Payoffs
- You might want to add _all_ the possible analyses, but also be able to produce a "trimmed" dictionary.
- For example, adding all the adjectives in -oso, even if they are low frequency, because they might give you context for adding some higher frequency noun.
- In the end though, you want to be able to produce a dictionary trimmed to a bilingual dictionary. Also, if it needs to be manually evaluated, it should be trimmed.
