Proverb: Candidate Generation

For example, given the clue:

<Farrow of ``Peyton Place'' [3]: MIA>

the movie module returns:

1.0: 0.909091 MIA, 0.010101 TOM, 0.010101 KIP, ... 0.010101 BEN, 0.010101 PEG, 0.010101 RAY

The module returns a 1.0 confidence in its list, and gives higher weight to the person on the show with the given last name, while giving lower weight to other cast members.

Partial List of Expert Modules

• Bigram: All letter strings of the correct length are given non-zero probability based on the pattern of letters (``st'' more likely than ``zq''). Other ``implicit distribution'' modules include word sequences and 4-grams.

• Word lists (WordList-Big, WordList, WordListCWDB): Ignore the clue and return all valid words. WordList-Big contains over 2.1 million entries.

• CWDB-specific (ExactMatch, Transformations, Partial Match): Measure similarity between the clue and clues in the CWDB. Return the targets of the best matching clues. IR style.

• Lexical distance (Dijkstra[1-4], Encyclopedia, LSI-Ency, LSI-CWDB): Using a word-word similarity metric, compare the words in the clue to a database of possible targets. Return the best matching words. IR style.

• Database modules (Movie, Music, Geography, Writers, Compass, Myth, WordNet, WordNetSyns, RogestSyns, MobySyns): Each uses a specific mechanism for transforming clues into queries against databases collected from the web.

• Syntactic (Blanks-[Books,Geo,Movies,Music,Quotes], KindOf): Fill in the blanks (and KindOf variation) with different databases of text.

• Expert modules were written by approximately ten people.
• Components written in at least 4 languages (Java, C, C++, Perl).
• Expert modules differ in coverage (clues they attempt to answer), accuracy, and candidate-list length.