Task ideas for Google Code-in/Tokenisation for spaceless orthographies
Objective
The objective of this task is to investigate how to best tokenise sentences in South and East Asian languages into words. Sentences in these languages are usually not written with spaces to show word boundaries.
Example
Imagineforamomentthatenglishwerewrittenwithoutspaces.
Given a fairly complete dictionary of English words it should be possible to generate all the possible ways of splitting up the sentence into words that are found in the dictionary:
Imagine·for·a·moment·that·english·were·writ·ten·with·out·spaces Imagine·fora·moment·that·english·were·writ·ten·with·out·spaces Imagine·for·a·moment·that·english·were·written·with·out·spaces Imagine·fora·moment·that·english·were·written·with·out·spaces Imagine·for·a·moment·that·english·were·writ·ten·without·spaces Imagine·fora·moment·that·english·were·writ·ten·without·spaces Imagine·for·a·moment·that·english·were·written·without·spaces Imagine·fora·moment·that·english·were·written·without·spaces
Tasks
Literature review
Input/output code
The input should be a sentence, and the output should be a lattice, for example:
^Imagine/Imagine$ ^fora/fora/for+a$ ^moment/moment$ ^that/that$ ^english/english$ \ ^were/were$ ^written/writ+ten/written$ ^without/with+out/without$ ^spaces/spaces$
Algorithms
- Longest-match left-to-right (LRLM)
- Maximal matching
- N-gram models
It should be possible to have for each word in the dictionary its possible parts-of-speech. Given this it should be possible to calculate n-gram co-occurrence probabilities, and use these to rank the possible segmentations.
Evaluation
Take about 3000 words of text in the language (about 6 pages) and split it into sentences. Then manually split the sentences into tokens. Compare the performance of the algorithm(s) you have implemented against the manually tokenised sentences.
