Another work presented world congress on computational intelligence has been: Evolving Natural Language Grammars without Supervision. paper: Training a Classifier for Good Query Expansion Terms with a Genetic Algorithm.
This work is devoted to unsupervised grammar induction, whose goal is to extract a grammar representing the language structure using texts without annotations of this structure. We have devised an evolutionary algorithm which for each sentence evolves a population of trees that represent different parse trees of that sentence. Each of these trees represent a part of a grammar.
The evaluation function takes into account the contexts in which each sequence of Part-Of-Speech tags (POSseq) appears in the training corpus, as well as the frequencies of those POSseqs and contexts.
The algorithm has been evaluated using a well known Annotated English corpus, though the annotation have only been used for evaluation purposes. Results indicate that the proposed algorithm is able to improve
the results of a classical optimization algorithm, such as EM (Expectation Maximization), for short
grammar constituents (right side of the grammar rules), and its precision is better in general.
The presentation can be found in:.
The paper is not available yet at IEEEExplore, but we can email to whoever is interested.