document: Extraction and search of chemical formulae in text documents on the web Often scientists seek to search for articles on the Web related to a particular chemical . When a scientist searches for a chemical formula using a search engine today , she gets articles where the exact keyword string expressing the chemical formula is found . Searching for the exact occurrence of keywords during searching results in two problems for this domain : a ) if the author searches for CH4 and the article has H4C , the article is not returned , and b ) ambiguous searches like `` He '' return all documents where Helium is mentioned as well as documents where the pronoun `` he '' occurs . To remedy these deficiencies , we propose a chemical formula search engine . To build a chemical formula search engine , we must solve the following problems : 1 ) extract chemical formulae from text documents , 2 ) index chemical formulae , and 3 ) designranking functions for the chemical formulae . Furthermore , query models are introduced for formula search , and for each a scoring scheme based on features of partial formulae is proposed tomeasure the relevance of chemical formulae and queries . We evaluate algorithms for identifying chemical formulae in documents using classification methods based on Support Vector Machines ( SVM ) , and a probabilistic model based on conditional random fields ( CRF ) . Different methods for SVM and CRF to tune the trade-off between recall and precision forim balanced data are proposed to improve the overall performance . A feature selection method based on frequency and discrimination isused to remove uninformative and redundant features . Experiments show that our approaches to chemical formula extraction work well , especially after trade-off tuning . The results also demonstrate that feature selection can reduce the index size without changing ranked query results much .
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