Computing and Information Systems - Research Publications
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ItemDetecting modification of biomedical events using a deep parsing approach(BMC, 2012-04-30)BACKGROUND: This work describes a system for identifying event mentions in bio-molecular research abstracts that are either speculative (e.g. analysis of IkappaBalpha phosphorylation, where it is not specified whether phosphorylation did or did not occur) or negated (e.g. inhibition of IkappaBalpha phosphorylation, where phosphorylation did not occur). The data comes from a standard dataset created for the BioNLP 2009 Shared Task. The system uses a machine-learning approach, where the features used for classification are a combination of shallow features derived from the words of the sentences and more complex features based on the semantic outputs produced by a deep parser. METHOD: To detect event modification, we use a Maximum Entropy learner with features extracted from the data relative to the trigger words of the events. The shallow features are bag-of-words features based on a small sliding context window of 3-4 tokens on either side of the trigger word. The deep parser features are derived from parses produced by the English Resource Grammar and the RASP parser. The outputs of these parsers are converted into the Minimal Recursion Semantics formalism, and from this, we extract features motivated by linguistics and the data itself. All of these features are combined to create training or test data for the machine learning algorithm. RESULTS: Over the test data, our methods produce approximately a 4% absolute increase in F-score for detection of event modification compared to a baseline based only on the shallow bag-of-words features. CONCLUSIONS: Our results indicate that grammar-based techniques can enhance the accuracy of methods for detecting event modification.
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ItemWord sense disambiguation for event trigger word detection in biomedicine(BMC, 2011-03-29)This paper describes a method for detecting event trigger words in biomedical text based on a word sense disambiguation (WSD) approach. We first investigate the applicability of existing WSD techniques to trigger word disambiguation in the BioNLP 2009 shared task data, and find that we are able to outperform a traditional CRF-based approach for certain word types. On the basis of this finding, we combine the WSD approach with the CRF, and obtain significant improvements over the standalone CRF, gaining particularly in recall.
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ItemTake and Took, Gaggle and Goose, Book and Read: Evaluating the Utility of Vector Differences for Lexical Relation Learning(The Association for Computational Linguistics, 2016)Recent work on word embeddings has shown that simple vector subtraction over pre-trained embeddings is surprisingly effective at capturing different lexical relations, despite lacking explicit supervision. Prior work has evaluated this intriguing result using a word analogy prediction formulation and hand-selected relations, but the generality of the finding over a broader range of lexical relation types and different learning settings has not been evaluated. In this paper, we carry out such an evaluation in two learning settings: (1) spectral clustering to induce word relations, and (2) supervised learning to classify vector differences into relation types. We find that word embeddings capture a surprising amount of information, and that, under suitable supervised training, vector subtraction generalises well to a broad range of relations, including over unseen lexical items.
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ItemCollecting low-density language materials on the Web(Southern Cross University, 2006)Most web content exists in a few dozen languages. Hundreds of other languages - the `low-density languages' - are only represented in scarce quantities on the web. How can we locate, store and describe these low-density resources? In particular, how can we identify linguistically interesting resources, such as translation sets and multilingual documents? In this paper we describe ongoing research in which we integrate a number of discrete systems (language data crawler, automated metadata generation tools, language data repositories and federated search services) to address the identification, retrieval, description, storage and access issues for low-density language materials from the web.
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ItemAnalysis and prediction of user behaviour in a museum environment(Australasian Language Technology Association, 2006)N/A
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ItemReconsidering language identification for written language resources(European Language Resources Association, 2006)The task of identifying the language in which a given document (ranging from a sentence to thousands of pages) is written has been relatively well studied over several decades. Automated approaches to written language identification are used widely throughout research and industrial contexts, over both oral and written source materials. Despite this widespread acceptance, a review of previous research in written language identification reveals a number of questions which remain open and ripe for further investigation.
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ItemOpen source corpus analysis tools for Malay( 2006)Tokenisers, lemmatisers and POS taggers are vital to the linguistic and digital furtherment of any language. In this paper, we present an open source toolkit for Malay incorporating a word and sentence tokeniser, a lemmatiser and a partial POS tagger, based on heavy reuse of pre-existing language resources. We outline the software architecture of each component, and present an evaluation of each over a 26K word sample of Malay text.
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ItemLooking for Prepositional Verbs in Corpus Data(Association for Computational Linguistics, 2005)
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ItemClassifying dialogue acts in one-on-one live chats(The Association for Computational Linguistics, 2010-12-01)
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ItemUnsupervised parse selection for HPSG(The Association for Computational Linguistics, 2010-12-01)