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Acknowledgments NICTA is funded by the Australian Government as represented by the Department of Broadband, Communications and the Digital Economy and the Australian Research Council through the ICT Centre of Excellence program. References Steven Bird, Ewan Klein, and Edward Loper. 2009. Natural Language Processing with Python — Analyzing Text with the Natural Language Toolkit. O’Reilly Media, Sebastopol, USA. Stephan Dreiseitl and Lucila Ohno-Machado. 2002. Logistic regression and artificial neural network classification models: a methodology review. Brain and Cognition, 35:352–359. Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang- Rui Wang, and Chih-Jen Lin. 2008. Liblinear: A library for large linear classification. J. Mach. Learn. Res., 9:1871–1874, June. Su Nam Kim, David Martinez, Lawrence Cavedon, and Lars Yencken. 2011. Automatic classification of sentences to support evidence based medicine. BMC Bioinformatics, 12:1–10. Robert E. Schapire. 1990. The Strength of Weak Learnability. Machine Learning, 5:197–227. Helmut Schmid. 1994. Probabilistic part-of-speech tagging using decision trees. In Proceedings of the Conference on New Methods in Natural Language Processing, Manchester, 1994. Mathias Verbeke, Vincent Van Asch, Roser Morante, Paolo Frasconi, Walter Daelemans, and Luc De Raedt. 2012. A statistical relational learning approach to identifying evidence based medicine categories. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 579–589, Jeju Island, Korea, July. Association for Computational Linguistics. David H. Wolpert. 1992. Stacked generalization. Neural Networks, 5:241–259. Sze-Meng Jojo Wong and Mark Dras. 2009. Contrastive analysis and native language identification. In Proceedings of the Australasian Language Technology Workshop 2009 (ALTW 2009), pages 53–61, Sydney, Australia, December. 138
Experiments with Clustering-based Features for Sentence Classification in Medical Publications: Macquarie Test’s participation in the ALTA 2012 shared task. Diego Mollá Department of Computing Macquarie University Sydney, NSW 2109, Australia diego.molla-aliod@mq.edu.au Abstract In our contribution to the ALTA 2012 shared task we experimented with the use of cluster-based features for sentence classification. In a first stage we cluster the documents according to the distribution of sentence labels. We then use this information as a feature in standard classifiers. We observed that the cluster-based feature improved the results for Naive-Bayes classifiers but not for better-informed classifiers such as MaxEnt or Logistic Regression. 1 Introduction In this paper we describe the experiments that led to our participation to the ALTA 2012 shared task. The ALTA shared tasks 1 are programming competitions where all participants attempt to solve a problem based on the same data. The participants are given annotated sample data that can be used to develop their systems, and unannotated test data that is used to submit the results of their runs. There are no constraints about what techniques of information are used to produce the final results, other than that the process should be fully automatic. The 2012 task was about classifying sentences of medical publications according to the PIBOSO taxonomy. PIBOSO (Kim et al., 2011) is an alternative to PICO for the specification of the main types of information useful for evidence-based medicine. The taxonomy specifies the following types: Population, Intervention, Background, Outcome, Study design, and Other. The dataset was provided by NICTA 2 and consisted of 1,000 1 http://alta.asn.au/events/sharedtask2012/ 2 http://www.nicta.com.au/ medical abstracts extracted from PubMed split into an annotated training set of 800 abstracts and an unannotated test set of 200 abstracts. The competition was hosted by “Kaggle in Class” 3 . Each sentence of each abstract can have multiple labels, one per sentence type. The “other” label is special in that it applies only to sentences that cannot be categorised into any of the other categories. The “other” label is therefore disjoint from the other labels. Every sentence has at least one label. 2 Approach The task can be approached as a multi-label sequence classification problem. As a sequence classification problem, one can attempt to train a sequence classifier such as Conditional Random Fields (CRF), as was done by Kim et al. (2011). As a multi-label classification problem, one can attempt to train multiple binary classifiers, one per target label. We followed the latter approach. It has been observed that the abstracts of different publication types present different characteristics that can be exploited. This lead Sarker and Mollá (2010) to the implementation simple but effective rule-based classifiers that determine some of the key publication types for evidence based medicine. In our contribution to the ALTA shared task, we want to use information about different publication types to determine the actual sentence labels of the abstract. To recover the publication types one can attempt to use the meta-data available in PubMed. However, as mentioned by Sarker and Mollá (2010), only a percentage of the PubMed abstracts is annotated with the publication type. Also, 3 http://inclass.kaggle.com/c/alta-nicta-challenge2 Diego Mollá. 2012. Experiments with Clustering-based Features for Sentence Classification in Medical Publications: Macquarie Test’s participation in the ALTA 2012 shared task. In Proceedings of Australasian Language Technology Association Workshop, pages 139−142.
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Australasian Language Technology As
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ALTA 2012 Workshop Committees Works
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ALTA 2012 Programme The proceedings
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Contents Invited talks 1 Using a la
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Invited talks 1
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Diverse Words, Shared Meanings: Sta
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A Citation Centric Annotation Schem
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The structure of this paper is as f
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understanding of sentences surround
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henceforth referred to as Annotator
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References Angrosh, M A, Cranefield
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Semantic Judgement of Medical Conce
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2012). The TE model provides a form
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Correlation coefficient with human
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Pair # Concept 1 Doc. Freq. Concept
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Active Learning and the Irish Treeb
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2.2 Sources of annotator disagreeme
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complement (csubj) 2 . See Figure 4
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top Y trees from this ordered set a
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References Ron Artstein and Massimo
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Unsupervised Estimation of Word Usa
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not lend itself to determining unsu
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T 0: think, want, thing, look, tell
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correlation is much stronger than t
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References Eneko Agirre and Philip
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eaders with sentences with a negati
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Question Which word is closest in m
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Acceptability and negativity: conce
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MORE NEGATIVE / LESS NEGATIVE MR Δ
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Julie Elizabeth Weeds. 2003. Measur
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cation produced by a supervised mac
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understood to carry a lower weight
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Figure 1: Average classification ac
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0.8 0.75 0.7 AuthorA4 AuthorB4 Auth
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Segmentation and Translation of Jap
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tomatosōsu “tomato sauce”, rev
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“markup” and “Mach”, so the
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MWE Segmentation Possible Translati
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English Term Pairs from Search Engi
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techniques used are, of necessity,
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ation metrics robust, if they are v
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Figure 2: Methodologies of human as
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an optimal method? Machine translat
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Towards Two-step Multi-document Sum
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archical and non-hierarchical relat
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We use the MetaMap 7 tool to automa
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T T & C CC z -1.5 -1.27 -1.33 p-val
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