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References Steven Bird, Edward Loper, and Ewan Klein. 2009. Natural Language Processing with Python. O’Reilly Media Inc. S. Burrows, A. L. Uitdenbogerd, and A. Turpin. 2009. Application of information retrieval techniques for source code authorship attribution. In Ramamohanarao Kotagiri Xiaofang Zhou, Haruo Yokota and Xuemin Lin, editors, International Conference on Database Systems for Advanced Applications, volume 14, pages 699– 713, Brisbane, Australia, April. G. L. Dillon. 2006. Corpus, creativity, cliché: Where statistics meet aesthetics. Journal of Literary Semantics, 35(2):97–103. B. Drummond and J. Cauty. 1988. The Manual (How to Have a Number One the Easy Way). KLF Publications, UK. H. Hirjee and D.G. Brown. 2009. Automatic detection of internal and imperfect rhymes in rap lyrics. In Proceedings of the 10th International Society for Music Information Retrieval Conference. H. Hirjee and D.G. Brown. 2010a. Rhyme Analyzer: An Analysis Tool for Rap Lyrics. In Proceedings of the 11th International Society for Music Information Retrieval Conference. H. Hirjee and D.G. Brown. 2010b. Solving misheard lyric search queries using a probabilistic model of speech sounds. In Proc. 11th International Society ofor Music Information Retrieval Conference, pages 147–152, Utrecht, Netherlands, August. ISMIR. Hussein Hirjee. 2010. Rhyme, rhythm, and rhubarb: Using probabilistic methods to analyze hip hop, poetry, and misheard lyrics. Master’s thesis, University of Waterloo. Florian Kleedorfer, Peter Knees, and Tim Pohle. 2008. Oh Oh Oh Whoah! Towards Automatic Topic Detection In Song Lyrics. In Proceedings of the 9th International Conference on Music Information Retrieval (ISMIR 2008), pages 287–292, September. P. Knees, M. Schedl, and G. Widmer. 2005. Multiple lyrics alignment: Automatic retrieval of song lyrics. In Proceedings of 6th international conference on music information retrieval (ismir05), pages 564–569. M. Koppel, J. Schler, and S. Argamon. 2009. Computational methods in authorship attribution. Journal of the American Society for information Science and Technology, 60(1):9–26. Dekang Lin. 1998. Extracting collocations from text corpora. In First Workshop on Computational Terminology, pages 57–63. B. Logan, A. Kositsky, and P. Moreno. 2005. Semantic analysis of song lyrics. In Multimedia and Expo, 2004. ICME’04. 2004 IEEE International Conference on, volume 2, pages 827–830. IEEE. Christopher D. Manning and Hinrich Schütze. 1999. Foundations of statistical natural language processing. MIT Press, Cambridge, MA, USA. R. Mayer, R. Neumayer, and A. Rauber. 2008. Rhyme and style features for musical genre classification by song lyrics. In ISMIR 2008: proceedings of the 9th International Conference of Music Information Retrieval, page 337. P. Pattison. 1991. Songwriting : essential guide to rhyming : a step-by-step guide to better rhyming and lyrics. Berklee Press, Boston. S.R. Pillay and T. Solorio. 2011. Authorship attribution of web forum posts. In eCrime Researchers Summit (eCrime), 2010, pages 1–7. IEEE. N. Ring and A. L. Uitdenbogerd. 2009. Finding ‘Lucy in Disguise’: the misheard lyric matching problem. In The Fifth Asia Information Retrieval Symposium, Sapporo, Japan, October. Gerard Salton and Christopher Buckley. 1988. Term-weighting approaches in automatic text retrieval. Inf. Process. Manage., 24:513–523, August. B. F. Skinner. 1941. A quantitative estimate of certain types of sound-patterning in poetry. American Journal of Psychology, 54(1):64–79, January. F. Smadja. 1993. Retrieving collocations from text: Xtract. Computational linguistics, 19(1):143–177. C. Whissell. 1996. Traditional and emotional stylometric analysis of the songs of Beatles Paul McCartney and John Lennon. Computers and the Humanities, 30(3):257–265. X. Xu, M. Naito, T. Kato, and K. Kawai. 2009. Robust and fast lyric search based on phonetic confusion matrix. In K. Hirata and G. Tzanetakis, editors, Proc. 10th International Society ofor Music Information Retrieval Conference, pages 417–422, Kobe, Japan, October. ISMIR. 96
Free-text input vs menu selection: exploring the difference with a tutorial dialogue system. Jenny McDonald 1 , Alistair Knott 2 and Richard Zeng 1 1 Higher Education Development Centre, University of Otago 2 Department of Computer Science, University of Otago jenny.mcdonald@otago.ac.nz Abstract We describe the in-class evaluation of two versions of a tutorial dialogue system with 338 volunteers from a first-year undergraduate health-sciences class. One version uses supervised machine-learning techniques to classify student free-text responses; the other requires students to select their preferred response from a series of options (menu-based). Our results indicate that both the free-text and menu-based tutors produced significant gains on immediate post-test scores compared to a control group. In addition, there was no significant difference in performance between students in the free-text and menu-based conditions. We note specific analysis work still to do as part of this research and speculate briefly on the potential for using tutorial dialogue systems in real class settings. 1 Introduction In large undergraduate classes (1500-1800 students), it is time-consuming, costly and seldom practical to provide students with feedback on their conceptions other than by computer-based marking of formative assessments. Typical examples of this include Learning Management System (LMS) based multiple-choice quizzes or similar. Most computer-assisted assessment involves students being able to recognise a correct response rather than recall and independently generate an answer. In the context of the first-year undergraduate health sciences course that we studied, currently all computer-assisted assessment takes this form. In 2008, the coordinator of a first year undergraduate health sciences class asked us about ways in which technologies might assist students to practice writing short-answer questions. As a result of this request, we wanted to investigate whether students answering questions with freetext or multiple-choice(menu-based) selections in a tutorial dialogue setting would result in performance gains on student test scores and whether there would be any difference in performance between students who generated free-text answers and those who selected their preferred answer from a number of options. In the next section we begin with a brief literature review from the fields of both Education and Cognitive Science. Next, we briefly describe the design and features of our tutorial dialogue system. The experimental design, and results are described in subsequent sections and we conclude with a discussion of our key findings. 2 Background This study is situated at the boundaries between at least three established fields of inquiry: educational assessment research; psychological research, in particular the study of memory, recognition and recall; and finally intelligent tutoring systems (ITS) and natural language processing (NLP) research. Since the 1920s the positive benefits on student performance of answering practice, or formative assessment, questions have been demonstrated in classroom studies (Frederiksen, 1984). Similar positive effects have been demonstrated in psychology laboratory studies since the 1970s. (McDaniel et al., 2007) Large meta-analytic educational studies looking at the impact of practice tests on student outcomes indicate that on average, the provision of practice assessments during a course of study does confer a clear advan- Jenny Mcdonald, Alistair Knott and Richard Zeng. 2012. Free-text input vs menu selection: exploring the difference with a tutorial dialogue system. In Proceedings of Australasian Language Technology Association Workshop, pages 97−105.
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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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