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Several of these, e.g. ゴールドプラン gōrudopuran “gold plan” and エースストライカー ēsusutoraikā “ace striker” were category 2 translations, and their possible inclusion in a dictionary would largely be because their meanings are not readily apparent from the component words, and an expanded gloss would be required. Some points which emerge from the analysis of the results of the tests described above are: a. to some extent, the Google n-gram test data had a bias towards the types of constructions favoured by Japanese webpage designers, e.g. ショッピングトップ shoppingutoppu “shopping top”, which possibly inflated the proportion of translations being scored with a 2; b. some of the problems leading to a failure to segment the MWEs were due to the way the English n-gram files were constructed. Words with apostrophes were split, so that “men’s” was recorded as a bigram: “men+’s”. This situation is not currently handled in CLST, which led to some of the segmentation failures, e.g. with メンズアイテム menzuaitemu “men’s item”; 6 Conclusion and Future Work In this paper we have described the CLST (Corpus-based Loanword Segmentation and Translation) system which has been developed to segment Japanese loanword MWEs and construct likely English translations. The system, which leverages the availability of large bilingual dictionaries of loanwords and English n-gram corpora, is achieving high levels of accuracy in discriminating between single loanwords and MWEs, and in segmenting MWEs. It is also generating useful translations of MWEs, and has the potential to being a major aide both to lexicography in this area, and to translating. The apparent success of an approach based on a combination of large corpora and relatively simple heuristics is consistent with the conclusions reached in a number of earlier investigations (Banko and Brill, 2001; Lapata and Keller, 2004). Although the CLST system is performing at a high level, there are a number of areas where refinement and experimentation on possible enhancements can be carried out. They include: a. instead of using the “first-gloss” heuristic, experiment with using all available glosses. This would be at the price of increased processing time, but may improve the performance of the segmentation and translation; b. align the searching of the n-gram corpus to cater for the manner in which words with apostrophes, etc. are segmented. At present this is not handled correctly; c. tune the presentation of the glosses in the dictionaries so that they will match better with the contents of the n-gram corpus. At present the dictionary used is simply a concatenation of several sources, and does not take into account such things as the n-gram corpus having hyphenated words segmented; d. extend the system by incorporating a back-transliteration module such as that reported in Bilac and Tanaka (2004). This would cater for single loanwords and thus provide more complete coverage. References Masayuki Asahara and Yuji Matsumoto. 2003. IPADIC version 2.7.0 User’s Manual (in Japanese). NAIST, Information Science Division. Timothy Baldwin and Su Nam Kim. 2009. Multiword expressions. In Nitin Indurkhya and Fred J. Damerau, editors, Handbook of Natural Language Processing. CRC Press, Boca Raton, USA, 2nd edition. Michele Banko and Eric Brill. 2001. Scaling to very very large corpora for natural language disambiguation. In Proceedings of the 39th Annual Meeting of the ACL and 10th Conference of the EACL (ACL-EACL 2001), Toulouse, France. Slaven Bilac and Hozumi Tanaka. 2004. A Hybrid Back-transliteration System for Japanese. In Proceedings of the 20th international conference on Computational Linguistics, COLING ’04, Geneva, Switzerland. James Breen. 2004. JMdict: a Japanese- Multilingual Dictionary. In Proceedings of the COLING-2004 Workshop on Multilingual Resources, pages 65–72, Geneva, Switzerland. Eric Brill, Gary Kacmarcik, and Chris Brockett. 2001. Automatically Harvesting Katakana- 68
English Term Pairs from Search Engine Query Logs. In Proceedings of the Sixth Natural Language Processing Pacific Rim Symposium, November 27-30, 2001, Hitotsubashi Memorial Hall, National Center of Sciences, Tokyo, Japan, pages 393–399. Yasuharu Den, Toshinobu Ogiso, Hideki Ogura, Atsushi Yamada, Nobuaki Minematsu, Kiyotaka Uchimoto, and Hanae Koiso. 2007. The development of an electronic dictionary for morphological analysis and its application to Japanese corpus linguistics (in Japanese). Japanese Linguistics, 22:101–123. EDR, 1995. EDR Electronic Dictionary Technical Guide. Japan Electronic Dictionary Research Institute, Ltd. (in Japanese). Yōichi Kabasawa and Morio Satō, editors. 2003. A Dictionary of Katakana Words. Gakken. Noriko Kando, Kazuko Kuriyama, and Masaharu Yoshioka. 2001. Overview of Japanese and English Information Retrieval Tasks (JEIR) at the Second NTCIR Workshop. In Proceedings of the Second NTCIR Workshop, Jeju, Korea. Kevin Knight and Jonathan Graehl. 1998. Machine Transliteration. Comput. Linguist., 24(4):599–612, December. Taku Kudo and Hideto Kazawa. 2007. Japanese Web N-gram Corpus version 1. http://www. ldc.upenn.edu/Catalog/docs/LDC2009T08/. Taku Kudo, Kaoru Yamamoto, and Yuji Matsumoto. 2004. Applying conditional random fields to Japanese morphological analysis. In Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing (EMNLP 2004), pages 230–237, Barcelona, Spain. Sadao Kurohashi and Makoto Nagao. 1998. Nihongo keitai-kaiseki sisutemu JUMAN [Japanese morphological analysis system JU- MAN] version 3.5. Technical report, Kyoto University. (in Japanese). Mirella Lapata and Frank Keller. 2004. The web as a baseline: Evaluating the performance of unsupervised web-based models for a range of NLP tasks. In Proceedings of the Human Langauge Techinology Conference and Conference on Empirical Methods in National Language Processing (HLT/NAACL-2004), pages 121–128, Boston, USA. Yuji Matsumoto, Akira Kitauchi, Tatsuo Yamashita, Yoshitaka Hirano, Hiroshi Matsuda, Kazuma Takaoka, and Masayuki Asahara. 2003. Japanese Morphological Analysis System ChaSen Version 2.3.3 Manual. Technical report, NAIST. Yoshihiro Matsuo, Mamiko Hatayama, and Satoru Ikehara. 1996. Translation of ’katakana’ words using an English dictionary and grammar (in Japanese). In Proceedings of the Information Processing Society of Japan, volume 53, pages 65–66. Toshiaki Nakazawa, Daisuke Kawahara, and Sadao Kurohashi. 2005. Automatic Acquisition of Basic Katakana Lexicon from a Given Corpus. In Proceedings of the 2nd International Joint Conference on Natural Language Processing (IJCNLP-05), pages 682–693, Jeju, Korea. Ivan A. Sag, Timothy Baldwin, Francis Bond, Ann Copestake, and Dan Flickinger. 2002. Multiword expressions: A pain in the neck for NLP. In Proceedings of the 3rd International Conference on Intelligent Text Processing and Computational Linguistics (CICLing-2002), pages 1–15, Mexico City, Mexico. Watanabe Toshiro, Edmund Skrzypczak, and Paul Snowdon (eds). 2003. Kenkyûsha New Japanese-English Dictionary, 5th Edition. Kenkyûsha. 69
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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
Page 25 and 26: 2012). The TE model provides a form
Page 27 and 28: Correlation coefficient with human
Page 29 and 30: Pair # Concept 1 Doc. Freq. Concept
Page 31 and 32: Active Learning and the Irish Treeb
Page 33 and 34: 2.2 Sources of annotator disagreeme
Page 35 and 36: complement (csubj) 2 . See Figure 4
Page 37 and 38: top Y trees from this ordered set a
Page 39 and 40: References Ron Artstein and Massimo
Page 41 and 42: Unsupervised Estimation of Word Usa
Page 43 and 44: not lend itself to determining unsu
Page 45 and 46: T 0: think, want, thing, look, tell
Page 47 and 48: correlation is much stronger than t
Page 49 and 50: References Eneko Agirre and Philip
Page 51 and 52: eaders with sentences with a negati
Page 53 and 54: Question Which word is closest in m
Page 55 and 56: Acceptability and negativity: conce
Page 57 and 58: MORE NEGATIVE / LESS NEGATIVE MR Δ
Page 59 and 60: Julie Elizabeth Weeds. 2003. Measur
Page 61 and 62: cation produced by a supervised mac
Page 63 and 64: understood to carry a lower weight
Page 65 and 66: Figure 1: Average classification ac
Page 67 and 68: 0.8 0.75 0.7 AuthorA4 AuthorB4 Auth
Page 69 and 70: Segmentation and Translation of Jap
Page 71 and 72: tomatosōsu “tomato sauce”, rev
Page 73 and 74: “markup” and “Mach”, so the
Page 75: MWE Segmentation Possible Translati
Page 79 and 80: techniques used are, of necessity,
Page 81 and 82: ation metrics robust, if they are v
Page 83 and 84: Figure 2: Methodologies of human as
Page 85 and 86: an optimal method? Machine translat
Page 87 and 88: Towards Two-step Multi-document Sum
Page 89 and 90: archical and non-hierarchical relat
Page 91 and 92: We use the MetaMap 7 tool to automa
Page 93 and 94: T T & C CC z -1.5 -1.27 -1.33 p-val
Page 95 and 96: References Alan R. Aronson. 2001. E
Page 97 and 98: techniques and results are shown. F
Page 99 and 100: Rock Hip-Hop Electronic Punk Metal
Page 101 and 102: Rank Frequency Frequency (filtered)
Page 103 and 104: Ex. 1 Ex. 2 Ex. 3 Score Song Title
Page 105 and 106: Free-text input vs menu selection:
Page 107 and 108: consistent with the much earlier ed
Page 109 and 110: 3.2 Dialogue system architecture Th
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Page 113 and 114: tions in analyzing algebra example
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Page 117 and 118: documents of MIME type text/html wi
Page 119 and 120: Acknowledgments NICTA is funded by
Page 121 and 122: LaBB-CAT: an Annotation Store Rober
Page 123 and 124: elationship between the coincidence
Page 125 and 126: Communication 33 (special issue on
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matically extracting location infor
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Classifier DBp:1R Geo:1R D+G:1R DBp
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ALTA Shared Task papers 123
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All Struct. Unstruct. Total - Abstr
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System Population Intervention Back
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tence positions (absolute and relat
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sitional attributes, and sequential
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ordering labels, All includes BOW,
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3 Software Used All experimentation
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Combination Output Public Private C
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Experiments with Clustering-based F
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3 Results For the initial experimen
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