Generalised brown clustering and roll-up feature generation

30th AAAI Conference on Artificial Intelligence, AAAI 2016

Volumn , Issue , 2016, Pages 1533-1539

  Derczynski, Leon ^a   Chester, Sean ^b,c  

^a UNIVERSITY OF SHEFFIELD   (United Kingdom)

^b NORWEGIAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Norway)

^c AARHUS UNIVERSITY   (Denmark)

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING SYSTEMS;

ACTIVE SETS; COMPLEX METHODS; DISTRIBUTIONAL INFORMATION; FEATURE GENERATION; GENERALISATION; OVERALL QUALITY; ROLLING UP; WORD REPRESENTATIONS;

ARTIFICIAL INTELLIGENCE;

EID: 85007198068     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (25)

1. Bansal, M.; Gimpel, K.; and Livescu, K. 2014. Tailoring continuous word representations for dependency parsing. In Proc. ACL, volume 2, 809-815. ACL.
2. Baroni, M.; Dinu, G.; and Kruszewski, G. 2014. Don't count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors. In Proc. ACL, volume 1, 238-247.
3. Bengio, Y.; Ducharme, R.; Vincent, P.; and Janvin, C. 2003. A neural probabilistic language model. The Journal of Machine Learning Research 3:1137-1155.
4. Blunsom, P., and Cohn, T. 2011. A hierarchical Pitman-Yor process HMM for unsupervised part of speech induction. In Proc. ACL, volume 1, 865-874.
5. Brown, P. F.; deSouza, P. V.; Mercer, R. L.; Della Pietra, V. J.; and Lai, J. C. 1992. Class-based n-gram models of natural language. Computational Linguistics 18(4):467-479.
6. Chester, S., and Derczynski, L. 2015. generalisedbrown: Source code for AAAI 2016 paper. http://dx.doi.org/10.5281/zenodo.33758.
7. Christodoulopoulos, C.; Goldwater, S.; and Steedman, M. 2010. Two Decades of Unsupervised POS induction: How far have we come? In Proc. EMNLP, volume 1, 575-584.
8. Chrupała, G. 2012. Hierarchical clustering of word class distributions. In Proc. NAACL-HLT Workshop on the Induction of Linguistic Structure, 100-104.
9. Derczynski, L.; Chester, S.; and Bøgh, K. S. 2015. Tune Your Brown Clustering, Please. In Proc. RANLP, 110-117.
10. Francis, W. N., and Kucera, H. 1979. Brown corpus manual. Brown University.
11. Koo, T.; Carreras, X.; and Collins, M. 2008. Simple semisupervised dependency parsing. In Proc. ACL, 595-603.
12. Liang, P. 2005. Semi-supervised learning for natural language. Master's thesis, MIT.
13. Martin, S.; Liermann, J.; and Ney, H. 1998. Algorithms for bigram and trigram word clustering. Speech communication 24(1):19-37.
14. Miller, S.; Guinness, J.; and Zamanian, A. 2004. Name tagging with word clusters and discriminative training. In Proc. NAACL, volume 4, 337-342.
15. Owoputi, O.; O'Connor, B.; Dyer, C.; Gimpel, K.; and Schneider, N. 2012. Part-of-speech tagging for Twitter: Word clusters and other advances. Technical Report CMUML- 12-107, School of Computer Science, Carnegie Mellon University, Tech. Rep.
16. Plank, B., and Moschitti, A. 2013. Embedding semantic similarity in tree kernels for domain adaptation of relation extraction. In Proc. ACL, volume 1, 1498-1507.
17. Qu, L.; Ferraro, G.; Zhou, L.; Hou, W.; Schneider, N.; and Baldwin, T. 2015. Big data small data, in domain out-of domain, known word unknown word: The impact of word representation on sequence labelling tasks. In Proc. CoNLL, volume 1, 83-93. ACL.
18. Ratinov, L., and Roth, D. 2009. Design challenges and misconceptions in named entity recognition. In Proc. CoNLL, 147-155. ACL.
19. Shannon, C. E. 1956. The zero error capacity of a noisy channel. Information Theory, IRE Transactions on 2(3):8- 19.
20. Stratos, K.; Kim, D.-k.; Collins, M.; and Hsu, D. 2014. A spectral algorithm for learning class-based n-gram models of natural language. Proc. UAI.
21. Stratos, K.; Collins, M.; and Hsu, D. 2015. Model-based word embeddings from decompositions of count matrices. In Proc. ACL, volume 1, 1282-1291. ACL.
22. Šuster, S., and van Noord, G. 2014. From neighborhood to parenthood: The advantages of dependency representation over bigrams in Brown clustering. In Proc. COLING, 1382- 1391.
23. Šuster, S.; van Noord, G.; and Titov, I. 2015. Word representations, tree models and syntactic functions. CoRR abs/1508.07709.
24. Turian, J.; Ratinov, L.; and Bengio, Y. 2010. Word representations: A simple and general method for semi-supervised learning. In Proc. ACL, volume 1, 384-394. ACL.
25. van Rijsbergen, C. J. 1977. A theoretical basis for the use of co-occurrence data in information retrieval. Journal of Documentation 33(2):106-119.