... DiscriminativeLanguageModel with Pseudo-Negative samples We propose a novel discriminativelanguage model; a DiscriminativeLanguageModelwith Pseudo-Negative samples (DLM-PN). In this model, pseudo-negative ... dis-criminative language models can achievemore accurate discrimination because theycan employ overlapping features and non-local information. However, discriminative language models have been used ... andnot all relevant features can be included. A discriminativelanguagemodel (DLM) assigns a scoreto a sentence , measuring the correct-ness of a sentence in terms of grammar and prag-matics,...
... comparedto similar research on L1 English by Heilman etal. (2008). Using more complex syntactic fea-tures, they obtained an adjacent accuracy of 52% with a PO model, and 45% witha MLR model. However, ... significantly with difficulty. Then, we addtwo NLP-oriented features, as described below: a statistical languagemodel and a measure of tensedifficulty.4.1 The language model The lexical difficulty of a ... Belgiumthomas.francois@uclouvain.beAbstractReading is known to be an essential taskin language learning, but finding the ap-propriate text for every learner is far fromeasy. In this context, automatic...
... NIST Language Recognition Evaluation database. 1 Introduction Spoken language and written language are similar in many ways. Therefore, much of the research in spoken language identification, ... Recognition Evaluation (LRE) data. The database was intended to establish a baseline of performance capability for language recognition of conversational tele-phone speech. The database contains recorded ... 2003. Acoustic, Pho-netic and Discriminative Approaches to Automatic language recognition, In Proc. of Eurospeech Masahide Sugiyama. 1991. Automatic language recog-nition using acoustic features,...
... Proceedings of the Human Language Technology Workshop, 272-277. ARPA. Raymond Lau, Ronald Rosenfeld, and Salim Roukos. 1993. Trigger-based language models: a maximum entropy approach. In Proceedings ... University, Baltimore, MD. Frederick Jelinek, John Lafferty, David M. Mager- man, Robert Mercer, Adwait Ratnaparkhi, Salim Roukos. 1994. Decision Tree Parsing using a Hid- den Derivational Model. ... 1, Nk at position k in the sentence. To ensure a proper probabilistic model we have to make sure that (1) and (2) are well defined con- ditional probabilities and that the model halts with...
... witha capital letter.Markables are the carriers of the actual annota-tion information. They can be queried by meansof string matching and by means of attribute-valuecombinations. A markable ... nominalattributes can have one of a (user-defined) closed setof possible values. The data model also supportsassociative relations between markables: Markableset relations associate arbitrarily many markables with ... the ACL Interactive Poster and Demonstration Sessions,pages 109–112, Ann Arbor, June 2005.c2005 Association for Computational Linguistics A Flexible Stand-Off Data Modelwith Query Language for...
... Ao and Toshihisa Takagi. 2005. ALICE: Analgorithm to extract abbreviations from MEDLINE.Journal of the American Medical Informatics Asso-ciation, 12(5):576–586.June A. Barrett and Mandalay ... forms asthe training/evaluation data.The evaluation metrics used in the abbreviationgeneration are exact-match accuracy (hereinafteraccuracy), including top-1 accuracy, top-2 accu-racy, and ... cross validation.We prepared six state-of-the-art abbreviationrecognizers as baselines: Schwartz and Hearst’smethod (SH) (2003), SaRAD (Adar, 2004), AL-ICE (Ao and Takagi, 2005), Chang and Schăutzesmethod...
... signif-icantly. Bear in mind that Charniak et al. (2003) in-tegrated Charniak’s languagemodelwith the syntax-based translation model Yamada and Knight pro-posed (2001) to rescore a tree-to-string ... Stochastic analysis of lexical andsemantic enhanced structural language model. The 8thInternational Colloquium on Grammatical Inference(ICGI), 97-111.K. Yamada and K. Knight. 2001. A syntax-based ... (EMNLP),858-867.E. Charniak. 2001. Immediate-head parsing for language models. The 39th Annual Conference on Associationof Computational Linguistics (ACL), 124-131.E. Charniak, K. Knight and K. Yamada. 2003....
... and Linda C. Bauman Peto. 1995. A hierarchical Dirichlet language model. Natural Lan-guage Engineering, 1(3):1–19.Y.W. Teh. 2006. A hierarchical Bayesian language model based on Pitman-Yor processes. ... n-grams:C(ab) − C(ab∗). A( ab) = max(1, K(C(ab) − C(ab∗))) A different K constant is chosen for each n-gramorder. Using this formulation as an interpolated 5-gram languagemodel gives a cross ... Speech and Language. R. Kneser and H. Ney. 1995. Improved backing-off form-gram language modeling. In International Confer-ence on Acoustics, Speech, and Signal Processing.David J. C. Mackay and...
... com-pression tasks achieved a significant com-pression rate without any loss.1 IntroductionThere has been an increase in available N -gramdata and a large amount of web-scaled N-gramdata has been ... the ACL-IJCNLP 2009 Conference Short Papers, pages 341–344,Suntec, Singapore, 4 August 2009.c2009 ACL and AFNLP A Succinct N-gram Language Model Taro Watanabe Hajime Tsukada Hideki IsozakiNTT ... Communication Science Laboratories2-4 Hikaridai Seika-cho Soraku-gun Kyoto 619-0237 Japan{taro,tsukada,isozaki}@cslab.kecl.ntt.co.jpAbstractEfficient processing of tera-scale text datais an important...
... Distortion models forstatistical machine translation. In ACL.D. Chiang. 2005. A hierarchical phrase-based model for statis-tical machine translation. In ACL.M. Collins. 2000. Discriminative reranking ... inference and train-ing of context-rich syntactic translation models. In ACL.P. Koehn. 2004. Pharaoh: A beam search decoder for phrase-based statistical machine translation models. In AMTA.R. ... 2002. Discriminative training and max-imum entropy models for statistical machine translation. InACL.F. J. Och and H. Ney. 2004. The alignment template approachto statistical machine translation....