Báo cáo khoa học: "A Class-Based Agreement Model for Generating Accurately Inflected Translations" pptx

... Association for Computational Linguistics, pages 146–155, Jeju, Republic of Korea, 8-14 July 2012. c 2012 Association for Computational Linguistics A Class-Based Agreement Model for Generating Accurately ... ample room for translation quality improve- ments through better selection of forms that already exist in the translation model. 2 A Class-based Model of Agreeme...

Ngày tải lên: 16/03/2014, 19:20

... re-ranking model performs rather well for a limited number of candidate structures, and out- performs Charniak’s model when k = 5. In this case we observe a small boost in performance for the detection ... consistently outper- forms the PCFG model on this metric, as for UAS, and BAS. Concerning the other metrics, as the number of k-best candidates increases, the PCFG model outpe...

Ngày tải lên: 20/02/2014, 04:20

... calculation formulas are similar with equations (13) and (14) respectively. Before training trigram model (3), all possible baseNP rules should be extracted from the training corpus. For instance, ... describe the two-pass statistical model, parameters training and Viterbi algorithm for the search of the best sequences of POS tagging and baseNP identification. Before describing our alg...

Ngày tải lên: 08/03/2014, 05:20

... paper, we formulate ex- tractive summarization as a two step learn- ing problem building a generative model for pattern discovery and a regression model for inference. We calculate scores for sentences ... hierarchical model and re- gression model to score sentences in new docu- ments, eliminating the need for building a genera- tive model for new document clusters. 3 Summ...

Ngày tải lên: 20/02/2014, 04:20

... of the Association for Computational Linguistics, pages 1367–1375, Uppsala, Sweden, 11-16 July 2010. c 2010 Association for Computational Linguistics A Unified Graph Model for Sentence-based ... relevant to the target concerned. Therefore, we argue that existing information representation i.e. bag-of-word, cannot satisfy the information needs for opinion retrieval. In this pape...

Ngày tải lên: 20/02/2014, 04:20

... 61–64, Prague, June 2007. c 2007 Association for Computational Linguistics A Joint Statistical Model for Simultaneous Word Spacing and Spelling Error Correction for Korean Hyungjong Noh* Jeong-Won ... network 8 4 Experiments and Analyses 4.1 Corpus Information Table 1: Corpus information Table 1 shows the information of corpus which is used for experiments. All corpora ar...

Ngày tải lên: 20/02/2014, 12:20

... June 2005. c 2005 Association for Computational Linguistics A Phonotactic Language Model for Spoken Language Identification Haizhou Li and Bin Ma Institute for Infocomm Research Singapore ... acoustic to- kens to form a unified acoustic vocabulary in our voice tokenizer. Readers are referred to (Ma et al. , 2005) for details of acoustic modeling. 3.1 Vector Space Modeling...

Ngày tải lên: 20/02/2014, 15:20

... blocks for for which . 560 4 Online Training of Maximum-entropy Model The local model described in Section 3 leads to the fol- lowing abstract maximum entropy training formulation: (8) In this formulation, ... is obtained for this model. The ’SWAP & OR’ model uses an orientation model as described in Section 3. Here, we obtain a small but significant improvement over the base-...

Ngày tải lên: 20/02/2014, 15:20

... cues for repair processing. Discussion In this paper, we have presented a"speech-first" model, the Repair Interval Model, for studying repairs in spon- taneous speech. This model ... AROA Air Travel Information System (ATIS) database. Our results are interpreted within our "speech-first" framework for investigating repairs, the REPAIR IN- TERVAL MODEL (...

Ngày tải lên: 20/02/2014, 21:20

... each feture. ALL: all features, PER: perceptron model, WLM: word language model, PLM: POS language model, GPR: generating model, LPR: labelling model, LEN: word count penalty. LM with Witten-Bell ... probability and the generating probability, 901 Algorithm 2 Decoding algorithm. 1: Input: character sequence C 1:n 2: for i ← 1 n do 3: L ← ∅ 4: for l ← 1 min(i, K) do 5: w ← C i−l...

Ngày tải lên: 08/03/2014, 01:20