joint learning improves semantic

... pages 589–596, Ann Arbor, June 2005. c 2005 Association for Computational Linguistics Joint Learning Improves Semantic Role Labeling Kristina Toutanova Dept of Computer Science Stanford University Stanford, ... 2005. Support vec- tor learning for semantic argument classification. Machine Learning Journal. Vasin Punyakanok, Dan Roth, Wen tau Yih, Dav Zimak, and Yuancheng Tu. 2004. Semantic role labeling ... our model is joint, we are able to use information about other argument nodes when labeling a node. Final Pipeline Here we describe the application in testing of a joint model for semantic role...

Ngày tải lên: 08/03/2014, 04:22

Ngày tải lên: 23/03/2014, 14:20

Ngày tải lên: 23/03/2014, 16:20

Ngày tải lên: 23/03/2014, 16:20

Ngày tải lên: 30/03/2014, 21:20

Ngày tải lên: 30/03/2014, 21:20

... algorithm that applies  1 / 2 regulariza- tion for joint feature selection over distributed stochastic learning processes. We present ex- periments on learning on 1.5 million training sentences, and ... that of (NT, term*, NT) on source and target sides. 4 Joint Feature Selection in Distributed Stochastic Learning The following discussion of learning methods is based on pairwise ranking in a Stochastic ... parallelized stochastic learning with feature selection inspired by multi-task learning. The simple but effective idea is to randomly divide training data into evenly sized shards, use stochastic learning on...

Ngày tải lên: 19/02/2014, 19:20

... Republic of Korea, 8-14 July 2012. c 2012 Association for Computational Linguistics Learning the Latent Semantics of a Concept from its Definition Weiwei Guo Department of Computer Science, Columbia ... latent semantics from sense definitions to improve elesk. However, the challenge lies in that sense defini- tions are typically too short/sparse for latent vari- able models to learn accurate semantics, ... will share the same latent semantics profile, as long as they are in the same topic/domain. To solve the sparsity issue we use missing words as negative evidence of latent semantics, as in (Guo and...

Ngày tải lên: 19/02/2014, 19:20

... by a hier- archy of coarse semantic roles. REL is an OTHER- style category for compounds encoding non-specific association. LEX(icalised) applies to compounds which are semantically opaque without ... simple as possible 6. The categories should provide useful semantic information These intuitively appear to be desirable principles for any semantic annotation scheme. The require- ment of class ... many languages where compounding is common but such resources are limited. The learning approach described here only cap- tures the lexical semantics of the individual con- situents. It seems intuitive that...

Ngày tải lên: 20/02/2014, 12:20

... 2005. Corpus- based learning of analogies and semantic relations. Machine Learning, 60(1–3):251–278. Ian H. Witten and Eibe Frank. 1999. Data Mining: Practical Machine Learning Tools and Techniques ... Machine Learning Algorithms We are also interested in comparing the perform- ance of machine learning algorithms on the task of mapping from n-gram frequencies of joining terms to semantic ... Noun-Modifier Semantic Relations. In Fifth Interna- tional Workshop on Computational Semantics, pp 285-301. Tillburg, Netherlands. Barbara Rosario and Marti A. Hearst. 2001. Classifying the semantic...

Ngày tải lên: 20/02/2014, 12:20

... Riloff, 2002) address this problem by learning multiple semantic categories simultaneously, relying on the often unrealistic assumption that a word cannot belong to more than one semantic category. How- 1483 4.2.2 ... when learning a relation and compare their yield, however this study is beyond the scope of this paper. We are particularly interested in the usage of re- cursive patterns for the learning of semantic ... Work A substantial body of work has been done in at- tempts to harvest bits of semantic information, in- cluding: semantic lexicons (Riloff and Shepherd, 1997), concept lists (Lin and Pantel,...

Ngày tải lên: 07/03/2014, 22:20

... Machine Learning, 37:277–296. E. Gabrilovich and S. Markovitch. 2007. Computing Semantic Relatedness using Wikipedia-based Explicit Semantic Analysis. Proceedings of the 20th Inter- national Joint ... alignment features with lexical semantic similarity measures us- ing machine learning techniques and show that the student answers can be more accu- rately graded than if the semantic measures were used ... and Sukkarieh, 2005) compares several machine learning techniques, including in- ductive logic programming, decision tree learning, and Bayesian learning, to the earlier pattern match- ing approach,...

Ngày tải lên: 07/03/2014, 22:20

... syntactic features from Section 4. Semantic The semantic features from Section 4. All Both syntactic and semantic features. All+Tmp (Causals Only) Syntactic and semantic features, plus the gold-standard ... of syntactic and semantic features performing 5 points better (43.6% precision and 55.8% recall) than either feature set individually. This suggests that our syn- tactic and semantic features ... 177–180, Columbus, Ohio, USA, June 2008. c 2008 Association for Computational Linguistics Learning Semantic Links from a Corpus of Parallel Temporal and Causal Relations Steven Bethard Institute...

Ngày tải lên: 08/03/2014, 01:20

... section, we describe an algorithm for learning semantic categories from search logs us- ing label propagation. We name the algorithm Quetchup. 3.1 Semi-supervised Learning by Laplacian Label Propagation Graph-based ... Click- through Data. KDD, pages 133–142. M. Komachi and H. Suzuki. 2008. Minimally Supervised Learning of Semantic Knowledge from Query Logs. IJC- NLP, pages 358–365. M. Pas¸ca and B. V. Durme. 2007. ... original form and manually assigned a semantic category. We allowed a query to have more than two categories. When a query had more than two terms, we as- signed a semantic category to the whole query...

Ngày tải lên: 08/03/2014, 01:20

... 89:385– 392. R. Ge and R. J. Mooney. 2005. A statistical semantic parser that integrates syntax and semantics. In Computational Natural Language Learning (CoNLL), pages 9–16, Ann Arbor, Michigan. P. ... IJCNLP of the AFNLP, pages 91–99, Suntec, Singapore, 2-7 August 2009. c 2009 ACL and AFNLP Learning Semantic Correspondences with Less Supervision Percy Liang UC Berkeley pliang@cs.berkeley.edu Michael ... difficulty—Robocup sportscasting, weather forecasts (a new domain), and NFL recaps. 1 Introduction Recent work in learning semantics has focused on mapping sentences to meaning representa- tions (e.g., some logical...

Ngày tải lên: 17/03/2014, 01:20

... area of semantic class learning, under a variety of different names and with a variety of different goals. Given the great deal of similar work in infor- mation extraction and ontology learning, ... weakly supervised or unsupervised semantic class (i.e., supertype-based) learning, since that is most related to the work in this paper. Fully unsupervised semantic clustering (e.g., (Lin, 1998; ... and Volker, 2005; Mann, 2002)), and learning semantic relations such as meronymy (Berland and Charniak, 1999; Girju et al., 2003). Our research focuses on semantic lexicon induc- tion, which...

Ngày tải lên: 17/03/2014, 02:20

... frame semantics to machine translation tasks. 1 Introduction As early as in the 1950s, semantic nets were in- vented as an “interlingua” for machine transla- tion. The semantic net” or semantic ... Juraf- sky.(2002).Automatic Labeling of Semantic Roles. In Computational Linguistics, Vol 28.3: 245-288. Rebecca Green, Bonnie Dorr, Philip Resnik. (2004). Inducing Frame Semantic Verb Classes from ... 2006. c 2006 Association for Computational Linguistics Robust Word Sense Translation by EM Learning of Frame Semantics Pascale Fung and Benfeng Chen Human Language Technology Center Department...

Ngày tải lên: 17/03/2014, 04:20

... 960–967, Prague, Czech Republic, June 2007. c 2007 Association for Computational Linguistics Learning Synchronous Grammars for Semantic Parsing with Lambda Calculus Yuk Wah Wong and Raymond J. Mooney Department ... first empirical results to our knowledge on learning synchronous grammars that generate logical forms. Using statistical machine translation techniques, a semantic parser based on a synchronous context-free ... uses statistical models developed for syntax-based SMT for lexical learning and parse disambiguation. The result is a robust semantic parser that gives good performance in various domains. More...

Ngày tải lên: 17/03/2014, 04:20

... namely learning the rules that can be used for automatically selecting an ap- propriate description of an entity in a specific 1073 Learning Correlations between Linguistic Indicators and Semantic ... automated learning. It is also useful if the technique can specify enough constraints on the text to be generated so that the number of possible surface realizations that match the semantic ... that one, and (2) if there is more than one description, pick the one whose semantic vector is closest to the predicted semantic vector. Finally, the profile extractor will be used as part of...

Ngày tải lên: 17/03/2014, 07:20

... essential requirement for semantic construction. A semantic algebra for the combi- nation of RMRSs in a non-lexicalist setting is de- fined in (Copestake, 2007). Unsaturated semantic increments have ... purposes of semantics repre- the same predecessor can be regarded as alternatives. Solid arrows indicate which information from a previous level an IU is grounded in (based on); here, every semantic ... of core grammar rules. 5 Conclusions We have described a model of semantic process- ing of natural, spontaneous speech that strives to jointly satisfy syntactic and pragmatic con- straints (the latter...

Ngày tải lên: 17/03/2014, 22:20