[PDF] Top 20 Semantic Classification with Distributional Kernels
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Semantic Classification with Distributional Kernels
... cessing: distributional measures of lexical simi- larity and kernel methods for ...these kernels are closely related to measures known from the distributional similarity ... See full document
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Visualisation and Exploration of High Dimensional Distributional Features in Lexical Semantic Classification
... lexical semantic classification: au- tomatic classification of words according to their ...identifying semantic classes of verbs, or nouns, or prepositions, ...tational semantic classes ... See full document
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Linear Compositional Distributional Semantics and Structural Kernels
... convolution kernels (Haussler, 1999), e.g.: tree kernels (Collins and Duffy, ...tree kernels (Zanzotto and Dell’Arciprete, 2012) are an interesting result to draw a stronger link between CDS models ... See full document
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Distributional semantic phrases vs. semantic distributional nonsense: Adjective Modification in Compositional Distributional Semantics
... non- semantic factors should also be taken into ...the semantic measures we considered ...exploits semantic, metrical and lexicalization features jointly for maximal classification ... See full document
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Semantic Role Labeling Systems for Arabic using Kernel Methods
... AST+EAST+Poly3 is slightly reduced. This may be attributed to the fact that they produce similar boundary detection results, which in turn, for the global SRL outcome, are summed to those of the classification ... See full document
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Fine Grained Classification of Named Entities Exploiting Latent Semantic Kernels
... We present a kernel-based approach for fine- grained classification of named entities. The only training data for our algorithm is a few manually annotated entities for each class. We defined kernel functions that ... See full document
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Choosing which to use? A study of distributional models for nominal lexical semantic classification
... lexical semantic clas- sification of verbs (Merlo and Stevenson, 2001), which selected very specific ad-hoc linguistic cues for classify- ing verbs undergoing different types of diathesis alterna- tions, while ... See full document
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Improving Text Classification by a Sense Spectrum Approach to Term Expansion
... as semantic smoothing matrix (Siolas and d’Alch´e Buc, 2000; Shawe-Taylor and Cristianini, 2004; Basili et ...the semantic similarity between the terms i and ...the semantic matrix S is almost fully ... See full document
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Semantic transparency: challenges for distributional semantics
... to semantic relations, the situation is a bit more ...compound classification algorithm on the ...a distributional model of AB semantic transparency, the vector for A should be given more ... See full document
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CCG Categories for Distributional Semantic Models
... vs. not-bare noun distinctions, they are not so rel- evant for noun classification. However, they in- deed play an important role in distinguishing some classes of verbs. Thus, embedding CCG cate- gories in the ... See full document
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Squibs: When the Whole Is Not Greater Than the Combination of Its Parts: A “Decompositional” Look at Compositional Distributional Semantics
... The Convolution Conjecture offers a general way to rewrite the phrase similarity com- putations of CDSMs by highlighting the role played by the subparts of a composed representation. This perspective allows for a better ... See full document
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Verb Classification using Distributional Similarity in Syntactic and Semantic Structures
... such structures, i.e., kernel functions, which can also exploit distributional lexical semantics, to train au- tomatic classifiers. The basic idea of such functions is to compute the similarity between two verbs ... See full document
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Integrating Distributional and Lexical Information for Semantic Classification of Words using MRMF
... In this paper we consider the task of classifying words into a large number of semantic categories. For this, we use two different data sets: 1. A dataset which is used in literature (Bullinaria and Levy, 2007) - ... See full document
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Exploiting Syntactic and Shallow Semantic Kernels for Question Answer Classification
... An effective way to integrate syntactic structures in machine learning algorithms is the use of tree ker- nel (TK) functions (Collins and Duffy, 2002), which have been successfully applied to question classifi- cation ... See full document
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Supervised Learning of German Qualia Relations
... alternatively, distributional semantic) information for the task pro- posed here - namely, the supervised classification of qualia-like relations - partly mirrors results for the supervised ... See full document
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Investigating the Contribution of Distributional Semantic Information for Dialogue Act Classification
... this distributional hypothe- sis have recently been applied to many tasks, but mostly at the word level: for instance, word sense disambiguation (Zhitomirsky-Geffet and Dagan, 2009) and lexical substitution ... See full document
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Towards a Distributional Model of Semantic Complexity
... The semantic complexity model we have proposed in this paper is strongly inspired by the general cognitive principles of the MUC ...a distributional subset of GEK, such that the more an event is strongly ... See full document
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Computing Semantic Compositionality in Distributional Semantics
... The evaluation of models of compositionality is still a very uncertain and problematic issue. Previous work has relied mainly on “external” tasks such as rating sentence similarity or detection idioms. These evaluation ... See full document
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Semantic Kernels for Semantic Parsing
... tree kernels: (i) Partial Tree Kernel (PTK), which can be effectively applied to both constituency and dependency parse trees (Moschitti, ...or semantic kernel (SK) (Croce et ...latent semantic ... See full document
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Finding Non Arbitrary Form Meaning Systematicity Using String Metric Learning for Kernel Regression
... and semantic vectors derived from a distributional se- mantic model, the Nadaraya-Watson estimator can estimate the position in the semantic vector space for each word in the ... See full document
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