The lexical semantics of verbs

Xem 1-20 trên 24 kết quả The lexical semantics of verbs
  • This paper presents a computational model of verb acquisition which uses what we will callthe principle of structured overeommitment to eliminate the need for negative evidence. The learner escapes from the need to be told that certain possibilities cannot occur (i.e.,are "ungrammatical") by one simple expedient: It assumes that all properties it has observed are either obligatory or forbidden until it sees otherwise, at which point it decides that what it thought was either obligatory or forbidden is merely optional.

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  • One of the hardest problems for knowledge extraction from machine readable textual sources is distinguishing entities and events that are part of the main story from those that are part of the narrative structure, hnportantly, however, reported sl)eech in newspaper articles explicitly links these two levels. In this paper, we illustrate what the lexical semantics of reporting verbs must incorporate in order to contribute to the reconstruction of story and context.

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  • The distinction between achievements and accomplishments is known to be an empirically important but subtle one. It is argued here to depend on the atomicity (rather than punctuality) of events, and to be strongly related to incrementality (i.e., to event-object mapping functions). A computational treatment of incrementality and atomicity is discussed in the paper, and a number of related empirical problems considered, notably lexical polysemy in verb - argument relationships.

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  • This paper discusses an implemented program that automatically classifies verbs into those that ~describe only states of the world, such as to know, and those that describe events, such as to look. It works by exploiting the con, straint between the syntactic environments in which a verb can occur and its meaning. The only input is on-line text. This demonstrates an important new technique for the automatic generation of lexical databases.

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  • A compositional account of the semantics of German prefix verbs in HPSG is outlined. We consider only those verbs that are formed by productive synchronic rules. Rules are fully productive if they apply to all base verbs which satisfy a common description. Prefixes can be polysemous and have separate, highly underspecified lexical entries. Adequate bases are determined via selection restrictions.

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  • A robust dictionary of semantic frames is an essential element of natural language understanding systems that use ontologies. However, creating lexical resources that accurately capture semantic representations en masse is a persistent problem. Where the sheer amount of content makes hand creation inefficient, computerized approaches often suffer from over generality and difficulty with sense disambiguation.

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  • We develop a general feature space for automatic classification of verbs into lexical semantic classes. Previous work was limited in scope by the need for manual selection of discriminating features, through a linguistic analysis of the target verb classes (Merlo and Stevenson, 2001). We instead analyze the classification structure at a higher level, using the possible defining characteristics of classes as the basis for our feature space.

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  • Widely accepted resources for semantic parsing, such as PropBank and FrameNet, are not perfect as a semantic role labeling framework. Their semantic roles are not strictly defined; therefore, their meanings and semantic characteristics are unclear. In addition, it is presupposed that a single semantic role is assigned to each syntactic argument. This is not necessarily true when we consider internal structures of verb semantics. We propose a new framework for semantic role annotation which solves these problems by extending the theory of lexical conceptual structure (LCS). ...

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  • We present a novel approach to the automatic acquisition of a Verbnet like classification of French verbs which involves the use (i) of a neural clustering method which associates clusters with features, (ii) of several supervised and unsupervised evaluation metrics and (iii) of various existing syntactic and semantic lexical resources. We evaluate our approach on an established test set and show that it outperforms previous related work with an Fmeasure of 0.70.

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  • We exploit the resources in the Arabic Treebank (ATB) and Arabic Gigaword (AG) to determine the best features for the novel task of automatically creating lexical semantic verb classes for Modern Standard Arabic (MSA). The verbs are classified into groups that share semantic elements of meaning as they exhibit similar syntactic behavior. The results of the clustering experiments are compared with a gold standard set of classes, which is approximated by using the noisy English translations provided in the ATB to create Levin-like classes for MSA.

    pdf8p hongvang_1 16-04-2013 28 1   Download

  • The EM clustering algorithm (Hofmann and Puzicha, 1998) used here is an unsupervised machine learning algorithm that has been applied in many NLP tasks, such as inducing a semantically labeled lexicon and determining lexical choice in machine translation (Rooth et al., 1998), automatic acquisition of verb semantic classes (Schulte im Walde, 2000) and automatic semantic labeling (Gildea and Jurafsky, 2002).

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  • We present experiments aiming at an automatic classification of Spanish verbs into lexical semantic classes. We apply well-known techniques that have been developed for the English language to Spanish, proving that empirical methods can be re-used through languages without substantial changes in the methodology.

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  • The paper describes the application of kMeans, a standard clustering technique, to the task of inducing semantic classes for German verbs. Using probability distributions over verb subcategorisation frames, we obtained an intuitively plausible clustering of 57 verbs into 14 classes. The automatic clustering was evaluated against independently motivated, handconstructed semantic verb classes.

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  • This paper will focus on the semantic representation of verbs in computer systems and its impact on lexical selection problems in machine translation (MT). Two groups of English and Chinese verbs are examined to show that lexical selection must be based on interpretation of the sentence as well as selection restrictions placed on the verb arguments.

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  • This paper aims at providing a comparison of lexicalization patterns of motion verbs with typological and universal principles they embody across two languages: English and Vietnamese. Cognitive semantics classifies motion verbs based on the sort of semantic information which their verb roots characteristically encode, that is, manner, path or shape.

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  • This paper describes automatic techniques for mapping 9611 entries in a database of English verbs to WordNet senses. The verbs were initially grouped into 491 classes based on syntactic features. Mapping these verbs into WordNet senses provides a resource that supports disambiguation in multilingual applications such as machine translation and cross-language information retrieval.

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  • We present a simple linguistically-motivated method for characterizing the semantic relations that hold between two nouns. The approach leverages the vast size of the Web in order to build lexically-specific features. The main idea is to look for verbs, prepositions, and coordinating conjunctions that can help make explicit the hidden relations between the target nouns.

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  • In this paper we present a methodology for extracting subcategorisation frames based on an automatic LFG f-structure annotation algorithm for the Penn-II Treebank. We extract abstract syntactic function-based subcategorisation frames (LFG semantic forms), traditional CFG categorybased subcategorisation frames as well as mixed function/category-based frames, with or without preposition information for obliques and particle information for particle verbs.

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  • Like single-word verbs, each PV has its own lexical features including subcategorization features that determine its structural patterns [Fraser 1976; Bolinger 1971; Pelli 1976; Shaked 1994], e.g., look for has syntactic subcategorization and semantic features similar to those of search; carry…on shares lexical features with continue. Such lexical features can be represented in the PV lexicon in the same way as those for single-word verbs, but a parser can only use them when the PV is identified. Problems like PVs are regarded as ‘a pain in the neck for NLP’ [Sag et al. 2002]. ...

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  • Part V (— sections 1.33 — 1.40) addresses the properties by which these latter semantic catego-ries (which we call 'situation-templates1) can be classified into different types. This section is concerned with 'ontological aspect1 (perhaps better known as 'lexical aspect1), which involves such oppositions as 'static1 versus 'dynamic', 'agentive1 versus 'nonagentive1, 'telic1 versus 'atelic1, 'homogeneous1 versus 'heterogeneous1, etc.

    pdf7p kathy214 21-09-2010 36 2   Download



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