Model of sentence processing

Xem 1-20 trên 213 kết quả Model of sentence processing
  • Probabilistic models of sentence comprehension are increasingly relevant to questions concerning human language processing. However, such models are often limited to syntactic factors. This paper introduces a novel sentence processing model that consists of a parser augmented with a probabilistic logic-based model of coreference resolution, which allows us to simulate how context interacts with syntax in a reading task. Our simulations show that a Weakly Interactive cognitive architecture can explain data which had been provided as evidence for the Strongly Interactive hypothesis. ...

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  • This paper describes a computational model of human sentence processing based on the principles and parameters paradigm of current linguistic theory. The syntactic processing model posits four modules, recovering phrase structure, long-distance dependencies, coreference, and thematic structure. These four modules are implemented as recta-interpreters over their relevant components of the grammar, permitting variation in the deductive strategies employed by each module.

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  • Tham khảo bài viết 'báo cáo khoa học: "modeling human sentence processing data with a statistical parts-of-speech tagger"', luận văn - báo cáo phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả

    pdf6p hongvang_1 16-04-2013 65 2   Download

  • The paper describes GEMS, a system for Generating and Expressing the Meaning of Sentences, focussing on the generation task, i.e. how GEMS extracts a set of propositional units from a knowledge store that can be expressed with a well-formed sentence in a target language. GEMS is lexically distributed. After a central processor has selected the first unit(s) from the knowledge store and activated the corresponding lexical entry, the further construction of the sentences meaning is entrusted to the entries in the vocabulary.

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  • A major focus of current work in distributional models of semantics is to construct phrase representations compositionally from word representations. However, the syntactic contexts which are modelled are usually severely limited, a fact which is reflected in the lexical-level WSD-like evaluation methods used. In this paper, we broaden the scope of these models to build sentence-level representations, and argue that phrase representations are best evaluated in terms of the inference decisions that they support, invariant to the particular syntactic constructions used to guide composition.

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  • It has previously been assumed in the psycholinguistic literature that finite-state models of language are crucially limited in their explanatory power by the locality of the probability distribution and the narrow scope of information used by the model. We show that a simple computational model (a bigram part-of-speech tagger based on the design used by Corley and Crocker (2000)) makes correct predictions on processing difficulty observed in a wide range of empirical sentence processing data. ...

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  • The limited capacity of working memory is intrinsic to human sentence processing, and therefore must be addressed by any theory of human sentence processing. This paper gives a theory of garden-path effects and processing overload that is based on simple assumptions about human short term memory capacity. hypothesis, is easily compatible with the above view of processing load calculation: given a choice between two different representations for the same input string, simply choose the representation that is associated with the lower processing load. ...

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  • AMBER is a model of first language acquisition that improves its performance through a process of error recovery. The model is implemented as an adaptive production system that introduces new condition-action rules on the basis of experience. AMBER starts with the ability to say only one word at a time, but adds rules for ordering goals and producing grammatical morphemes, based on comparisons between predicted and observed sentences.

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  • In some computer applications of linguistics (such as maximum-likelihood decoding of speech or handwriting), the purpose of the language-handling component (Language Model) is to estimate the linguistic (a priori) probability of arbitrary natural-language sentences.

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  • Informal and formal (“T/V”) address in dialogue is not distinguished overtly in modern English, e.g. by pronoun choice like in many other languages such as French (“tu”/“vous”). Our study investigates the status of the T/V distinction in English literary texts. Our main findings are: (a) human raters can label monolingual English utterances as T or V fairly well, given sufficient context; (b), a bilingual corpus can be exploited to induce a supervised classifier for T/V without human annotation.

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  • First of all, the criteria are described that are used to identify the elementary units of undering structure and the operations conoining them into complex units (Sect.l), t h e n t h e m a i n t y p e s o f ~ n ~ t s and o p e r a t i o n s resulting from an empirical investigation on t h e b a s i s o f t h e c r i t e r i a are registered ( S e c t . 2 ) ,...

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  • We present the psycholinguistically motivated task of predicting human plausibility judgements for verb-role-argument triples and introduce a probabilistic model that solves it. We also evaluate our model on the related role-labelling task, and compare it with a standard role labeller. For both tasks, our model benefits from classbased smoothing, which allows it to make correct argument-specific predictions despite a severe sparse data problem. The standard labeller suffers from sparse data and a strong reliance on syntactic cues, especially in the prediction task. ...

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  • The purpose of this paper is to present LX-Suite, a set of tools for the shallow processing of Portuguese, developed under the TagShare1 project by the NLX Group.2 The tools included in this suite are a sentence chunker; a tokenizer; a POS tagger; a nominal featurizer; a nominal lemmatizer; and a verbal featurizer and lemmatizer. These tools were implemented as autonomous modules. This option allows to easily replace any of the modules by an updated version or even by a third-party tool. It also allows to use any of these tools separately, outside the pipeline of the suite. ...

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  • This paper presents a new, exemplar-based model of thematic fit. In contrast to previous models, it does not approximate thematic fit as argument plausibility or ‘fit with verb selectional preferences’, but directly as semantic role plausibility for a verb-argument pair, through similaritybased generalization from previously seen verb-argument pairs. This makes the model very robust for data sparsity. We argue that the model is easily extensible to a model of semantic role ambiguity resolution during online sentence comprehension. ...

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  • We describe a computational system which parses discourses consisting of sequences of simple sentences. These contain a range of temporal constructions, including time adverbials, progressive aspect and various aspectual classes. In particular, the grammar generates the required readings, according to the theoretical analysis of (Glasbey, forthcoming), for sentence-final 'then'.

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  • Sentence Similarity is the process of computing a similarity score between two sentences. Previous sentence similarity work finds that latent semantics approaches to the problem do not perform well due to insufficient information in single sentences. In this paper, we show that by carefully handling words that are not in the sentences (missing words), we can train a reliable latent variable model on sentences.

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  • This paper describes a method of interactively visualizing and directing the process of translating a sentence. The method allows a user to explore a model of syntax-based statistical machine translation (MT), to understand the model’s strengths and weaknesses, and to compare it to other MT systems. Using this visualization method, we can find and address conceptual and practical problems in an MT system. In our demonstration at ACL, new users of our tool will drive a syntaxbased decoder for themselves. ...

    pdf4p bunbo_1 17-04-2013 35 2   Download

  • During thc past two decades, much work in linguistics has focused on sentences as minimal units of communication, and the project of rigorously characterizing the structure of sentences in natural language has met with some succcss. Not surprisingly, however, sentcnce grammars have contributed little to the analysis of discourse, Human discourse consists not just of words in sequences, hut of words in sequences directed by a speaker to an addressee, used to represent situations and to reveal intentions.

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  • We describe the adaptation to French of a machine-learned sentence realization system called Amalgam that was originally developed to be as language independent as possible and was first implemented for German. We discuss the development of the French implementation with particular attention to the degree to which the original system could be reused, and we present the results of a human evaluation of the quality of sentence realization using the new French system.

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  • We present a simple and effective method for extracting parallel sentences from comparable corpora. We employ a statistical machine translation (SMT) system built from small amounts of parallel texts to translate the source side of the nonparallel corpus. The target side texts are used, along with other corpora, in the language model of this SMT system. We then use information retrieval techniques and simple filters to create French/English parallel data from a comparable news corpora.

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