Models of language acquisition

Xem 1-20 trên 56 kết quả Models of language acquisition
  • An investment of effort over the last two years has begun to produce a wealth of data concerning computational psycholinguistic models of syntax acquisition. The data is generated by running simulations on a recently completed database of word order patterns from over 3,000 abstract languages. This article presents the design of the database which contains sentence patterns, grammars and derivations that can be used to test acquisition models from widely divergent paradigms.

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  • In this paper we discuss our approach toward establishing a model of the acquisition of English grammatical structures by users of our English language tutoring system, which has been designed for deaf users of American Sign Language. We explore the correlation between a corpus of error-tagged texts and their holistic proficiency scores assigned by experts in order to draw initial conclusions about what language errors typically occur at different levels of proficiency in this population.

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  • This paper argues that developmental patterns in child language be taken seriously in computational models of language acquisition, and proposes a formal theory that meets this criterion. We first present developmental facts that are problematic for statistical learning approaches which assume no prior knowledge of grammar, and for traditional learnability models which assume the learner moves from one UG-defined grammar to another. In contrast, we view language acquisition as a population of grammars associated with "weights", that compete in a Darwinian selectionist process. ...

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  • This paper reports the on-going research of a thesis project investigating a computational model of early language acquisition. The model discovers word-like units from crossmodal input data and builds continuously evolving internal representations within a cognitive model of memory. Current cognitive theories suggest that young infants employ general statistical mechanisms that exploit the statistical regularities within their environment to acquire language skills.

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  • At least since Chomsky, the usual response to the projection problem has been to characterize knowledge of language as a grammar, and then proceed by restricting so severely the class of grammars available for acquisition that the induction task is greatly simplified - perhaps trivialized. The work reported here describes an implemented LISP program that explicitly reproduces this methodological approach to acquisitio,~ - but in a computational setting.

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  • The nature and amount of information needed for learning a natural language, and the underlying mechanisms involved in this process, are the subject of much debate: is it possible to learn a language from usage data only, or some sort of innate knowledge and/or bias is needed to boost the process? This is a topic of interest to (psycho)linguists who study human language acquisition, as well as computational linguists who develop the knowledge sources necessary for largescale natural language processing systems. ...

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  • Theories of second language acquisition có nội dung trình bày về: An innatist model: Krashen’s input hypothesis, Two cognitive models, A social constructivist model: Long’s interaction hypothesi. Mời các bạn cùng tham khảo bài giảng.

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  • During early language acquisition, infants must learn both a lexicon and a model of phonetics that explains how lexical items can vary in pronunciation—for instance “the” might be realized as [Di] or [D@]. Previous models of acquisition have generally tackled these problems in isolation, yet behavioral evidence suggests infants acquire lexical and phonetic knowledge simultaneously.

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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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  • While computational estimation of difficulty of words in the lexicon is useful in many educational and assessment applications, the concept of scalar word difficulty and current corpus-based methods for its estimation are inadequate. We propose a new paradigm called word meaning maturity which tracks the degree of knowledge of each word at different stages of language learning. We present a computational algorithm for estimating word maturity, based on modeling language acquisition with Latent Semantic Analysis. ...

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  • This paper presents an incremental probabilistic learner that models the acquistion of syntax and semantics from a corpus of child-directed utterances paired with possible representations of their meanings. These meaning representations approximate the contextual input available to the child; they do not specify the meanings of individual words or syntactic derivations. The learner then has to infer the meanings and syntactic properties of the words in the input along with a parsing model.

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  • A new account of parameter setting during grammatical acquisition is presented in terms of Generalized Categorial G r a m m a r embedded in a default inheritance hierarchy, providing a natural partial ordering on the setting of parameters. Experiments show that several experimentally effective learners can be defined in this framework. Ew)lutionary simulations suggest that a lea.rner with default initial settings for parameters will emerge, provided that learning is memory limited and the environment of linguistic adaptation contains an appropriate language. ...

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  • This paper describes a modular connectionist model of the acquisition of receptive inflectional morphology. The model takes inputs in the form of phones one at a time and outputs the associated roots and inflections. Simulations using artificial language stimuli demonstrate the capacity of the model to learn suffixation, prefixation, infixation, circumfixation, mutation, template, and deletion rules.

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  • This paper describes a computational model of concept acquisition for natural language. W e develop a theory of lexical semantics, the Eztended Aspect Calculus, which together with a ~maxkedness theory" for thematic relations, constrains what a possible word meaning can be. This is based on the supposition that predicates from the perceptual domain axe the primitives for more abstract relations. W e then describe an implementation of this model, TULLY, which mirrors the stages of lexical acquisition for children. ...

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  • 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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  • Having received great interest, there have been a number of attempts in defining and classifying OCF up to date. Different models of classification have been proposed with different aspects of OCF taken into consideration, namely implicitness/explicitness and input-providing and output-prompting. This paper aims to review a body of literature on OCF classification models in the field of SLA.

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  • We describe the early stage of our methodology of knowledge acquisition from technical texts. First, a partial morpho-syntactic analysis is performed to extract "candidate terms". Then, the knowledge engineer, assisted by an automatic clustering tool, builds the "conceptual fields" of the domain. We focus on this conceptual analysis stage, describe the data prepared from the results of the morpho-syntactic analysis and show the results of the clustering module and their interpretation.

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  • This paper demonstrates a novel distributed architecture to facilitate the acquisition of Language Resources. We build a factory that automates the stages involved in the acquisition, production, updating and maintenance of these resources. The factory is designed as a platform where functionalities are deployed as web services, which can be combined in complex acquisition chains using workflows. We show a case study, which acquires a Translation Memory for a given pair of languages and a domain using web services for crawling, sentence alignment and conversion to TMX. ...

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  • This paper uses an unsupervised model of grounded language acquisition to study the role that social cues play in language acquisition. The input to the model consists of (orthographically transcribed) child-directed utterances accompanied by the set of objects present in the non-linguistic context. Each object is annotated by social cues, indicating e.g., whether the caregiver is looking at or touching the object.

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  • Language modeling is to associate a sequence of words with a priori probability, which is a key part of many natural language applications such as speech recognition and statistical machine translation. In this paper, we present a language modeling based on a kind of simple dependency grammar. The grammar consists of head-dependent relations between words and can be learned automatically from a raw corpus using the reestimation algorithm which is also introduced in this paper. Our experiments show that the proposed model performs better than n-gram models at 11% to 11.

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