Xem 1-20 trên 342 kết quả Grammatical
  • We present a novel approach to grammatical error correction based on Alternating Structure Optimization. As part of our work, we introduce the NUS Corpus of Learner English (NUCLE), a fully annotated one million words corpus of learner English available for research purposes. We conduct an extensive evaluation for article and preposition errors using various feature sets. Our experiments show that our approach outperforms two baselines trained on non-learner text and learner text, respectively. ...

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  • Invite you to consult the document content, "Focus on grammatical points" below for additional learning materials and study of English grammar, the document gives you multiple choice quiz questions now have answers to help you reinforce the knowledge learned and become familiar with the form of exercises.

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  • We introduce a novel method for grammatical error correction with a number of small corpora. To make the best use of several corpora with different characteristics, we employ a meta-learning with several base classifiers trained on different corpora. This research focuses on a grammatical error correction task for article errors.

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  • We apply machine learning techniques to classify automatically a set of verbs into lexical semantic classes, based on distributional approximations of diatheses, extracted from a very large annotated corpus. Distributions of four grammatical features are sufficient to reduce error rate by 50% over chance. We conclude that corpus data is a usable repository of verb class information, and that corpus-driven extraction of grammatical features is a promising methodology for automatic lexical acquisition. ...

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  • Ambiguity in language translation is due to the presence of words in the source language with multiple non-synonymous target equivalents. A contextual analysis is required whenever a grammatical analysis fails to resolve such ambiguity. In the case of scientific and engineering literature, clues to the context can be obtained from a knowledge of the varying degrees of probability with which words occur in different fields of science.

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  • In this paper, we show that local features computed from the derivations of tree substitution grammars — such as the identify of particular fragments, and a count of large and small fragments — are useful in binary grammatical classification tasks. Such features outperform n-gram features and various model scores by a wide margin. Although they fall short of the performance of the hand-crafted feature set of Charniak and Johnson (2005) developed for parse tree reranking, they do so with an order of magnitude fewer features. ...

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  • Despite the rising interest in developing grammatical error detection systems for non-native speakers of English, progress in the field has been hampered by a lack of informative metrics and an inability to directly compare the performance of systems developed by different researchers. In this paper we address these problems by presenting two evaluation methodologies, both based on a novel use of crowdsourcing.

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  • We present a robust parser which is trained on a treebank of ungrammatical sentences. The treebank is created automatically by modifying Penn treebank sentences so that they contain one or more syntactic errors. We evaluate an existing Penn-treebank-trained parser on the ungrammatical treebank to see how it reacts to noise in the form of grammatical errors. We re-train this parser on the training section of the ungrammatical treebank, leading to an significantly improved performance on the ungrammatical test sets. ...

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  • We introduce a stochastic grammatical channel model for machine translation, that synthesizes several desirable characteristics of both statistical and grammatical machine translation. As with the pure statistical translation model described by Wu (1996) (in which a bracketing transduction grammar models the channel), alternative hypotheses compete probabilistically, exhaustive search of the translation hypothesis space can be performed in polynomial time, and robustness heuristics arise naturally from a language-independent inversiontransduction model. ...

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  • Abstract-like text summarisation requires a means of producing novel summary sentences. In order to improve the grammaticality of the generated sentence, we model a global (sentence) level syntactic structure. We couch statistical sentence generation as a spanning tree problem in order to search for the best dependency tree spanning a set of chosen words. We also introduce a new search algorithm for this task that models argument satisfaction to improve the linguistic validity of the generated tree. ...

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  • We present a web service for natural language parsing, prediction, generation, and translation using grammars in Portable Grammar Format (PGF), the target format of the Grammatical Framework (GF) grammar compiler. The web service implementation is open source, works with any PGF grammar, and with any web server that supports FastCGI. The service exposes a simple interface which makes it possible to use it for interactive natural language web applications. We describe the functionality and interface of the web service, and demonstrate several applications built on top of it. ...

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  • The Constituent Likelihood Automatic Word-tagging System (CLAWS) was originally designed for the low-level grammatical analysis of the million-word LOB Corpus of English text samples. CLAWS does not attempt a full parse, but uses a firat-order Markov model of language to assign word-class labels to words. CLAWS can be modified to detect grammatical errors, essentially by flagging unlikely word-class transitions in the input text.

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  • For languages with (semi-) free word order (such as German), labelling grammatical functions on top of phrase-structural constituent analyses is crucial for making them interpretable. Unfortunately, most statistical classifiers consider only local information for function labelling and fail to capture important restrictions on the distribution of core argument functions such as subject, object etc., namely that there is at most one subject (etc.) per clause.

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  • We propose in this paper a method for quantifying sentence grammaticality. The approach based on Property Grammars, a constraint-based syntactic formalism, makes it possible to evaluate a grammaticality index for any kind of sentence, including ill-formed ones. We compare on a sample of sentences the grammaticality indices obtained from PG formalism and the acceptability judgements measured by means of a psycholinguistic analysis.

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  • In this paper, we propose adding long-term grammatical information in a Whole Sentence Maximun Entropy Language Model (WSME) in order to improve the performance of the model. The grammatical information was added to the WSME model as features and were obtained from a Stochastic Context-Free grammar. Finally, experiments using a part of the Penn Treebank corpus were carried out and significant improvements were acheived.

    pdf8p bunrieu_1 18-04-2013 23 1   Download

  • That are ill-formed with respect to the grammar will be received, both because p e o p l e regularly form ungra=cmatical utterances and because there are a variety of forms that cannot be readily included in current grammatical models and are hence "extra-grammatical". These might be rejected, but as Wilks stresses, "...understanding requires, at the very least, ... some attempt to interpret, rather than merely reject, what seem to be ill-formed utterances." [WIL76] This paper i n v e s t i g a t e s several language phenomena commonly considered ungrammatical....

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  • The problem addressed in this paper is to disambiguate grammatically ambiguous input semences by asking the user. who need not be a computer specialist or a linguist, without showing any parse trees or phrase structure rules. Explanation List Comgarison (ELC) is the technique that implements this process. It is applicable to all parsers which are based on phrase structure grammar, regardless of the parser implementation. An experimental system has been implemented at Carnegie-Mellon University, and it has been applied to English-Japanese machine translation at Kyoto University. ...

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  • Research has been under way at the Unit for Computer Research on the ~hglish Language at the University of Lancaster, England, to develop a suite of computer programs which provide a detailed grammatical analysis of the LOB corpus, a collection of about 1 million words of British English texts available in machine readable form. The first phrase of the pruject, completed in September 1983, produced a grammatically annotated version of the corpus giving a tag showing the word class of each word token. ...

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  • We consider the structural descriptions produced by various grammatical formalisms in ~ of the complexity of the paths and the relationship between paths in the sets of structural descriptions that each system can generate. In considering the relationship between formalisms, we show that it is useful to abstract away from the details of the formalism, and examine the nature of their derivation process as reflected by properties of their deriva:ion trees.

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  • We use the grammatical relations (GRs) described in Carroll et al. (1998) to compare a number of parsing algorithms A first ranking of the parsers is provided by comparing the extracted GRs to a gold standard GR annotation of 500 Susanne sentences: this required an implementation of GR extraction software for Penn Treebank style parsers. In addition, we perform an experiment using the extracted GRs as input to the Lappin and Leass (1994) anaphora resolution algorithm.

    pdf8p bunthai_1 06-05-2013 26 1   Download


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