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科学通报 , 1950,
Abstract: 中國科學院社會研究所是作爲前中央研究院重要組成部分之一,由前中央研究院社會科學研穷所和北平社會調查所合併而成。原名社會科學研究所,自一九四五年超改稱社會研究所。中國科學院接管後,仍用今名。
解放一年來的社會研究所  [PDF]
科学通报 , 1950,
Abstract: 中國科學院社會研究所是作爲前中央研究院重要組成部分之一,由前中央研究院社會科學研穷所和北平社會調查所合併而成。原名社會科學研究所,自一九四五年超改稱社會研究所。中國科學院接管後,仍用今名。
Homotopy invariants of Gauss phrases  [PDF]
Andrew Gibson
Mathematics , 2008,
Abstract: Equivalence relations can be defined on Gauss phrases using combinatorial moves. In this paper we consider two closely related equivalence relations on Gauss phrases, homotopy and open homotopy. In particular, in each case, we define a new invariant and determine the values that it can attain.
Identifying Keywords and Key Phrases  [PDF]
Ashwini Madane
International Journal of Soft Computing & Engineering , 2012,
Abstract: Keywords and key phrases are widely used in large document collections. They describe the content of single documents and provide a kind of semantic metadata that is useful for a variety of purposes. Text mining is powerful tool to find useful and needed information from huge data set. For context based text mining, key phrases are used. Key phrases provide brief summary about the contents of documents. In document clustering, number of total cluster is not known in advance. In K-means, if prespecified number of clusters modified, the precision of each result is also modified. Therefore Kea, is algorithm for automatically extracting key phrases from text is used. In this kea algorithm, number of clusters is automatically determined by using extracted key phrases. Keameans clustering algorithm provide easy and efficient way to extract test document from large quantity of resources. Key phrase play important role in text indexing, summarization and categorization. Key phrases are selected manually. Assigning key phrases manually is tedious process that requires knowledge of subject. Therefore automatic extraction techniques are most useful.
Coalgebras of words and phrases  [PDF]
Vladimir Turaev
Mathematics , 2004,
Abstract: We introduce two constructions of a coassociative comultiplication in the algebra of phrases in a given alphabet. As a preliminary step we give two constructions of a pre-Lie comultiplication in the module generated by words.
Routinizing Lexical Phrases on Spoken Discourse  [cached]
Nazira Binti Osman,Kamaruzaman Jusoff
International Education Studies , 2009, DOI: 10.5539/ies.v2n2p188
Abstract: This paper examines the effectiveness of routinizing lexical phrases to a group of second language learners. A group of proficiency class students were drilled or routinized with semi-fixed and fixed phrases which are commonly used in problem-solving group discussion. Basic frequency counts and interview were carried out to see improvement in learners’ communicative ability and how the lexical phrases benefit them. The learners can use a number of phrases appropriately in several group discussions. Thus, the practice of routinizing lexical phrases which is based on the lexical approach can be applied in second language learning.
The Morphosyntactic Interface of Determiner Phrases  [PDF]
Gabrielle Klassen, John W. Schwieter
Open Journal of Modern Linguistics (OJML) , 2013, DOI: 10.4236/ojml.2013.34047
Abstract: The functional category of determiners has undergone a number of representational changes in the last half century. Beginning with Abney in 1987 and as early as work by Brame (1981, 1982) and Postal (1966), linguists began to adapt the notion that determiners were a type of functional category with phrasal structure, and not specifiers of noun phrases. The flexibility allotted to this category to hold a significant role in syntactic structure has led to theories of feature and feature strength and the development of these features in first and second language acquisition. This paper seeks to review the current theories of syntactic structure of determiner phrases in English and universally. In particular, it examines one area of controversy regarding this category, namely nominal gender agreement, and how this affects applied areas of linguistics. Recent studies seem to favor specific transfer theories, however the default hypothesis that arises leaves much to be considered. From the discussion, we argue that gender feature agreement in L1 and L2 acquisition is distinct and merits further investigation, perhaps benefiting from the recent developments in the area of psycholinguistics.
An Estimate of Referent of Noun Phrases in Japanese Sentences  [PDF]
M. Murata,M. Nagao
Computer Science , 1999,
Abstract: In machine translation and man-machine dialogue, it is important to clarify referents of noun phrases. We present a method for determining the referents of noun phrases in Japanese sentences by using the referential properties, modifiers, and possessors of noun phrases. Since the Japanese language has no articles, it is difficult to decide whether a noun phrase has an antecedent or not. We had previously estimated the referential properties of noun phrases that correspond to articles by using clue words in the sentences. By using these referential properties, our system determined the referents of noun phrases in Japanese sentences. Furthermore we used the modifiers and possessors of noun phrases in determining the referents of noun phrases. As a result, on training sentences we obtained a precision rate of 82% and a recall rate of 85% in the determination of the referents of noun phrases that have antecedents. On test sentences, we obtained a precision rate of 79% and a recall rate of 77%.
刑事审判话语之标记语探究  [PDF]
湖南工业大学学报(社会科学版) , 2009,
Abstract: 刑事审判话语中的话语标记语主要分为话语来源标记语、换言标记语、话题控制型标记语、言说方式标记语、递进标记语、缓和标记语、话轮控制型标记语等类别。这些话语标记语在刑事审判话语中起着实现话语目的, 显示社会地位, 强化机构权力和限定话语内容的作用。
Distributed Representations of Words and Phrases and their Compositionality  [PDF]
Tomas Mikolov,Ilya Sutskever,Kai Chen,Greg Corrado,Jeffrey Dean
Computer Science , 2013,
Abstract: The recently introduced continuous Skip-gram model is an efficient method for learning high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships. In this paper we present several extensions that improve both the quality of the vectors and the training speed. By subsampling of the frequent words we obtain significant speedup and also learn more regular word representations. We also describe a simple alternative to the hierarchical softmax called negative sampling. An inherent limitation of word representations is their indifference to word order and their inability to represent idiomatic phrases. For example, the meanings of "Canada" and "Air" cannot be easily combined to obtain "Air Canada". Motivated by this example, we present a simple method for finding phrases in text, and show that learning good vector representations for millions of phrases is possible.
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