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Search Results: 1 - 10 of 39794 matches for " Word Vector Model "
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A Comparative Study to Understanding about Poetics Based on Natural Language Processing  [PDF]
Lingyi Zhang, Junhui Gao
Open Journal of Modern Linguistics (OJML) , 2017, DOI: 10.4236/ojml.2017.75017
Abstract: This paper tries to find out five poets’ (Thomas Hardy, Wilde, Browning, Yeats, and Tagore) differences and similarities through analyzing their works on nineteenth Century by using natural language understanding technology and word vector model. Firstly, we collect enough poems from these five poets, build five corpus respectively, and calculate their high-frequency words, by using Natural Language Processing method. Then, based on the word vector model, we calculate the word vectors of the five poets’ high-frequency words, and combine the word vectors of each poet into one vector. Finally, we analyze the similarity between the combined word vectors by using the hierarchical clustering method. The result shows that the poems of Hardy, Browning, and Wilde are similar; the poems of Tagore and Yeats are relatively close—but the gap between the two is relatively large. In addition, we evaluate the stability of our approach by altering the word vector dimension, and try to analyze the results of clustering in a literary (poetic) perspective. Yeats and Tagore possessed a kind of mysticism poetics thought, while Hardy, Browning, and Wilde have the elements of realism combined with tragedy and comedy. The results are similar comparing to those we get from the word vector model.
A Technique to Choose the Proper Vector Space Models of Semantics in Case of Automatic Text Categorization
Sukanya Ray,Nidhi Chandra
International Journal of Modern Education and Computer Science , 2012,
Abstract: vides a proper solution to this limitation. There are broadly three main categories of Vector Space Model: term-document, word-content and pair-pattern matrices. The main aim of this paper is to discuss broadly the three main categories of VSM for semantic analysis of texts and make proper selection for automatic categorizing. The scenario taken up here is categorization of research papers for organizing a national or an international conference based on the proposed methodology. Computers do not understand human language and this makes it difficult when human wants the computer to do some specific task like categorization according to human need. Vector Space Model (VSM) for semantic analysis of texts and make proper selection of one of the three main categories for automatic categorizing of research papers for organizing a national or an international conference based on the proposed methodology.
基于领域知识和词向量的词义消歧方法
Word Sense Disambiguation Based on Domain Knowledge and Word Vector Model

杨安,李素建,李芸
- , 2017, DOI: 10.13209/j.0479-8023.2017.027
Abstract: 摘要 利用无标注文本构建词向量模型, 结合特定领域的关键词信息, 提出一种词义消歧方法。以环境领域的待消歧文本作为评测语料, 通过与Lesk等其他消歧方法进行比较, 证明了所提方法的有效性。通过引入不同的领域知识, 证明该方法亦可在其他领域的文本消歧任务中加以应用。
Abstract A WSD method is presented, using domain keywords and word vector model built from unlabelled data. The effectiveness of the proposed approach is proved, compared with other WSD methods including Lesk on evaluation corpus in environmental domain. Through employing knowledge from different fields, proposed method can be adapted into the WSD task of other domains.
Research on Intelligent Search Engine Based on Semantic Comprehension
基于语义理解的智能搜索引擎研究

CHEN Lin,YANG Dan,ZHAO Jun-qin,
陈林
,杨丹,赵俊芹

计算机科学 , 2008,
Abstract: This article proposes a search engine model which is based on the natural language understanding. It includes a method to analyze users' quest ions in natural language from three layers, that is, keyword, quest ion type and question focus. The analysis consists of semantic analysis, feature extraction and semantic matching. And with this thought the feature base that faces to Web page content is built. In addition, this article proposes an algorithm of returning to the documents arrangement, it investigates...
An Unsuptervised Approach to Word Sense Disambiguation Based on Sense-Words in Vector Space Model
基于向量空间模型中义项词语的无导词义消歧

LU Song,BAI Shuo,HUANG Xiong,
鲁松
,白硕,黄雄

软件学报 , 2002,
Abstract: 有导词义消歧机器学习方法的引入虽然使词义消歧取得了长足的进步,但由于需要大量人力进行词义标注,使其难以适用于大规模词义消歧任务.针对这一问题,提出了一种避免人工词义标注巨大工作量的无导学习方法.在仅需义项词语知识库的支持下,将待消歧多义词与义项词语映射到向量空间中,基于k-NN(k=1)方法,计算二者相似度来实现词义消歧任务.在对10个典型多义词进行词义消歧的测试实验中,采用该方法取得了平均正确率为83.13%的消歧结果.
Study on Text Classification Model Based on SUMO and WordNet Ontology Integration
基于SUMO和WordNet本体集成的文本分类模型研究

Hu Zewen Wang Xiaoyue Bai Rujiang,
胡泽文
,王效岳,白如江

现代图书情报技术 , 2011,
Abstract: Aiming at the existing problems in the traditional text classification methods and the current semantic classification methods, a new text classification model based on SUMO and WordNet Ontology integration is proposed. This model utilizes the mapping relations between WordNet synsets and SUMO Ontology concepts to map terms in document-words vector space into the corresponding concepts in Ontology, and forms document-concepts vector space to classify texts automatically. The experiment results show that the proposed method can greatly decrease the dimensionality of vector space and improve the text classification performance.
New Word Vector Representation for Semantic Clustering Une nouvelle représentation vectorielle pour la classi cation sémantique
Salma Jamoussi
Traitement Automatique des Langues , 2010,
Abstract: The idea we defend in this paper is the possibility to obtain signi cant semantic concepts using clustering methods. We start by de ning some semantic measures to quantify the semantic relations between words. Then, we use some clustering methods to build up concepts in an automatic way. We test two well known methods: the K-means algorithm and the Ko- honen maps. Then, we propose the use of a Bayesian network conceived for clustering and called AutoClass. To group the words of the vocabulary in various classes, we test three vector representations of words. The rst is a simple contextual representation. The second associates to each word a vector which represents its similarity with each word of the vocabulary. The third representation is a combination of the rst and the second one.
Falcon: A Novel Chinese Short Text Classification Method  [PDF]
Haiming Li, Haining Huang, Xiang Cao, Jingu Qian
Journal of Computer and Communications (JCC) , 2018, DOI: 10.4236/jcc.2018.611021
Abstract: For natural language processing problems, the short text classification is still a research hot topic, with obviously problem in the features sparse, high-dimensional text data and feature representation. In order to express text directly, a simple but new variation which employs one-hot with low-dimension was proposed. In this paper, a Densenet-based model was proposed to short text classification. Furthermore, the feature diversity and reuse were implemented by the concat and average shuffle operation between Resnet and Densenet for enlarging short text feature selection. Finally, some benchmarks were introduced to evaluate the Falcon. From our experimental results, the Falcon method obtained significant improvements in the state-of-art models on most of them in all respects, especially in the first experiment of error rate. To sum up, the Falcon is an efficient and economical model, whilst requiring less computation to achieve high performance.
Construction of Sensory Transfer Model of Gustatory and Olfactory-Synaesthetic Metaphor (GO-STM) and English-Chinese Comparative Study  [PDF]
Hong Duan, Li Gao
Open Journal of Modern Linguistics (OJML) , 2014, DOI: 10.4236/ojml.2014.42023
Abstract:

This paper aims to explore the synaesthetic truth of taste and smell in particular. Based on the corpus of gustatory and olfactory-synaesthetic cases, classification and statistical tasks are undertaken, in which four main characters are discovered: 1) the particular sorts of gustatory and olfactory-synaesthetic metaphors; 2) the bi-directional transfer in specific pairs; 3) the hierarchical distribution among sensory modes; 4) the transfer frequencies of sensory transfer tendencies. Thus, GO-STM of both English and Chinese is constructed. What’s more, the comparison of English and Chinese has been conducted for the first time. According to the statistic results, the percentage of English-Chinese gustatory and olfactory-synaesthetic dead metaphors is 48.1% and 48.3% respectively, and that’s why we always ignore them. Furthermore, the embodiment basis of synaesthetic metaphors is analyzed in light of recent neurological research and Cognitive Linguistics.

Word Formation in German Linguistics: Theoretical and Methodological Analysis  [PDF]
Maharramova Malahat Abdurrahman Gizi
Open Journal of Modern Linguistics (OJML) , 2018, DOI: 10.4236/ojml.2018.85015
Abstract: The paper defines the ways and peculiarities of word formation in modern German language. The paper deals with one of the ways of enriching the verb vocabulary in the modern German language, in particular wordbuilding. In the result of the analysis of the language material, the most productive models and means of the word-building of the verbs are emphasized including word-building models borrowed from other languages. The vocabulary of the language, being a system, is in constant motion. The functioning of language is associated with the disappearance of certain words, with the emergence of new ones, with the change in the meaning or stylistic status of words. Each of the ways of developing the vocabulary of the German language has its own characteristics. The paper draws attention to these features. The paper describes verbal neoplasms not registered in dictionaries until the middle of the 20th century, selected from the texts of the German newspapers “Süddeutsche Zeitung”, “Frankfurter Allgemeine”, “Der Spiegel”, “Joe”, “Alles für die Frau”, “GEO” and others. The study considers the linguistic material of some dictionaries published in the second half of the 20th and the beginning of the 21st centuries, including dictionaries of colloquial youth vocabulary. So, the main idea of this article is to highlight the peculiarities of word formation in German linguistics through the history.
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