Results 221 to 230 of about 49,185 (256)

Efficient Computation of Co-occurrence Based Word Relatedness

Proceedings of the 2015 ACM Symposium on Document Engineering, 2015
Measuring document relatedness using unsupervised co-occurrence based word relatedness methods is a processing-time and memory consuming task. This paper introduces the application of compact data structures for efficient computation of word relatedness based on corpus statistics.
Jie Mei 0005, Xinxin Kou, Zhimin Yao, Andrew Rau-Chaplin, Aminul Islam 0001, Abidalrahman Moh'd, Evangelos E. Milios   +6 more
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Word co-occurrence augmented topic model in short text

Intelligent Data Analysis, 2017
The large amount of text on the Internet cause people hard to understand the meaning in a short limit time. Topic models (e.g. LDA and PLSA) have then been proposed to summarize the long text into several topic terms. In the recent years, the short text media such as Twitter is very popular.
Guan-Bin Chen, Hung-Yu Kao
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Visualizing Textbook Concepts: Beyond Word Co-occurrences

2018
In this paper, we present a simple and elegant algorithm to extract and visualize various concept relationships present in sections of a textbook. This can be easily extended to develop visualizations of entire chapters or textbooks, thereby opening up opportunities for developing a range of visual applications for e-learning and education in general ...
Chandramouli Shama Sastry, Darshan Siddesh Jagaluru, Kavi Mahesh   +2 more
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Explorations in the derivation of word co-occurrence statistics

South Pacific Journal of Psychology, 1999
ABSTRACTRecent work has demonstrated that counts of which other words co-occur with a word of interest can reflect interesting properties of that word. We have studied aspects of this kind of methodology by systematically examining the effects of different combinations of parameters used in the preparation of co-occurrence statistics.
Joseph P. Levy, John A. Bullinaria, Malti Patel   +2 more
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Extended Strategies for Document Clustering with Word Co-occurrences

2015
To tackle the sparse data problem of the bag-of-words model for document clustering, recent strategies have been proposed to enrich a document with the relatedness of all the words in a corpus to the document, where the relatedness is estimated by the weighted sum of word co-occurrences. However, the relatedness is overestimated without eliminating the
Yang Wei, Jin-Mao Wei 0001, Zhenglu Yang
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The structure of word co-occurrence network for microblogs

Physica A: Statistical Mechanics and its Applications, 2018
Abstract The study of structure and dynamics of complex networks is seeking attention of academic researchers and practitioners in recent years. Although Word Co-occurrence Networks (WCN) have been studied for different languages, yet there is the need to study the structure of WCN for microblogs due to the presence of ill-formed and unstructured ...
Muskan Garg, Mukesh Kumar
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Exploiting Word and Visual Word Co-occurrence for Sketch-based Clipart Image Retrieval

Proceedings of the 23rd ACM international conference on Multimedia, 2015
As the increasing popularity of touch-screen devices, retrieving images by hand-drawn sketch has become a trend. Human sketch can easily express some complex user intention such as the object shape. However, sketches are sometimes ambiguous due to different drawing styles and inter-class object shape ambiguity.
Ching-Hsuan Liu, Yen-Liang Lin, Wen-Feng Cheng, Winston H. Hsu   +3 more
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Learning Latent Topics From the Word Co-Occurrence Network

Communications in Computer and Information Science, 2017
Topic modeling is widely used to uncover the latent thematic structure in corpora. Based on the separability assumption, the spectral method focuses on the word co-occurrence patterns at the document-level and it includes two steps: anchor selection and topic recovery.
Kun He, John E Hopcroft
exaly   +3 more sources

Valence-arousal ratings prediction with co-occurrence word-embedding

2016 International Conference on Asian Language Processing (IALP), 2016
Dimensional approach has become a popular method in sentiment analysis because it represents emotions as continuous numerical values in multiple dimensions, such as valence-arousal (VA) space [1], therefore it can provide more fine-grained sentiment analysis compared to traditional categorical approach which represents affective states in discrete ...
Yan Gao, Xu Yang, Jing Xu, Bin Xu
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Constraining Weighted Word Co-occurrence Frequencies in Word Embeddings

2021 IEEE International Conference on Big Data (Big Data), 2021
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