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Automatic Speech Recognition Texts Clustering [chapter]

Svetlana Popova, Ivan Khodyrev, Irina Ponomareva, Tatiana Krivosheeva
2014 Lecture Notes in Computer Science  
We present a comparative analysis of clustering results for recognition texts and manual text transcripts, make an evaluation of how recognition quality affects clustering and explore approaches to increasing  ...  clustering quality by using stop words and Latent Semantic Indexing (LSI).  ...  Table 1 contains a description of both datasets. Each text in the dataset is the recognition result for one short phone call, which is a text of small length.  ... 
doi:10.1007/978-3-319-10816-2_59 fatcat:bn6rvq4xmnctvldybre3w47zki

A Hotspot Discovery Method Based on Improved FIHC Clustering Algorithm

2021 Tehnički Vjesnik  
Compared with the other text clustering algorithms and hotspot detection methods, the method has good effect, and can be a more comprehensive response to the current hot topics.  ...  Then the initial cluster of the text repletion of mircoblog was reduced, and the idea of Single-Pass clustering was used to the reduced topic cluster in order to get the Hotspot.  ...  terms are better suited for short text clustering on microblogs and can get results faster.  ... 
doi:10.17559/tv-20210610120531 fatcat:aztw4tpuejbpde5ypxv35jrfoy

Agent for Documents Clustering using Semantic-based Model and Fuzzy

Khaled M.Fouad, Moataz O. Hassan
2013 International Journal of Computer Applications  
Text clustering plays an important role in providing intuitive navigation and browsing mechanisms by organizing large sets of documents into a small number of meaningful clusters.  ...  The bag of words representation method used for these clustering is often unsatisfactory because it ignores the semantic of words.  ...  They also showed that it is possible to select a subset of the semantic features that are useful for clustering.  ... 
doi:10.5120/10059-4651 fatcat:wz5juvgx2nb6rnsuh7strzxa2u

Review On Abstractive Text Summarization Techniques For Biomedical Domain

Patel Krutika, Desai Urmi
2018 Zenodo  
Abstractive text summarization can solve this problem by representing the extracted sentences into another understandable semantic form.  ...  The task of analyzing huge amount of biomedical data and association of biological data is much difficult. To efficiently analyze the biomedical domain data text summarization approach is used.  ...  For making cluster used Hierarchical Agglomerative Clustering (HAC) algorithm [15] . HAC algorithm accepts the semantic similarity matrix as input.  ... 
doi:10.5281/zenodo.1252402 fatcat:6khvqb535jamdnk3abc4jra3ui

An efficient Particle Swarm Optimization approach to cluster short texts

Leticia Cagnina, Marcelo Errecalde, Diego Ingaramo, Paolo Rosso
2014 Information Sciences  
In this context, the clustering of short texts is a significant analysis task and a discrete Particle Swarm Optimization (PSO) algorithm named CLUDIPSO has recently shown a promising performance in this  ...  Experimental results with corpora containing scientific abstracts, news and short legal documents obtained from the Web, show that CLUDIPSO ⋆ is an effective clustering method for short-text corpora of  ...  Then, the difficulties of document clustering in general and short-text clustering in particular are analyzed considering the limitations of common methods to reflect real semantics.  ... 
doi:10.1016/j.ins.2013.12.010 fatcat:ghpkmvuujjafbdhfu25mvmi4a4

Covid-Transformer: Detecting COVID-19 Trending Topics on Twitter Using Universal Sentence Encoder [article]

Meysam Asgari-Chenaghlu, Narjes Nikzad-Khasmakhi, Shervin Minaee
2020 arXiv   pre-print
After that, the cluster summary is obtained using a text summarization algorithm based on deep learning, which can uncover the underlying topics of each cluster.  ...  We then used the sentence similarity and their embeddings, and feed them to K-means clustering algorithm to group similar tweets (in semantic sense).  ...  Then these embeddings are fed into a clustering algorithm to group similar Tweets, and finally text summarization is applied to all sentences of a each cluster to provde a short summary of that.  ... 
arXiv:2009.03947v3 fatcat:vcsrfx7x2jcipgcej2ylsfzj3a

A Hybrid Approach using Ontology Similarity and Fuzzy Logic for Semantic Question Answering [article]

Monika Rani, Maybin K. Muyeba, O. P. Vyas
2017 arXiv   pre-print
We use a Fuzzy Co-clustering algorithm to retrieve the collection of documents based on Ontology Similarity.  ...  In this paper, our objective is to present a hybrid approach for a Semantic question answering retrieval system using Ontology Similarity and Fuzzy logic.  ...  A fuzzy scale is used to prioritize the answers retrieved by matrix using Fuzzy co-clustering. For Fuzzy co-clustering, the FCC_STF algorithm is preferred to FCCM and Fuzzy codok.  ... 
arXiv:1709.09214v2 fatcat:w3snfi2m3zg53lr2mwqxuayece

The Construction and Trend of Feminist Literature Theory Based on Social Media Data Mining

Lanlan Cai, Xuhong Xu, Naeem Jan
2022 Mathematical Problems in Engineering  
Based on social media data mining, this study uses the Word2vec model to map the text content to a more abstract word vector space, improves the original Text Rank algorithm from three aspects, semantic  ...  The research in this study provides a reference for the analysis of users' interests and behaviors and has certain theoretical significance and application value.  ...  Overlapping community discovery algorithms generally have high time complexity. is section will focus on analyzing the implementation process of LFM overlapping community discovery algorithm, make three  ... 
doi:10.1155/2022/5791338 fatcat:4qaw7fgddjabfjrvd25dnv5i2a

Visualizing Streaming Text Data with Dynamic Graphs and Maps [chapter]

Emden R. Gansner, Yifan Hu, Stephen North
2013 Lecture Notes in Computer Science  
In this paper, we describe a methodology for visualizing text streams in real-time modeled as a dynamic graph and its derived map.  ...  The many endless rivers of text now available present a serious challenge in the task of gleaning, analyzing and discovering useful information.  ...  Second, for certain topics, when we perform a periodic packing refresh, the map may change significantly. It would be good to avoid this discontinuity, either algorithmically or visually.  ... 
doi:10.1007/978-3-642-36763-2_39 fatcat:p24bje5dkzb3lom6fj2kofr7a4

Clustering of semantically enriched short texts

Marek Kozlowski, Henryk Rybinski
2018 Journal of Intelligent Information Systems  
In addition, we test the possibilities of improving the quality of clustering ultra-short texts by means of enriching them semantically.  ...  The paper is devoted to the issue of clustering small sets of very short texts.  ...  Acknowledgments We would like to thank three anonymous referees for their valuable and constructive comments, which helped us to improve the quality of this article.  ... 
doi:10.1007/s10844-018-0541-4 fatcat:eipabygtdrdr3ji7wqth6vic4a

Exploiting internal and external semantics for the clustering of short texts using world knowledge

Xia Hu, Nan Sun, Chao Zhang, Tat-Seng Chua
2009 Proceeding of the 18th ACM conference on Information and knowledge management - CIKM '09  
In this paper, we propose a novel framework to improve the performance of short text clustering by exploiting the internal semantics from the original text and external concepts from world knowledge.  ...  Clustering of short texts, such as snippets, presents great challenges in existing aggregated search techniques due to the problem of data sparseness and the complex semantics of natural language.  ...  In this paper, we present a novel framework to improve the clustering of short texts by incorporating both the rich internal and external semantics.  ... 
doi:10.1145/1645953.1646071 dblp:conf/cikm/HuSZC09 fatcat:je6zmajetbbg7or72m2poajlcu

A Survey on Similarity Measures in Text Mining

Vijaymeena M.K, Kavitha K
2016 Machine Learning and Applications An International Journal  
The similarity measure process in text mining can be used to identify the suitable clustering algorithm for a specific problem.  ...  Different Clustering algorithms require a metric for quantifying how dissimilar two given documents are.  ...  The user of the algorithm could drop out the low entropy columns in the matrix. In the beginning of the window, a focus word is placed if the text is analyzed.  ... 
doi:10.5121/mlaij.2016.3103 fatcat:ggqyuuxdqfdkjn3zjw2h46kiuq

Circular context-based semantic matching to identify web service composition

Aviv Segev
2008 Proceedings of the 2008 international workshop on Context enabled source and service selection, integration and adaptation organized with the 17th International World Wide Web Conference (WWW 2008) - CSSSIA '08  
Motivation for the work is displayed with examples from Web services in the field of business.  ...  A common method of context extraction is used to compare two types of service description, textual and WSDL.  ...  For each Web service the repository provided a WSDL document and a short textual description.  ... 
doi:10.1145/1361482.1361489 dblp:conf/www/Segev08 fatcat:zkgrhmnlo5aebnxajx4gnxs6fu

Intent Discovery Through Unsupervised Semantic Text Clustering

A Padmasundari, Srinivas Bangalore
2018 Interspeech 2018  
We explore a range of representations for the texts and various clustering methods to validate the clustering stability through quantitative metrics like Adjusted Random Index (ARI).  ...  A final alignment of the clusters to the semantic intent is determined through consensus labelling.  ...  With the right choice of semantic representation for the spoken language text data, clustering can serve as a tool to spot sentence variants for the same or similar intents.  ... 
doi:10.21437/interspeech.2018-2436 dblp:conf/interspeech/PadmasundariB18 fatcat:xpojukqy7zcmxhbl4ujauo3gty

Short text understanding through lexical-semantic analysis

Wen Hua, Zhongyuan Wang, Haixun Wang, Kai Zheng, Xiaofang Zhou
2015 2015 IEEE 31st International Conference on Data Engineering  
In this work, we use lexicalsemantic knowledge provided by a well-known semantic network for short text understanding.  ...  The results show that knowledge is indispensable for short text understanding, and our knowledge-intensive approaches are effective in harvesting semantics of short texts.  ...  Therefore, a better framework for short text understanding should be one with feedbacks. We leave it as future work.  ... 
doi:10.1109/icde.2015.7113309 dblp:conf/icde/HuaWWZZ15 fatcat:5eslddmeqvav7pxac75aq4oadq
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