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Classification of Short Legal Lithuanian Texts. [PDF]

, 2015
Statistical analysis of parliamentary roll call votes is an important topic in political science because it reveals ideological positions of members of parliament (MP) and factions. However, it depends on the issues debated and voted upon. Therefore, analysis of carefully selected sets of roll call votes provides a deeper knowledge about MPs.
Mickevičius, Vytautas, Krilavičius, Tomas, Morkevičius, Vaidas   +2 more
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On Term Similarity Measures for Short Text Classification

2019 IEEE 11th International Workshop on Computational Intelligence and Applications (IWCIA), 2019
We study term expansion (or document expansion), which is used for classifying documents, especially for short documents such as twitter and blogs on the Web. Term expansion enables us to augment the sparse information in those short documents. Carpineto et al.
Hirohisa Seki, Shuhei Toriyama
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Research on short text classification for web forum

2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), 2011
The unique characteristic of short text makes short text classification quite different from traditional long text processing. The feature space of short text is so sparse, which makes it notoriously difficult to extract sufficient and effective features.
Xiaochun He, Conghui Zhu, Tiejun Zhao
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Classification of Short Texts by Deploying Topical Annotations

2012
We propose a novel approach to the classification of short texts based on two factors: the use of Wikipedia-based annotators that have been recently introduced to detect the main topics present in an input text, represented via Wikipedia pages, and the design of a novel classification algorithm that measures the similarity between the input text and ...
Vitale D, FERRAGINA, PAOLO, Scaiella U.
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Short technical text classification

2021 20th RoEduNet Conference: Networking in Education and Research (RoEduNet), 2021
Iulia-Maria Florea, Razvan Victor Rughinis, Stefan Dan Ciocîrlan   +2 more
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Few-shot learning for short text classification

Multimedia Tools and Applications, 2018
Due to the limited length and freely constructed sentence structures, it is a difficult classification task for short text classification. In this paper, a short text classification framework based on Siamese CNNs and few-shot learning is proposed. The Siamese CNNs will learn the discriminative text encoding so as to help classifiers distinguish those ...
Leiming Yan, Yuhui Zheng, Jie Cao 0011
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Classification of sentiments in short-text

Proceedings of the 2nd International Conference on Machine Learning and Soft Computing, 2018
Sentiment analysis or opinion mining is an automated process to recognize opinion, moods, emotions, attitude of individuals or communities through natural language processing, text analysis, and computational linguistics. In recent years, many studies concentrated on numerous blogs, tweets, forums and consumer review websites to identify sentiment of ...
H. M. Keerthi Kumar, B. S. Harish, S. V. Aruna Kumar, V. N. Manjunath Aradhya   +3 more
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Topic Classification for Short Texts

International Conference on Information Systems Development, 2022
Dan Claudiu Neagu, Andrei Bogdan Rus, Mihai Grec, Mihai Boroianu, Gheorghe Cosmin Silaghi   +4 more
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Short-Text Classification Based on ICA and LSA

2006
Many applications, such as word-sense disambiguation and information retrieval, can benefit from text classification. Text classifiers based on Independent Component Analysis (ICA) try to make the most of the independent components of text documents and give in many cases good classification effects.
Qiang Pu, Guo-Wei Yang
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Review of Chinese Short Text Classification

Applied Mechanics and Materials, 2013
Because features of Chinese short text are different from long text, current text classification technology of long text classification are not suit of short text classification. This paper summarizes features, various methods and Application for Chinese short text classification, and pointes out their advantages, disadvantages and applicability scope.
Fen Lin Wu, Jin Gou, Cheng Wang
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