Mike Thelwall, Kevan Buckley, Georgios Paltoglou, Di Cai Statistical Cybermetrics Research Group, School of Computing and Information Technology, University of Wolverhampton, Wulfruna Street, Wolverhampton WV1 1SB, UK. E-mail: [email protected], [email protected], [email protected], [email protected] Tel: +44 1902 321470 Fax: +44 1902 321478 Arvid Kappas School of Humanities and Social ...

This paper presents work that uses Transductive Latent Semantic Indexing (LSI) for text classification. In addition to relying on labeled training data, we improve classification accuracy by incorporating the set of test examples in the classification process. Rather than performing LSI’s singular value decomposition (SVD) process solely on the training data, we instead use an expanded term-by-...

Measuring the similarity between documents and queries has been extensively studied in information retrieval. However, there are a growing number of tasks that require computing the similarity between two very short segments of text. These tasks include query reformulation, sponsored search, and image retrieval. Standard text similarity measures perform poorly on such tasks because of data spar...

In “Role” based Performance Appraisal process the evaluation of Individuals is done based on the meeting of target for “Goals” given to that individual in the specified time period. Standardization of goals with the help of a pre defined “template” is important for completeness and correctness of role definition and comparing two individuals. Since a goal is a short textual description of expec...

We propose Neural Responding Machine (NRM), a neural network-based response generator for Short-Text Conversation. NRM takes the general encoder-decoder framework: it formalizes the generation of response as a decoding process based on the latent representation of the input text, while both encoding and decoding are realized with recurrent neural networks (RNN). The NRM is trained with a large ...

As the type and the number of such venues increase, automated analysis of sentiment on textual resources has become an essential data mining task. In this paper, we investigate the problem of mining opinions by extracting aspects of entities on the collection of informal short texts. Both positive and negative sentiment strength of texts are detected. We focus on a non-English language that has...

Indonesian government has developed a system for citizens to voice their aspirations and complaints, which are then stored in the form of short documents. Unfortunately, the existing system employs human annotators to manually categorize the short documents, which is very expensive and time-consuming. As a result, automatically classifying the short documents into their correct topics will redu...

The long text classification has got great achievements, but short text classification still needs to be perfected. In this paper, at first, we describe why we select the ITC feature selection algorithm not the conventional TFIDF and the superiority of the ITC compared with the TFIDF, then we conclude the flaws of the conventional ITC algorithm, and then we present an improved ITC feature selec...

In this paper, we propose a novel method for improving the classification performance of short text strings using conditional random fields (CRFs) that combine morphological information. Experimental results on three datasets (Uyghur, Chinese, and English) demonstrate that our method can yield higher classification accuracy than Support Vector Machine (SVM) classifier and Maximum Entropy Model ...