In natural language processing, short-text semantic similarity (STSS) is a very prominent field. It has significant impact on broad range of applications, such as question–answering systems, information retrieval, entity recognition, text analytics, sentiment classification, and so on. Despite their widespread use, many traditional machine learning techniques are incapable identifying the seman...

Analyzing short texts infers discriminative and coherent latent topics that is a critical fundamental task since many real-world applications require semantic understanding of texts. Traditional long text topic modeling algorithms (e.g., PLSA LDA) based on word co-occurrences cannot solve this problem very well only limited co-occurrence information available in Therefore, has already attracted...

in this research, it has bec : tried to survey the short narration of ibrahim adham repentence being selected from four major mystical texts. at first each text has been divided into a few statements. then based on different codes, they have been classified. after a comparison through the narrative structure framework, their narrative characteristics have been found out. in fact, a scientific n...

Short Texts play a significant role in reviews, blogs, social networking sites etc. The replacement of featured text by short text can greatly reduce the text size, thereby allowing more information to be communicated. Emails apparently do not have large number of short texts. This paper proposes a novel idea for automatic construction of Short Text Dictionary (STD) that maps the large texts in...

conclusions mobile phone text messaging increased adherence to diabetes therapy and improved the clinical outcome in saudi patients with type 2 diabetes. results in addition to significant improvement in patients’ knowledge, mean fasting blood glucose level improved from 8.60 ± 3.16 to 7.77 ± 3.11 mmol/l and mean hba1c decreased from 9.9% ± 1.8% to 9.5% ± 1.7%. objectives we conducted a study t...

Machine Translation (MT) was an important area of Natural Language Processing that dealt with the translation of one natural language to another language. In this paper we were presenting the research on Translation of short messages to Plain English Text Messages. In today’s world where communication over the internet had increased by using various types of websites and another internet applic...

Text classification is a fundamental task in NLP applications. Most existing work relied on either explicit or implicit text representation to address this problem. While these techniques work well for sentences, they can not easily be applied to short text because of its shortness and sparsity. In this paper, we propose a framework based on convolutional neural networks that combines explicit ...

Many current Natural Language Processing [NLP] techniques work well assuming a large context of text as input data. However they become ineffective when applied to short texts such as Twitter feeds. To overcome the issue, we want to find a related newswire document to a given tweet to provide contextual support for NLP tasks. This requires a robust modeling and understanding of the semantics of...