<span lang="EN-US">Unit short-term memory (LSTM) is a type of recurrent neural network (RNN) whose sequence-based models are being used in text generation and/or prediction tasks, question answering, and classification systems due to their ability learn long-term dependencies. The present research integrates the LSTM dropout technique generate from corpus as input, model developed find be...

The technological leaps of artificial intelligence (AI) and the rise machine learning have triggered significant progress in a plethora natural language processing (NLP) understanding tasks. One these tasks is argumentation mining which has received interest recent years regarded as key domain for future decision-making systems, behaviour modelling, problems. Until recently, modelling tasks, su...

Linking authors of short-text contents has important usages in many applications, including Named Entity Recognition (NER) and human community detection. However, certain challenges lie ahead. First, the input are noisy, ambiguous, do not follow grammatical rules. Second, traditional text mining methods fail to effectively extract concepts through words phrases. Third, textual temporally skewed...

The amount of information exchanged over the Internet is continuously growing, taking the form of short text messages on microblogging platforms such as Twitter. Due to the limited size of these types of messages, their understanding may require to know the context of their occurrence. In this paper, we propose a higher-level representation of short text messages based on a thematic model obtai...

Short-text classification is increasingly used in a wide range of applications. However, it still remains a challenging problem due to the insufficient nature of word occurrences in short-text documents, although some recently developed methods which exploit syntactic or semantic information have enhanced performance in short-text classification. The language-dependency problem, however, caused...

Near-duplicates are abundant in short text databases. Detecting and eliminating them is of great importance. SimFinder proposed in this paper is a fast algorithm to identify all nearduplicates in large-scale short text databases. An ad hoc term weighting scheme is employed to measure each term’s discriminative ability. A certain number of terms are extracted to form a feature list for each shor...

The ubiquitous, diverse and growing impact of digital living creates a massive amount of short text a search query, a twit or a caption. Short text frequently presents itself as an arbitrary combination of semantically unconnected words. Using machine learning to classify the corpora of such texts is a challenging task. A large body of research exists in this area, but in this paper we will foc...