MicroRNAs play critical roles in many physiological processes. Their dysregulations are also closely related to the development and progression of various human diseases, including cancer. Therefore, identifying new microRNAs that are associated with diseases contributes to a better understanding of pathogenicity mechanisms. MicroRNAs also represent a tremendous opportunity in biotechnology for...

Many traditional information retrieval (IR) tasks, such as text search, text clustering or text categorization, have natural language documents as their first-class objects, in the sense that the algorithms that are meant to solve these tasks require explicit internal representations of the documents they need to deal with. In IR documents are usually given as extensional vectorial representati...

Corresponding Author: Jitendra Nath Singh Department of Computer Science, Babasaheb Bhimrao Ambedkar University, Lucknow, India Email: [email protected] Abstract: Vector space model allows computing a continuous degree of similarity between queries and retrieved documents and then ranks the documents in increasing order of cosine (similarity) value. It computes cosine or similarity value us...

We present an efficient method that uses canonical correlation analysis (CCA) between words and their contexts (i.e., the neighboring words) to estimate a real-valued vector for each word that characterizes its “hidden state” or “meaning”. The use of CCA allows us to prove theorems characterizing how accurately we can estimate this hidden state. Recently developed algorithms for computing the r...

We address the task of computing vector space representations for the meaning of word occurrences, which can vary widely according to context. This task is a crucial step towards a robust, vector-based compositional account of sentence meaning. We argue that existing models for this task do not take syntactic structure sufficiently into account. We present a novel structured vector space model ...

Over the last quarter-century, there is increasing body of research on understanding the human emotions. In this study, automatic classification of anger, disgust, fear, joy and sad emotions in text have been studied on the ISEAR (International Survey on Emotion Antecedents and Reactions) dataset. For the classification we have used Vector Space Model with a total of 801 news headlines provided...

This paper introduces the task of Chinese personal name disambiguation of the Second CIPS-SIGHAN Joint Conference on Chinese Language Processing (CLP) 2012 that Natural Language Processing Laboratory of Zhengzhou University took part in. In this task, we mainly use the Vector Space Model to disambiguate Chinese personal name. We extract different named entity features from diverse names informa...

Cross-document coreference occurs when the same person, place, event, or concept is discussed in more than one text source. Computer recognition of this phenomenon is important because it helps break "the document boundary" by allowing a user to examine information about a particular entity from multiple text sources at the same time. In this paper we describe a cross-document coreference resol...

Classification of text documents presents a unique challenge to conventional classification algorithms. Due to the existence of large number of features in the datasets, providing a desired representation for text documents can be seen as another problem. In this paper a simple but effective representation model for text documents to tackle the classification problem is discussed. Two different...

Syntactic comparison across languages is essential in the research field of linguistics, e.g. when investigating the relationship among closely related languages. In IR and NLP, the syntactic information is used to understand the meaning of word occurrences according to the context in which their appear. In this paper, we discuss a mathematical framework to compute the distance between language...