We explore using recursive autoencoders for SemEval 2015 Task 1: Paraphrase and Semantic Similarity in Twitter. Our paraphrase detection system makes use of phrase-structure parse tree embeddings that are then provided as input to a conventional supervised classification model. We achieve an F1 score of 0.45 on paraphrase identification and a Pearson correlation of 0.303 on computing semantic s...

In this paper, for the detection of the masquerade attacks in the cloud infrastructure collaborative filtering algorithm based on the cloud model is proposed. One of the advantages of this model is the identification of the similarity between the users on the basis of the cloud model. While using the similarity measurement method based on the cloud model, it does not require a strict comparison...

Measuring the similarity between two texts is a fundamental problem in many NLP and IR applications. Among the existing approaches, the cosine measure of the term vectors representing the original texts has been widely used, where the score of each term is often determined by a TFIDF formula. Despite its simplicity, the quality of such cosine similarity measure is usually domain dependent and d...

This paper proposes an innovative instance similarity based evaluation metric that reduces the search map for clustering to be performed. An aggregate global score is calculated for each instance using the novel idea of Fibonacci series. The use of Fibonacci numbers is able to separate the instances effectively and, in hence, the intra-cluster similarity is increased and the intercluster simila...

Paraphrase recognition is the task of identifying whether two pieces of natural language represent similar meanings. This paper describes a system participating in the shared task 1 of SemEval 2015, which is about paraphrase detection and semantic similarity in twitter. Our approach is to exploit semantically meaningful features to detect paraphrases. An existing state-of-the-art model for pred...

This paper describes FCICU team participation in SemEval 2015 for Semantic Textual Similarity challenge. Our main contribution is to propose a word-sense similarity method using BabelNet relationships. In the English subtask challenge, we submitted three systems (runs) to assess the proposed method. In Run1, we used our proposed method coupled with a string kernel mapping function to calculate ...

Unsupervised Word Sense Disambiguation (WSD) is one of the challenging problems in natural language processing. Recently, an unsupervised bilingual WSD approach has been proposed. This approach uses context aware EM formulation for estimating the sense distribution by using the co-occurrence counts of cross-linked words in comparable corpora. WordNetbased similarity measures are used for approx...

The topic of this work is the presentation of a novel clustering methodology based on instance similarity in two or more attribute layers. The work is motivated by multi-view clustering and redescription mining algorithms. In our approach we do not construct descriptions of subsets of instances and we do not use conditional independence assumption of different views. We do bottom up merging of ...

BACKGROUND The diversity of organisms is being commonly accessed using metabarcoding of environmental samples. Reliable identification of barcodes is one of the critical steps in the process and several taxonomy assignment methods were proposed to accomplish this task, including alignment-based approach that uses Basic Local Alignment Search Tool (BLAST) algorithm. This publication evaluates th...

Name disambiguation has become one of the hard to crack problem in a virtual setup. With each passing day more and more entities with identical features are emerging online making it quite difficult to distinguish them. Digital libraries face similar problems in differentiating publications of similar looking authors. This leads to incorrect attribution of publications, thus making the entire e...