Informatics Publishing Limited

Jisha P. Jayan ¹, Deepu S. Nair ¹, Elizabeth Sherly ¹

Affiliations
1 Virtual Resource Center for Language Computing(VRCLC), Indian Institute of Information Technology and Management-Kerala, Thiruvananthapuram, IN

Abstract

In recent days, Sentiment Analysis has become an active research in NLP, which analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from writing language. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, and social network. In his paper, sentiment analysis of Malayalam film review is carried out using machine learning techniques CRF combined with a rule based approach. The system shows 82 % accuracy.

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Maya Moneykumar ¹, Elizabeth Sherly ¹, Win Sam Varghese ¹

Affiliations
1 Indian Institute of Information Technology and Management (IIITM-K), Kerala, IN

Abstract

Automatic Speech Recognition (ASR) Systems have long been a goal of artificial intelligence researchers. The lack of state-of-the art ASR System has been a major hindrance due to its complexity in reproducing in Computer. Hidden Markov Models (HMMs) are used heavily in most current speech recognition systems for both phoneme and syllable based approach. In this paper, we also propose to use HMM model, but based on energy measure and Mel Frequency Cepstral Coefficient (MFCC) to determine the syllable based segmentation and features of power spectrum of speech signal. The system was trained with utterances of 3 male and 2 female speakers and the database included 40 utterances. The training and testing was done with bi-syllable words and the implementation of the system has been done using Hidden Markov Model Toolkit (HTK).

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Indu Joseph Thoppil ¹, Elizabeth Sherly ¹

Affiliations
1 Indian Institute of Information Technology and Management-Kerala, IN

Abstract

Morpheme boundary identification is one of the prominent problems in morphological analysis of NLP applications. This study proposes an alternative technique to effectively identify the boundary of each morpheme from a compound word. This is useful in wide range of NLP applications from stemming, word assistance to document categorizers. Malayalam, a major South Indian language has the linguistic capability to have high number of morphemes per word. The present study sets out to discover the effectiveness of Letter Successor Variety techniques could be useful for identifying the morpheme boundary in Malayalam. Letter Successor Variety is based on statistical co-occurrence measures and contextually similar words.

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Stephy Joseph ¹, Shabina Bhaskar ¹, Elizabeth Sherly ²

Affiliations
1 Indian Institute of Information Technology and Management – Kerala, IN
2 Indian Institute of Information Technology and Management - Kerala, IN

Abstract

A study on divergence between or among languages plays a significant role to formulate a generic approach to Machine Translation (MT) in Indian Languages. Transfer Grammar acts as a bridging component between the source and target languages, through which various divergences between two languages can be analysed. In this paper, a word order based changes in Malayalam-English and Malayalam-Hindi is analysed and applied rule based and Machine Learning techniques to extract relevant data structures.

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Raja Varma Pamba ¹, Elizabeth Sherly ², Kiran Mohan ³

Affiliations
1 School of Computer Sciences, Mahatma Gandhi University, Kottayam, IN
2 Indian Institute of Information Technology and Management-Kerala, Trivandrum, IN
3 Payszone LLC LTD, Dubai, AE

Abstract

To mine out relevant facts at the time of need from web has been a tenuous task. Research on diverse fields are fine tuning methodologies toward these goals that extracts the best of information relevant to the users search query. In the proposed methodology discussed in this paper find ways to ease the search complexity tackling the severe issues hindering the performance of traditional approaches in use. The proposed methodology find effective means to find all possible semantic relatable frequent sets with FP Growth algorithm. The outcome of which is the further source of fuel for Bio inspired Fuzzy PSO to find the optimal attractive points for the web documents to get clustered meeting the requirement of the search query without losing the relevance. On the whole the proposed system optimizes the objective function of minimizing the intra cluster differences and maximizes the inter cluster distances along with retention of all possible relationships with the search context intact. The major contribution being the system finds all possible combinations matching the user search transaction and thereby making the system more meaningful. These relatable sets form the set of particles for Fuzzy Clustering as well as PSO and thus being unbiased and maintains a innate behaviour for any number of new additions to follow the herd behaviour's evaluations reveals the proposed methodology fares well as an optimized and effective enhancements over the conventional approaches.

Keywords

Information Retrieval, Clustering, Fuzzy Particle Swarm Optimization and Frequent Pattern Growth.

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