Alok Ranjan Pal

WORD SENSE DISAMBIGUATION: A SURVEY

In this paper, we made a survey on Word Sense Disambiguation (WSD). Near about in all major languages around the world, research in WSD has been conducted upto different extents. In this paper, we have gone through a survey regarding the different approaches adopted in different research works, the State of the Art in the performance in this domain, recent works in different Indian languages and finally a survey in Bengali language. We have made a survey on different competitions in this field and the bench mark results, obtained from those competitions.

Fuzzy Based Multi-Agent System Offering Cost Effective Corporate Envi- ronment

We propose a general framework of a hierarchical structure, consisting of several levels of activities, for typical software related corporate hierarchy using multi-agent system. This work identifies the functionality of each level. Each and every level is considered as agent who is described further using fuzzy analysis. Our framework consists of six levels within which first five levels are considered as agents followed by the human interaction at the lowermost level. These agents interact with each other to produce a desired result for the client based on autonomous decisions which are decided through fuzzy reasoning with the help of predefined databases. A layered architecture has been proposed in this paper for showing a corporate office hierarchy in a cost effective manner. In general, management employees of the corporate sys- tem draw a huge amount of money for their activities. Our ultimate aim is to reduce the cost of the existing corporate sys- tem by observing and controlling the behavioral characteristics of each level of hierarchy by replacing typical manual op- erations with agents (1,2). We have presented a case study or practical engineering example along with the description of each agent.

Word sense disambiguation in Bengali: A lemmatized system increases the accuracy of the result

In the proposed approach, an attempt was made to disambiguate Bengali ambiguous words using Naïve Bayes Classification algorithm. The whole task was divided into two modules. Each module executes a specific task. In the first module, the algorithm was applied on a regular text, collected from the Bengali text corpus developed in the TDIL project of the Govt. of India and the accuracy of disambiguation process was obtained around 80%. In the second module, the whole training data and the test data were lemmatized and applying the same algorithm, around 85% accurate result was obtained. The output was verified with a previously tagged output file, generated with the help of a Bengali lexical dictionary. The implicational relevance of this study was attested in automatic text classification, machine learning, information extraction, and word sense disambiguation.

Detection of Slang Words in e-Data using semi- Supervised Learning

The proposed algorithmic approach deals with finding the sense of a word in an electronic data. Now a day, in different communication mediums like internet, mobile services etc. people use few words, which are slang in nature. This approach detects those abusive words using supervised learning procedure. But in the real life scenario, the slang words are not used in complete word forms always. Most of the times, those words are used in different abbreviated forms like sounds alike forms, taboo morphemes etc. This proposed approach can detect those abbreviated forms also using semi supervised learning procedure. Using the synset and concept analysis of the text, the probability of a suspicious word to be a slang word is also evaluated.

A HYBRID APPROACH TO WORD SENSE DISAMBIGUATION COMBINING SUPERVISED AND UNSUPERVISED LEARNING

In this paper, we are going to find meaning of words based on distinct situations. Word Sense Disambiguation is used to find meaning of words based on live contexts using supervised and unsupervised approaches. Unsupervised approaches use online dictionary for learning, and supervised approaches use manual learning sets. Hand tagged data are populated which might not be effective and sufficient for learning procedure. This limitation of information is main flaw of the supervised approach. Our proposed approach focuses to overcome the limitation using learning set which is enriched in dynamic way maintaining new data. Trivial filtering method is utilized to achieve appropriate training data. We introduce a mixed methodology having “Modified Lesk” approach and “Bag-of-Words” having enriched bags using learning methods. Our approach establishes the superiority over individual “Modified Lesk” and “Bag-of-Words” approaches based on experimentation.

Comparative study on Null Boundary and Periodic Boundary 3-neighborhood Multiple Attractor Cellular Automata for classification

This paper reports a generic analysis on null boundary and periodic boundary 3-neighborhood multiple attractor cellular automata (MACA) for showing the comparative study in classification technique. Cellular automata (CA) is now-a-days an essential tool for researchers in the area of pattern recognition, pattern generation, testing field, fault diagnosis and so on. So, general knowledge on CA up to some extent is a must for researchers in these areas. A CA may be linear or non-linear in behavior. A linear/additive CA employs XOR/XNOR logic, while a non-linear CA employs AND/OR/NOT logic. This paper shows a graph analysis along with state transition behavior of CA cells. A rule vector graph (RVG) is generated from the rule vector (RV) of a CA. Linear time algorithms are reported for generation of RVG. MACA provides an implicit memory to store the patterns. Search operation to identify the class of a pattern out of several classes boils down to running a CA for one time step. This demands storage of RV and seed values. MACA is based on sound theoretical foundation of CA technology. This paper only concentrates on MACA since it is responsible for classifying the various types of patterns.

Word sense disambiguation in Bengali: A knowledge based approach using Bengali WordNet

In this paper, a knowledge based approach for Word Sense Disambiguation (WSD) in Bengali language has been presented. Bengali WordNet, developed at ISI Kolkata has been used as a knowledge base and the input data set is prepared from the Bengali Text Corpus developed in the TDIL (Technology Development for Indian Language) project of the Government of India. The proposed approach resolute the exact sense of a Bengali ambiguous word based on the maximum overlap among the dictionary definitions of the ambiguous word, with its collocating words in that sentence and the synonymous words of these collocating words. The algorithm is tested on 9 (nine) mostly used Bengali ambiguous words. The accuracy of the output is achieved 75% which is verified by an expert. The challenges and the pitfalls of this approach are discussed in this report in detail.

Word sense disambiguation in Bengali: An unsupervised approach

In the proposed approach, Word Sense Disambiguation (WSD) in Bengali language has been done using unsupervised methodology. This work is consisted of sequential two sub-tasks. First one is grouping of Bengali sentences into a certain number of clusters where a particular cluster contains the sentences of similar meaning and second one is labeling the clusters with its inner meanings with the help of a linguistic expert as these sense tagged clusters could be used as a knowledge reference for WSD task. In this work, clustering has been performed using weka-3-6-13 tool. The test sentences are collected from the Bengali text corpus developed in the TDIL (Technology Development for Indian Language) project of the Govt. of India. In this work, Type-based and Token-based distributional approaches have been developed for Bengali sentence clustering. In Type-based method, a feature vector of co-occurring words of a target word in a sentence has been considered and in Token-based method, synsets of the collocating words are also considered. The synsets of the collocating words are retrieved from the Bengali WordNet, developed at ISI, Kolkata. The base line result, achieved result and the pitfalls of the procedure are discussed in the report in detail.

Word Sense Disambiguation in Bengali: An Auto-updated Learning Set Increases the Accuracy of the Result

This work is implemented using the Naive Bayes probabilistic model. The whole task is implemented in two phases. First, the algorithm was tested on a dataset from the Bengali corpus, which was developed in the TDIL (Technology Development for the Indian Languages) project of the Govt. of India. In the first execution of the algorithm, the accuracy of result was nearly 80 %. In addition to the disambiguation task, the sense evaluated sentences were inserted into the related learning sets to take part in the next executions. In the second phase, after a small manipulation over the learning sets, a new input data set was tested using the same algorithm, and in this second execution, the algorithm produced a better result, around 83 %. The results were verified with the help of a standard Bengali dictionary.