Data Set Used
In the task of semantic category labeling, given a text, every word in it has to be assigned a semantic category. Our language of interest is Hindi. We use the ontological categories defined in Hindi Wordnet as semantic category inventories. In this paper we present two un-supervised approaches namely Flat Semantic Category Labeler (FSCL) and Hierarchical… (More)
Hindi is an Indian language which is relatively rich in morphology. A few morphological analyzers of this language have been developed. However, they give only inflectional analysis of the language. In this paper, we present our Hindi derivational morphological analyzer. Our algorithm upgrades an existing inflectional analyzer to a derivational analyzer and… (More)
We describe two systems that participated in SemEval-2010 task 17 (All-words Word Sense Disambiguation on a Specific Domain) and were ranked in the third and fourth positions in the formal evaluation. Domain adaptation techniques using the background documents released in the task were used to assign ranking scores to the words and their senses. The test… (More)
One of the important phase of Natural language Processing is Morphological Analysis that helps in work of machine translation. Effective implementation of morphological analyzer can be seen in language which is rich in morphemes. Hindi being an inflected language has capability of generating hundreds of words from the root word. It is morphologically rich… (More)
The purpose of SIGMORPHON is to foster computational research on the phonological, morphological, and phonetic properties of human language. All three of these sub-areas deal largely with the local structure of words and so share many technical methods. Furthermore, computational work that models empirical data must often draw on at least two of these… (More)
This work models Word Sense Disam-biguation (WSD) problem as a Distributed Constraint Optimization Problem (DCOP). To model WSD as a DCOP, we view information from various knowledge sources as constraints. DCOP algorithms have the remarkable property to jointly maximize over a wide range of utility functions associated with these constraints. We show how… (More)
In this paper, we evaluate and compare six semantic relatedness measures used for Hindi semantic category labeling. Our experiments show that the measure " adapted lesk " performed better than other measures. However, a simple baseline system achieved better accuracy than all the measures.