Enrichment of OntoSenseNet: Adding a Sense-annotated Telugu lexicon

The paper describes the enrichment of OntoSenseNet - a verb-centric lexical resource for Indian Languages. This resource contains a newly developed Telugu-Telugu dictionary. It is important because native speakers can better annotate the senses when both the word and its meaning are in Telugu. Hence efforts are made to develop a soft copy of Telugu dictionary. Our resource also has manually annotated gold standard corpus consisting 8483 verbs, 253 adverbs and 1673 adjectives. Annotations are done by native speakers according to defined annotation guidelines. In this paper, we provide an overview of the annotation procedure and present the validation of our resource through inter-annotator agreement. Concepts of sense-class and sense-type are discussed. Additionally, we discuss the potential of lexical sense-annotated corpora in improving word sense disambiguation (WSD) tasks. Telugu WordNet is crowd-sourced for annotation of individual words in synsets and is compared with the developed sense-annotated lexicon (OntoSenseNet) to examine the improvement. Also, we present a special categorization (spatio-temporal classification) of adjectives.Comment: Accepted Long Paper at 19th International Conference on Computational Linguistics and Intelligent Text Processing, March 2018, Hanoi, Vietna

Towards Enhancing Lexical Resource and Using Sense-annotations of OntoSenseNet for Sentiment Analysis

This paper illustrates the interface of the tool we developed for crowd sourcing and we explain the annotation procedure in detail. Our tool is named as 'Parupalli Padajaalam' which means web of words by Parupalli. The aim of this tool is to populate the OntoSenseNet, sentiment polarity annotated Telugu resource. Recent works have shown the importance of word-level annotations on sentiment analysis. With this as basis, we aim to analyze the importance of sense-annotations obtained from OntoSenseNet in performing the task of sentiment analysis. We explain the fea- tures extracted from OntoSenseNet (Telugu). Furthermore we compute and explain the adverbial class distribution of verbs in OntoSenseNet. This task is known to aid in disambiguating word-senses which helps in enhancing the performance of word-sense disambiguation (WSD) task(s).Comment: Accepted at 3rd Workshop on Semantic Deep Learning (SemDeep-3) at The 27th International Conference on Computational Linguistics, COLING (August 2018) in Santa Fe, New Mexico, US

The 6th Workshop on Asian Languae Resources, 2008 ASSESSMENT AND DEVELOPMENT OF POS TAG SET FOR TELUGU

In this paper, we first had a overall study of existing POS tag sets for European and Indian languages. Till now, most of the research done on POS tagging is for English. We observed that even though the research on POS tagging for English is done exhaustively, part-of-speech annotation in various research applications is incomparable which is variously due to the variations in tag set definitions. We understand that the morphosyntactic features of the language and the degree of desire to represent the granularity of these morpho-syntactic features, domain etc., decide the tags in the tag set. We then examined how POS tagset design has to be handled for Indian languages, taking Telugu language into consideration. 1