Prof Navjyoti Singh not only for his valuable inputs and insights regarding academics and research, but also for his view on life which expanded my boundaries of thought. I also thank him for his immense support and belief in me. His continuous review process before submitting every research paper made me understand the way of presenting ideas in the paper. I also want to thank Tavva Rajesh for his patience and efforts in making me understand the foundational concepts related to my research. I also want to thank my junior Divya Kukar who played an useful role in making the game which is designed for data enrichment. I would like to thank all my friends for making my stay at IIIT Hyderabad memorable. I have no words to express my gratitude to my parents Kumar, Bhagya Lakshmi and my brother Santhosh for their unconditional love, support, and continuous motivation throughout my life.
The paper describes the enrichment of OntoSenseNet-a verbcentric 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. It is 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.
Most scholarly works in the field of computational detection of humour derive their inspiration from the incongruity theory. Incongruity is an indispensable facet in drawing a line between humorous and non-humorous occurrences but is immensely inadequate in shedding light on what actually made the particular occurrence a funny one. Classical theories like Script-based Semantic Theory of Humour and General Verbal Theory of Humour try and achieve this feat to an adequate extent. In this paper we adhere to a more holistic approach towards classification of humour based on these classical theories with a few improvements and revisions. Through experiments based on our linear approach and performed on large data-sets of jokes, we are able to demonstrate the adaptability and show componentizability of our model, and that a host of classification techniques can be used to overcome the challenging problem of distinguishing between various categories and sub-categories of jokes. * * Both authors have contributed equally towards the paper (names in lexicographic sequence).
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