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Enrichment of OntoSenseNet: Adding a sense-annotated Telugu lexicon
The paper describes the enrichment of OntoSenseNet- a verb-centric lexic...
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OntoSenseNet: A Verb-Centric Ontological Resource for Indian Languages
Following approaches for understanding lexical meaning developed by Yask...
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Prior Polarity Lexical Resources for the Italian Language
In this paper we present SABRINA (Sentiment Analysis: a Broad Resource f...
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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 ...
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Towards Automation of Sense-type Identification of Verbs in OntoSenseNet(Telugu)
In this paper, we discuss the enrichment of a manually developed resourc...
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SweLL on the rise: Swedish Learner Language corpus for European Reference Level studies
We present a new resource for Swedish, SweLL, a corpus of Swedish Learne...
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Developing a cardiovascular disease risk factor annotated corpus of Chinese electronic medical records
Cardiovascular disease (CVD) has become the leading cause of death in Ch...
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A Formal Ontology-Based Classification of Lexemes and its Applications
The paper describes the enrichment of OntoSenseNet - a verb-centric lexical resource for Indian Languages. A major contribution of this work is preservation of an authentic Telugu dictionary by developing a computational version of the same. It is important because native speakers can better annotate the sense-types when both the word and its meaning are in Telugu. Hence efforts are made to develop the aforementioned Telugu dictionary and annotations are done manually. The manually annotated gold standard corpus consists 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 the developed resource through inter-annotator agreement. Additional words from Telugu WordNet are added to our resource and are crowd-sourced for annotation. The statistics are compared with the sense-annotated lexicon, our resource for more insights.
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