word embeddings

"A Passage to India": Pre-trained Word Embeddings for Indian Languages

Dense word vectors or 'word embeddings' which encode semantic properties of words, have now become integral to NLP tasks like Machine Translation (MT), Question Answering (QA), Word Sense Disambiguation (WSD), and Information Retrieval (IR). In this …

Recommendation Chart of Domains for Cross-Domain Sentiment Analysis: Findings of A 20 Domain Study

Cross-domain sentiment analysis (CDSA) helps to address the problem of data scarcity in scenarios where labelled data for a domain (known as the target domain) is unavailable or insufficient. However, the decision to choose a domain (known as the …

Strategies of Effective Digitization of Commentaries and Sub-commentaries: Towards the Construction of Textual History

This paper describes additional aspects of a digital tool called the ‘Textual History Tool’. We describe its various salient features with special reference to those of its features that may help the philologist digitize commentaries and …

"Keep Your Dimensions on a Leash": True Cognate Detection using Siamese Deep Neural Networks

Automatic Cognate Detection helps NLP tasks of Machine Translation, Information Retrieval, and Phylogenetics. Cognate words are defined as word pairs across languages which exhibit partial or full lexical similarity and mean the same (e.g., …

Harnessing Deep Cross-lingual Word Embeddings to Infer Accurate Phylogenetic Trees

Establishing language relatedness by inferring phylogenetic trees has been a topic of interest in the area of diachronic linguistics. However, existing methods face meaning conflation deficiency due to the usage of lexical similarity-based measures. …

Utilizing Word Embeddings based Features for Phylogenetic Tree Generation of Sanskrit Texts

Tracing the root of a text i.e., the original version of the text, by inferring phylogenetic trees has been a topic of interest in philological studies. However, existing methods face meaning conflation deficiency due to the usage of lexical …