English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this paper, we present our approaches for the FinSim 2020 shared task on"Learning Semantic Representations for the Financial Domain". The goal of thistask is to classify financial terms into the most relevant hypernym (ortop-level) concept in an external ontology. We leverage both context-dependentand context-independent word embeddings in our analysis. Our systems deployWord2vec embeddings trained from scratch on the corpus (Financial Prospectus inEnglish) along with pre-trained BERT embeddings. We divide the test datasetinto two subsets based on a domain rule. For one subset, we use unsuperviseddistance measures to classify the term. For the second subset, we use simplesupervised classifiers like Naive Bayes, on top of the embeddings, to arrive ata final prediction. Finally, we combine both the results. Our system ranks 1stbased on both the metrics, i.e., mean rank and accuracy.

IITK at the FinSim Task: Hypernym Detection in Financial Domain via Context-Free and Contextualized Word Embeddings