Search Results for author:
Found 18 papers, 9 papers with code
Incorporating Sub-Word Level Information in Language Invariant Neural Event Detection
We compare the performance of our language invariant model to the current state-of-the-art in English, Spanish, Italian and French.
Event Centric Entity Linking for Hindi News Articles: A Knowledge Graph Based Approach
We describe the development of a knowledge graph from an event annotated corpus by presenting a pipeline that identifies and extracts the relations between entities and events from Hindi news articles.
Improving the TENOR of Labeling: Re-evaluating Topic Models for Content Analysis
Topic models are a popular tool for understanding text collections, but their evaluation has been a point of contention.
Natural Language Decompositions of Implicit Content Enable Better Text Representations
When people interpret text, they rely on inferences that go beyond the observed language itself.
Are Neural Topic Models Broken?
Recently, the relationship between automated and human evaluation of topic models has been called into question.
Studying word order through iterative shuffling
As neural language models approach human performance on NLP benchmark tasks, their advances are widely seen as evidence of an increasingly complex understanding of syntax.
Is Automated Topic Model Evaluation Broken?: The Incoherence of Coherence
To address the standardization gap, we systematically evaluate a dominant classical model and two state-of-the-art neural models on two commonly used datasets.
GPT Perdetry Test: Generating new meanings for new words
Human innovation in language, such as inventing new words, is a challenge for pretrained language models.
Is Automated Topic Model Evaluation Broken? The Incoherence of Coherence
To address the standardization gap, we systematically evaluate a dominant classical model and two state-of-the-art neural models on two commonly used datasets.
Improving Neural Topic Models using Knowledge Distillation
Topic models are often used to identify human-interpretable topics to help make sense of large document collections.
Hindi TimeBank: An ISO-TimeML Annotated Reference Corpus
In this paper, we present the Hindi TimeBank, an ISO-TimeML annotated reference corpus for the detection and classification of events, states and time expressions, and the links between them.
Towards Automatic Generation of Questions from Long Answers
Therefore, we propose a novel evaluation benchmark to assess the performance of existing AQG systems for long-text answers.
How Pre-trained Word Representations Capture Commonsense Physical Comparisons
One type of common sense is how two objects compare on physical properties such as size and weight: e. g., {`}is a house bigger than a person?{'}.
How emotional are you? Neural Architectures for Emotion Intensity Prediction in Microblogs
Social media based micro-blogging sites like Twitter have become a common source of real-time information (impacting organizations and their strategies, and are used for expressing emotions and opinions.
Prayas at EmoInt 2017: An Ensemble of Deep Neural Architectures for Emotion Intensity Prediction in Tweets
Intensity is a real valued score, between 0 and 1.
IIT (BHU): System Description for LSDSem'17 Shared Task
This paper describes an ensemble system submitted as part of the LSDSem Shared Task 2017 - the Story Cloze Test.
Automatic Identification of Sarcasm Target: An Introductory Approach
To compare our approach, we use two baselines: a na\"ive baseline and another baseline based on work in sentiment target identification.
