1 code implementation • 17 Jan 2017 • Siddhesh Khandelwal, Amit Awekar
We propose a fast heuristic to overcome this bottleneck with only marginal increase in MSE.
1 code implementation • 19 Jan 2018 • Sweta Agrawal, Amit Awekar
We show that deep learning based models can overcome all three bottlenecks.
1 code implementation • EACL 2017 • abhishek, Ashish Anand, Amit Awekar
Fine-grained entity type classification (FETC) is the task of classifying an entity mention to a broad set of types.
1 code implementation • AKBC 2019 • Abhishek Abhishek, Sanya Bathla Taneja, Garima Malik, Ashish Anand, Amit Awekar
Fine-grained Entity Recognition (FgER) is the task of detecting and classifying entity mentions to a large set of types spanning diverse domains such as biomedical, finance and sports.
1 code implementation • 17 Jul 2019 • Manash Pratim Barman, Amit Awekar, Sambhav Kothari
We focus on two aspects: style and biases of song lyrics.
1 code implementation • 13 Sep 2019 • Akshay Parekh, Ashish Anand, Amit Awekar
Further, to address the second question, we canonicalize, filter, and combine the identified relations from the three resources to construct a taxonomical hierarchy.
no code implementations • 20 Oct 2018 • Abhishek Abhishek, Amar Prakash Azad, Balaji Ganesan, Ashish Anand, Amit Awekar
The CLF first creates a unified hierarchical label set (UHLS) and a label mapping by aggregating label information from all available datasets.
no code implementations • 17 Apr 2021 • Angana Borah, Manash Pratim Barman, Amit Awekar
A representation learning method is considered stable if it consistently generates similar representation of the given data across multiple runs.
no code implementations • 26 Dec 2021 • Akshay Parekh, Ashish Anand, Amit Awekar
The immediate follow-up problem is: Given a specific reannotation budget, which subset of the data should we reannotate?
no code implementations • 21 Nov 2023 • Akshay Parekh, Ashish Anand, Amit Awekar
Towards the first objective, we analyze predictions and performance of state-of-the-art (SOTA) models to identify the root cause of noise in the dataset.
no code implementations • 28 Dec 2023 • Rohit Raj Rai, Amit Awekar
First, there is a significant variation in the bias of word embeddings with the dimensionality change.