Search Results for author: Ayush Maheshwari

Found 8 papers, 5 papers with code

Learning to Robustly Aggregate Labeling Functions for Semi-supervised Data Programming

no code implementations23 Sep 2021 Ayush Maheshwari, KrishnaTeja Killamsetty, Ganesh Ramakrishnan, Rishabh Iyer, Marina Danilevsky, Lucian Popa

These LFs, in turn, have been used to generate a large amount of additional noisy labeled data, in a paradigm that is now commonly referred to as data programming.

Text Classification

SPEAR : Semi-supervised Data Programming in Python

1 code implementation1 Aug 2021 Guttu Sai Abhishek, Harshad Ingole, Parth Laturia, Vineeth Dorna, Ayush Maheshwari, Ganesh Ramakrishnan, Rishabh Iyer

SPEAR facilitates weak supervision in the form of heuristics (or rules) and association of noisy labels to the training dataset.

Rule Augmented Unsupervised Constituency Parsing

2 code implementations Findings (ACL) 2021 Atul Sahay, Anshul Nasery, Ayush Maheshwari, Ganesh Ramakrishnan, Rishabh Iyer

We introduce a novel formulation that takes advantage of the syntactic grammar rules and is independent of the base system.

Constituency Parsing

Joint Learning of Hyperbolic Label Embeddings for Hierarchical Multi-label Classification

1 code implementation EACL 2021 Soumya Chatterjee, Ayush Maheshwari, Ganesh Ramakrishnan, Saketha Nath Jagaralpudi

Such a joint learning is expected to provide a twofold advantage: i) the classifier generalizes better as it leverages the prior knowledge of existence of a hierarchy over the labels, and ii) in addition to the label co-occurrence information, the label-embedding may benefit from the manifold structure of the input datapoints, leading to embeddings that are more faithful to the label hierarchy.

Classification General Classification +1

Semi-Supervised Data Programming with Subset Selection

2 code implementations Findings (ACL) 2021 Ayush Maheshwari, Oishik Chatterjee, KrishnaTeja Killamsetty, Ganesh Ramakrishnan, Rishabh Iyer

The first contribution of this work is an introduction of a framework, \model which is a semi-supervised data programming paradigm that learns a \emph{joint model} that effectively uses the rules/labelling functions along with semi-supervised loss functions on the feature space.

Text Classification

Tale of tails using rule augmented sequence labeling for event extraction

no code implementations19 Aug 2019 Ayush Maheshwari, Hrishikesh Patel, Nandan Rathod, Ritesh Kumar, Ganesh Ramakrishnan, Pushpak Bhattacharyya

The problem of event extraction is a relatively difficult task for low resource languages due to the non-availability of sufficient annotated data.

Event Extraction

Entity Resolution and Location Disambiguation in the Ancient Hindu Temples Domain using Web Data

no code implementations NAACL 2018 Ayush Maheshwari, Vishwajeet Kumar, Ganesh Ramakrishnan, J. Saketha Nath

We present a system for resolving entities and disambiguating locations based on publicly available web data in the domain of ancient Hindu Temples.

Entity Resolution

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