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Found 20 papers, 11 papers with code
CAVES: A Dataset to facilitate Explainable Classification and Summarization of Concerns towards COVID Vaccines
This is also the first multi-label classification dataset that provides explanations for each of the labels.
FaiRIR: Mitigating Exposure Bias from Related Item Recommendations in Two-Sided Platforms
To this end, our experiments on multiple real-world RIR datasets reveal that the existing RIR algorithms often result in very skewed exposure distribution of items, and the quality of items is not a plausible explanation for such skew in exposure.
Alexa, in you, I trust! Fairness and Interpretability Issues in E-commerce Search through Smart Speakers
While investigating for the fairness of the default action, we observe that over a set of as many as 1000 queries, in nearly 68% cases, there exist one or more products which are more relevant (as per Amazon's own desktop search results) than the product chosen by Alexa.
LeSICiN: A Heterogeneous Graph-based Approach for Automatic Legal Statute Identification from Indian Legal Documents
The task of Legal Statute Identification (LSI) aims to identify the legal statutes that are relevant to a given description of Facts or evidence of a legal case.
Incorporating Domain Knowledge for Extractive Summarization of Legal Case Documents
Automatic summarization of legal case documents is an important and practical challenge.
When the Umpire is also a Player: Bias in Private Label Product Recommendations on E-commerce Marketplaces
Along a number of our proposed bias measures, we find that the sponsored recommendations are significantly more biased toward Amazon private label products compared to organic recommendations.
Fairness for Whom? Understanding the Reader's Perception of Fairness in Text Summarization
Moreover, standard ROUGE evaluation metrics are unable to quantify the perceived (un)fairness of the summaries.
An Unsupervised Normalization Algorithm for Noisy Text: A Case Study for Information Retrieval and Stance Detection
To enable tasks such as search/retrieval and classification over all the available data, we need robust algorithms for text normalization, i. e., for cleaning different kinds of noise in the text.
Automatic Charge Identification from Facts: A Few Sentence-Level Charge Annotations is All You Need
Additionally, we propose a novel model that utilizes sentence-level charge labels as an auxiliary task, coupled with the main task of document-level charge identification in a multi-task learning framework.
Machine Learning assisted Chimera and Solitary states in Networks
First, the chimera states (solitary states) are engineered by establishing delays in the neighboring links of a node (the interlayer links) in a 2-D lattice (multiplex network) of oscillators.
ZSCRGAN: A GAN-based Expectation Maximization Model for Zero-Shot Retrieval of Images from Textual Descriptions
Most existing algorithms for cross-modal Information Retrieval are based on a supervised train-test setup, where a model learns to align the mode of the query (e. g., text) to the mode of the documents (e. g., images) from a given training set.
Utilizing Microblogs for Assisting Post-Disaster Relief Operations via Matching Resource Needs and Availabilities
The few prior works that attempted matching only considered the resources, and no attempt has been made to understand other aspects of needs/availabilities that are essential for matching in practice.
Stance Detection in Web and Social Media: A Comparative Study
Online forums and social media platforms are increasingly being used to discuss topics of varying polarities where different people take different stances.
Hier-SPCNet: A Legal Statute Hierarchy-based Heterogeneous Network for Computing Legal Case Document Similarity
We propose to augment the PCNet with the hierarchy of legal statutes, to form a heterogeneous network Hier-SPCNet, having citation links between case documents and statutes, as well as citation and hierarchy links among the statutes.
NARMADA: Need and Available Resource Managing Assistant for Disasters and Adversities
Although a lot of research has been done on utilising Online Social Media during disasters, there exists no system for a specific task that is critical in a post-disaster scenario -- identifying resource-needs and resource-availabilities in the disaster-affected region, coupled with their subsequent matching.
Methods for Computing Legal Document Similarity: A Comparative Study
Computing similarity between two legal documents is an important and challenging task in the domain of Legal Information Retrieval.
Identification of Chimera using Machine Learning
The oblique random forest with null space regularization achieved consistent performance (more than $83\%$ accuracy) across different dynamical models while the auto-encoder based random vector functional link neural network showed relatively lower performance.
Identification of Rhetorical Roles of Sentences in Indian Legal Judgments
Automatically understanding the rhetorical roles of sentences in a legal case judgement is an important problem to solve, since it can help in several downstream tasks like summarization of legal judgments, legal search, and so on.
A Network-centric Framework for Auditing Recommendation Systems
In this work, we propose a novel network-centric framework which is not only able to quantify various static properties of RSs, but also is able to quantify dynamic properties such as how likely RSs are to lead to polarization or segregation of information among their users.
Summarizing User-generated Textual Content: Motivation and Methods for Fairness in Algorithmic Summaries
Specifically, considering that an extractive summarization algorithm selects a subset of the textual units (e. g. microblogs) in the original data for inclusion in the summary, we investigate whether this selection is fair or not.
