Search Results for author:
Found 15 papers, 2 papers with code
Tree-based Focused Web Crawling with Reinforcement Learning
A focused crawler aims at discovering as many web pages relevant to a target topic as possible, while avoiding irrelevant ones.
A study of text representations in Hate Speech Detection
The pervasiveness of the Internet and social media have enabled the rapid and anonymous spread of Hate Speech content on microblogging platforms such as Twitter.
MultiLing 2017 Overview
In this brief report we present an overview of the MultiLing 2017 effort and workshop, as implemented within EACL 2017.
A Graph-based Text Similarity Measure That Employs Named Entity Information
Text comparison is an interesting though hard task, with many applications in Natural Language Processing.
NOMAD: Linguistic Resources and Tools Aimed at Policy Formulation and Validation
The NOMAD project (Policy Formulation and Validation through non Moderated Crowd-sourcing) is a project that supports policy making, by providing rich, actionable information related to how citizens perceive different policies.
The Summary Evaluation Task in the MultiLing - RANLP 2019 Workshop
This report covers the summarization evaluation task, proposed to the summarization community via the MultiLing 2019 Workshop of the RANLP 2019 conference.
A Study on Game Review Summarization
Game reviews have constituted a unique means of interaction between players and companies for many years.
Social Web Observatory: A Platform and Method for Gathering Knowledge on Entities from Different Textual Sources
Within this work we describe a framework for the collection and summarization of information from the Web in an entity-driven manner.
The Financial Narrative Summarisation Shared Task (FNS 2020)
This paper presents the results and findings of the Financial Narrative Summarisation shared task (FNS 2020) on summarising UK annual reports.
Developing Machine-Learned Potentials for Coarse-Grained Molecular Simulations: Challenges and Pitfalls
Coarse graining (CG) enables the investigation of molecular properties for larger systems and at longer timescales than the ones attainable at the atomistic resolution.
Investigation of Machine Learning-based Coarse-Grained Mapping Schemes for Organic Molecules
Due to the wide range of timescales that are present in macromolecular systems, hierarchical multiscale strategies are necessary for their computational study.
