BomJi at SemEval-2018 Task 10: Combining Vector-, Pattern- and Graph-based Information to Identify Discriminative Attributes

04/30/2018
by   Enrico Santus, et al.
0

This paper describes BomJi, a supervised system for capturing discriminative attributes in word pairs (e.g. yellow as discriminative for banana over watermelon). The system relies on an XGB classifier trained on carefully engineered graph-, pattern- and word embedding based features. It participated in the SemEval- 2018 Task 10 on Capturing Discriminative Attributes, achieving an F1 score of 0:73 and ranking 2nd out of 26 participant systems.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/05/2018

Luminoso at SemEval-2018 Task 10: Distinguishing Attributes Using Text Corpora and Relational Knowledge

Luminoso participated in the SemEval 2018 task on "Capturing Discriminat...
research
09/05/2019

Identifying and Explaining Discriminative Attributes

Identifying what is at the center of the meaning of a word and what disc...
research
09/08/2017

CLaC at SemEval-2016 Task 11: Exploring linguistic and psycho-linguistic Features for Complex Word Identification

This paper describes the system deployed by the CLaC-EDLK team to the "S...
research
04/07/2019

A Facial Affect Analysis System for Autism Spectrum Disorder

In this paper, we introduce an end-to-end machine learning-based system ...
research
03/08/2016

The red one!: On learning to refer to things based on their discriminative properties

As a first step towards agents learning to communicate about their visua...
research
08/29/2020

SocCogCom at SemEval-2020 Task 11: Characterizing and Detecting Propaganda using Sentence-Level Emotional Salience Features

This paper describes a system developed for detecting propaganda techniq...
research
11/18/2014

A Unified Semantic Embedding: Relating Taxonomies and Attributes

We propose a method that learns a discriminative yet semantic space for ...

Please sign up or login with your details

Forgot password? Click here to reset