ANDI @ CONcreTEXT: Predicting Concreteness in Context for English and Italian using Distributional Models and Behavioural Norms (short paper)

A. Rotaru
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引用次数: 3

Abstract

In this paper we describe our participation in the CONcreTEXT task of EVALITA 2020, which involved predicting subjective ratings of concreteness for words presented in context. Our approach, which ranked first in both the English and Italian subtasks, relies on a combination of context-dependent and context-independent distributional models, together with behavioural norms. We show that good results can be obtained for Italian, by first automatically translating the Italian stimuli into English, and then using existing resources for both Italian and English.
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ANDI @ CONcreTEXT:使用分布模型和行为规范预测英语和意大利语语境中的具体性(短文)
在本文中,我们描述了我们对EVALITA 2020的具体任务的参与,该任务涉及预测上下文中呈现的单词的具体程度的主观评分。我们的方法在英语和意大利语子任务中都排名第一,它依赖于上下文依赖和上下文独立分布模型的组合,以及行为规范。我们表明,通过首先自动将意大利语刺激翻译成英语,然后使用意大利语和英语的现有资源,可以获得良好的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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DIACR-Ita @ EVALITA2020: Overview of the EVALITA2020 Diachronic Lexical Semantics (DIACR-Ita) Task QMUL-SDS @ DIACR-Ita: Evaluating Unsupervised Diachronic Lexical Semantics Classification in Italian (short paper) By1510 @ HaSpeeDe 2: Identification of Hate Speech for Italian Language in Social Media Data (short paper) HaSpeeDe 2 @ EVALITA2020: Overview of the EVALITA 2020 Hate Speech Detection Task KIPoS @ EVALITA2020: Overview of the Task on KIParla Part of Speech Tagging
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