# Centroid-based Text Summarization through Compositionality of Word Embeddings

The textual similarity is a crucial aspect for many extractive text summarization methods.

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# Readers vs. Writers vs. Texts: Coping with Different Perspectives of Text Understanding in Emotion Annotation

We here examine how different perspectives of understanding written discourse, like the reader{'}s, the writer{'}s or the text{'}s point of view, affect the quality of emotion annotations.

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# A Consolidated Open Knowledge Representation for Multiple Texts

We propose to move from Open Information Extraction (OIE) ahead to Open Knowledge Representation (OKR), aiming to represent information conveyed jointly in a set of texts in an open text-based manner.

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# Character-based Neural Embeddings for Tweet Clustering

In this paper we show how the performance of tweet clustering can be improved by leveraging character-based neural networks.

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# The BECauSE Corpus 2.0: Annotating Causality and Overlapping Relations

Language of cause and effect captures an essential component of the semantics of a text.

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# An open-source tool for negation detection: a maximum-margin approach

This paper presents an open-source toolkit for negation detection.

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# Finding Good Conversations Online: The Yahoo News Annotated Comments Corpus

This work presents a dataset and annotation scheme for the new task of identifying {}good{''} conversations that occur online, which we call ERICs: Engaging, Respectful, and/or Informative Conversations.

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# LSDSem 2017: Exploring Data Generation Methods for the Story Cloze Test

The Story Cloze test is a recent effort in providing a common test scenario for text understanding systems.

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# Semi-Automated Resolution of Inconsistency for a Harmonized Multiword Expression and Dependency Parse Annotation

This paper presents a methodology for identifying and resolving various kinds of inconsistency in the context of merging dependency and multiword expression (MWE) annotations, to generate a dependency treebank with comprehensive MWE annotations.

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# Social Bias in Elicited Natural Language Inferences

We analyze the Stanford Natural Language Inference (SNLI) corpus in an investigation of bias and stereotyping in NLP data.

3