paper-with-me

홈 › Papers

The BECauSE Corpus 2.0: Annotating Causality and Overlapping Relations

2017-04-01 · WS 2017 4 · Jesse Dunietz, Lori Levin, Jaime Carbonell

Language of cause and effect captures an essential component of the semantics of a text. However, causal language is also intertwined with other semantic relations, such as temporal precedence and correlation. This makes it difficult to determine when causation is the primary intended meaning. This paper presents BECauSE 2.0, a new version of the BECauSE corpus with exhaustively annotated expressions of causal language, but also seven semantic relations that are frequently co-present with causation. The new corpus shows high inter-annotator agreement, and yields insights both about the linguistic expressions of causation and about the process of annotating co-present semantic relations.

📄 PDF Abstract BibTeX

Code (1)

duncanka/BECauSE 공식 구현

Tasks

Decision Making

Similar Papers 제목 키워드 기반

The Causal News Corpus: Annotating Causal Relations in Event Sentences from News

2022-04-25 · LREC 2022 6 · Fiona Anting Tan, Ali Hürriyetoğlu, Tommaso Caselli, Nelleke Oostdijk 외

Despite the importance of understanding causality, corpora addressing causal relations are limited. There is a discrepancy between existing annotation guidelines of event causality and conventional causality corpora that…

Language Modelling

Annotating Causality in the TempEval-3 Corpus

2014-04-01 · WS 2014 4 · Paramita Mirza, Rachele Sprugnoli, Sara Tonelli, Manuela Speranza
Relation Extraction

Persian Causality Corpus (PerCause) and the Causality Detection Benchmark

2021-06-27 · Zeinab Rahimi, Mehrnoush Shamsfard

Recognizing causal elements and causal relations in text is one of the challenging issues in natural language processing; specifically, in low resource languages such as Persian. In this research we prepare a causality h…

Deep Learning

A Corpus of Deep Argumentative Structures as an Explanation to Argumentative Relations

2017-12-07 · Paul Reisert, Naoya Inoue, Naoaki Okazaki, Kentaro Inui

In this paper, we compose a new task for deep argumentative structure analysis that goes beyond shallow discourse structure analysis. The idea is that argumentative relations can reasonably be represented with a small se…

Catching the Common Cause: Extraction and Annotation of Causal Relations and their Participants

2017-04-01 · WS 2017 4 · Ines Rehbein, Josef Ruppenhofer

In this paper, we present a simple, yet effective method for the automatic identification and extraction of causal relations from text, based on a large English-German parallel corpus. The goal of this effort is to creat…

Language ModelingLanguage Modelling