Corpus Wide Argument Mining—A Working Solution

Abstract

One of the main tasks in argument mining is the retrieval of argumentative content pertaining to a given topic. Most previous work addressed this task by retrieving a relatively small number of relevant documents as the initial source for such content. This line of research yielded moderate success, which is of limited use in a real-world system. Furthermore, for such a system to yield a comprehensive set of relevant arguments, over a wide range of topics, it requires leveraging a large and diverse corpus in an appropriate manner. Here we present a first end-to-end high-precision, corpus-wide argument mining system. This is made possible by combining sentence-level queries over an appropriate indexing of a very large corpus of newspaper articles, with an iterative annotation scheme. This scheme addresses the inherent label bias in the data and pinpoints the regions of the sample space whose manual labeling is required to obtain high-precision among top-ranked candidates.

The workweek is the best time to start a family – A Study of GPT-2 Based Claim Generation

“…A GT is considered plausible if ≥ 70% of the annotators considered it as such. The aver-age inter-annotator Cohen's Kappa obtained in this task is 0.37, which is common for such a subjective task (see, e.g., Ein-Dor et al (2020) and Boltuzic and Snajder (2014)).…”

“…LN55k. This proprietary dataset consists of 55,024 manually curated claims for the 192 topics in the train set of Ein-Dor et al (2020). These claims were extracted from a corpus of some 400 million newspaper articles provided by Lex-isNexis, 2 as done in Ein-Dor et al (2020) for evidence rather than claims.…”

A Large-Scale Dataset for Argument Quality Ranking: Construction and Analysis

“…Computational Argumentation is a rapidly emerging discipline within the Natural Language Processing community (Reed 2016), dealing with various sub-tasks such as argument detection (Lippi and Torroni 2016;Ein-Dor et al 2019), stance detection (Bar-Haim et al 2017) and argument clustering (Reimers et al 2019).…”

ArgumenText: Argument Classification and Clustering in a Generalized Search Scenario

“…Class. Prototype [26] args.me debate portals offline www.args.me [21] ArgumenText generic web crawl online www.argumentsearch.com [4] PerspectroScope curated online sources offline and online www.perspectroscope.com [10] IBM Debater news articles, Wikipedia online not available in (e.g. a web crawl), the ideal AM system should be able to extract all relevant reasoning from previous debates about the topic of interest.…”

“…To date, ArgumenText is the only publicly available argumentative search engine retrieving English and German arguments in realtime from completely uncurated web sources (see Table 1). Recent work in the context of the IBM Debater project [10] presents a similar system extracting arguments from news and Wikipedia articles -however, they only release the Wikipedia portion of the dataset and do not offer a public search engine. The methodological details of our proposed solution to this problem are described in Sect.…”