HHU at SemEval-2016 Task 1: Multiple Approaches to Measuring Semantic Textual Similarity

Liebeck, Matthias; Pollack, Philipp; Modaresi, Pashutan; Conrad, Stefan

doi:10.18653/v1/s16-1090

Cited by 6 publications

(3 citation statements)

References 14 publications

(11 reference statements)

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“…After filtering out, based on the mentioned entities, claims with non-scientific content (i.e., 62.3% of the total claims), we end up with a final set of ˜4𝐾 scientific claims, out of which 79.8% has been determined to be False, and 20.2% has been determined to be True. We relate claims by computing their Semantic Textual Similarity [31] and setting an appropriate threshold (0.9 in our experiments).…”

Section: Enhanced Fact-checking Contextmentioning

confidence: 99%

SciClops: Detecting and Contextualizing Scientific Claims for Assisting Manual Fact-Checking

Smeros,

Castillo,

Aberer

2021

Preprint

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This paper describes SciClops, a method to help combat online scientific misinformation. Although automated fact-checking methods have gained significant attention recently, they require pre-existing ground-truth evidence, which, in the scientific context, is sparse and scattered across a constantly-evolving scientific literature. Existing methods do not exploit this literature, which can effectively contextualize and combat science-related fallacies. Furthermore, these methods rarely require human intervention, which is essential for the convoluted and critical domain of scientific misinformation.SciClops involves three main steps to process scientific claims found in online news articles and social media postings: extraction, clustering, and contextualization. First, the extraction of scientific claims takes place using a domain-specific, fine-tuned transformer model. Second, similar claims extracted from heterogeneous sources are clustered together with related scientific literature using a method that exploits their content and the connections among them. Third, check-worthy claims, broadcasted by popular yet unreliable sources, are highlighted together with an enhanced factchecking context that includes related verified claims, news articles, and scientific papers. Extensive experiments show that SciClops tackles sufficiently these three steps, and effectively assists nonexpert fact-checkers in the verification of complex scientific claims, outperforming commercial fact-checking systems.

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Section: Enhanced Fact-checking Contextmentioning

confidence: 99%

SciClops: Detecting and Contextualizing Scientific Claims for Assisting Manual Fact-Checking

Smeros,

Castillo,

Aberer

2021

Preprint

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“…This is a popular task in the International Workshop on Semantic Evaluation (SemEval). Three approaches that are part of many proposed methods over the last few years include: (i) surface-level similarity (e.g., similarity between sets or sequences of words or named entities in the two documents); (ii) context similarity (e.g., similarity between document representations); and (iii) topical similarity [26,38].…”

Section: Indicator Extraction Techniquesmentioning

confidence: 99%

SciLens: Evaluating the Quality of Scientific News Articles Using Social Media and Scientific Literature Indicators

Smeros

Castillo

Aberer

2019

The World Wide Web Conference

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This paper describes, develops, and validates SciLens, a method to evaluate the quality of scientific news articles. The starting point for our work are structured methodologies that define a series of quality aspects for manually evaluating news. Based on these aspects, we describe a series of indicators of news quality. According to our experiments, these indicators help non-experts evaluate more accurately the quality of a scientific news article, compared to nonexperts that do not have access to these indicators. Furthermore, SciLens can also be used to produce a completely automated quality score for an article, which agrees more with expert evaluators than manual evaluations done by non-experts. One of the main elements of SciLens is the focus on both content and context of articles, where context is provided by (1) explicit and implicit references on the article to scientific literature, and (2) reactions in social media referencing the article. We show that both contextual elements can be valuable sources of information for determining article quality. The validation of SciLens, done through a combination of expert and non-expert annotation, demonstrates its effectiveness for both semi-automatic and automatic quality evaluation of scientific news.

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“…Most solutions include an ensemble of modules that employs features coming from different unit sizes and depths. More recent approaches generally include the word embedding-based similarity (Liebeck et al, 2016;Brychcín and Svoboda, 2016) as a component of the final ensemble. The top performing team in 2016 (Rychalska et al, 2016) uses an ensemble of multiple modules including recursive autoencoders with WordNet and monolingual aligner (Sultan et al, 2016).…”

Section: Related Workmentioning

confidence: 99%

Neobility at SemEval-2017 Task 1: An Attention-based Sentence Similarity Model

Zhuang¹,

Chang²

2017

Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

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This paper describes a neural-network model which performed competitively (top 6) at the SemEval 2017 cross-lingual Semantic Textual Similarity (STS) task. Our system employs an attention-based recurrent neural network model that optimizes the sentence similarity. In this paper, we describe our participation in the multilingual STS task which measures similarity across English, Spanish, and Arabic.

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HHU at SemEval-2016 Task 1: Multiple Approaches to Measuring Semantic Textual Similarity

Cited by 6 publications

References 14 publications

SciClops: Detecting and Contextualizing Scientific Claims for Assisting Manual Fact-Checking

SciClops: Detecting and Contextualizing Scientific Claims for Assisting Manual Fact-Checking

SciLens: Evaluating the Quality of Scientific News Articles Using Social Media and Scientific Literature Indicators

Neobility at SemEval-2017 Task 1: An Attention-based Sentence Similarity Model

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