Extraction of Natural-Language Dates and Comparison of Dates in Hypothesis and Text to Identify Negative Textual Entailment

We present a system to determine entailment, when a sentence (the text) implies a second sentence (the hypothesis). While some systems use temporal information to decide entailment, no study has measured the effectiveness of temporal features alone in entailment resolution. The system identifies natural-language dates, and precludes entailment solely by comparing dates in the hypothesis and text. Evaluation of date detection on three 800-pair corpora from the Recognising Textual Entailment (RTE) Challenges provided precision of 98.0% (RTE3devmt, 338/345), 96.6% (RTE3test, 198/205), and 99.7% (RTE1test, 336/337), and recall of 97.7% (RTE3devmt, 338/346), 99.0% (RTE3test, 198/200), and 99.1% (RTE1devmt 336/339). For sentence pairs with years, the proposed method improved entailment accuracy from 33 to 42/72 (RTE3devmt) and from 42 to 44/63 (RTE3test,) which corresponds to an overall improvement of 1.1% (RTE3devmt) and 0.1% (RTE3test). Our analysis suggests that matching temporal information with an event would further increase the entailment accuracy

Finding answers to questions, in text collections or web, in open domain or specialty domains

International audienceThis chapter is dedicated to factual question answering, i.e. extracting precise and exact answers to question given in natural language from texts. A question in natural language gives more information than a bag of word query (i.e. a query made of a list of words), and provides clues for finding precise answers. We will first focus on the presentation of the underlying problems mainly due to the existence of linguistic variations between questions and their answerable pieces of texts for selecting relevant passages and extracting reliable answers. We will first present how to answer factual question in open domain. We will also present answering questions in specialty domain as it requires dealing with semi-structured knowledge and specialized terminologies, and can lead to different applications, as information management in corporations for example. Searching answers on the Web constitutes another application frame and introduces specificities linked to Web redundancy or collaborative usage. Besides, the Web is also multilingual, and a challenging problem consists in searching answers in target language documents other than the source language of the question. For all these topics, we present main approaches and the remaining problems

Automatic fake news detection on Twitter

Nowadays, information is easily accessible online, from articles by reliable news agencies to reports from independent reporters, to extreme views published by unknown individuals. Moreover, social media platforms are becoming increasingly important in everyday life, where users can obtain the latest news and updates, share links to any information they want to spread, and post their own opinions. Such information may create difficulties for information consumers as they try to distinguish fake news from genuine news. Indeed, users may not be necessarily aware that the information they encounter is false and may not have the time and effort to fact-check all the claims and information they encounter online. With the amount of information created and shared daily, it is also not feasible for journalists to manually fact-check every published news article, sentence or tweet. Therefore, an automatic fact-checking system that identifies the check-worthy claims and tweets, and then fact-checks these identified check-worthy claims and tweets can help inform the public of fake news circulating online. Existing fake news detection systems mostly rely on the machine learning models’ computational power to automatically identify fake news. Some researchers have focused on extracting the semantic and contextual meaning from news articles, statements, and tweets. These methods aim to identify fake news by analysing the differences in writing style between fake news and factual news. On the other hand, some researchers investigated using social networks information to detect fake news accurately. These methods aim to distinguish fake news from factual news based on the spreading pattern of news, and the statistical information of the engaging users with the propagated news. In this thesis, we propose a novel end-to-end fake news detection framework that leverages both the textual features and social network features, which can be extracted from news, tweets, and their engaging users. Specifically, our proposed end-to-end framework is able to process a Twitter feed, identify check-worthy tweets and sentences using textual features and embedded entity features, and fact-check the claims using previously unexplored information, such as existing fake news collections and user network embeddings. Our ultimate aim is to rank tweets and claims based on their check-worthiness to focus the available computational power on fact-checking the tweets and claims that are important and potentially fake. In particular, we leverage existing fake news collections to identify recurring fake news, while we explore the Twitter users’ engagement with the check-worthy news to identify fake news that are spreading on Twitter. To identify fake news effectively, we first propose the fake news detection framework (FNDF), which consists of the check-worthiness identification phase and the fact-checking phase. These two phases are divided into three tasks: Phase 1 Task 1: check-worthiness identification task; Phase 2 Task 2: recurring fake news identification task; and Phase 2 Task 3: social network structure-assisted fake news detection task. We conduct experiments on two large publicly available datasets, namely the MM-COVID and the stance detection (SD) datasets. The experimental results show that our proposed framework, FNDF, can indeed identify fake news more effectively than the existing SOTA models, with 23.2% and 4.0% significant increases in F1 scores on the two tested datasets, respectively. To identify the check-worthy tweets and claims effectively, we incorporate embedded entities with language representations to form a vector representation of a given text, to identify if the text is check-worthy or not. We conduct experiments using three publicly available datasets, namely, the CLEF 2019, 2020 CheckThat! Lab check-worthy sentence detection dataset, and the CLEF 2021 CheckThat! Lab check-worthy tweets detection dataset. The experimental results show that combining entity representations and language model representations enhance the language model’s performance in identifying check-worthy tweets and sentences. Specifically, combining embedded entities with the language model results in as much as 177.6% increase in MAP on ranking check-worthy tweets,and a 92.9% increase in ranking check-worthy sentences. Moreover, we conduct an ablation study on the proposed end-to-end framework, FNDF, and show that including a model for identifying check-worthy tweets and claims in our end-to-end framework, can significantly increase the F1 score by as much as 14.7%, compared to not including this model in our framework. To identify recurring fake news effectively, we propose an ensemble model of the BM25 scores and the BERT language model. Experiments conducted on two datasets, namely, the WSDM Cup 2019 Fake News Challenge dataset, and the MM-COVID dataset. Experimental results show that enriching the BERT language model with the BM25 scores can help the BERT model identify fake news significantly more accurately by 4.4%. Moreover, the ablation study on the end-to-end fake news detection framework, FNDF, shows that including the identification of recurring fake news model in our proposed framework results in significant increase in terms of F1 score by as much as 15.5%, compared to not including this task in our framework. To leverage the user network structure in detecting fake news, we first obtain user embed- dings from unsupervised user network embeddings based on their friendship or follower connections on Twitter. Next, we use the user embeddings of the users who engaged with the news to represent a check-worthy tweet/claim, thus predicting whether it is fake news. Our results show that using user network embeddings to represent check-worthy tweets/sentences significantly outperforms the SOTA model, which uses language models to represent the tweets/sentences and complex networks requiring handcrafted features, by 12.0% in terms of the F1 score. Furthermore, including the user network assisted fake news detection model in our end-to-end framework, FNDF, significantly increase the F1 score by as much as 29.3%. Overall, this thesis shows that an end-to-end fake news detection framework, FNDF, that identifies check-worthy tweets and claims, then fact-checks the check-worthy tweets and claims, by identifying recurring fake news and leveraging the social network users’ connections, can effectively identify fake news online

EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020

Welcome to EVALITA 2020! EVALITA is the evaluation campaign of Natural Language Processing and Speech Tools for Italian. EVALITA is an initiative of the Italian Association for Computational Linguistics (AILC, http://www.ai-lc.it) and it is endorsed by the Italian Association for Artificial Intelligence (AIxIA, http://www.aixia.it) and the Italian Association for Speech Sciences (AISV, http://www.aisv.it)

EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020

Welcome to EVALITA 2020! EVALITA is the evaluation campaign of Natural Language Processing and Speech Tools for Italian. EVALITA is an initiative of the Italian Association for Computational Linguistics (AILC, http://www.ai-lc.it) and it is endorsed by the Italian Association for Artificial Intelligence (AIxIA, http://www.aixia.it) and the Italian Association for Speech Sciences (AISV, http://www.aisv.it)