Extending local features with contextual information in graph kernels

Graph kernels are usually defined in terms of simpler kernels over local substructures of the original graphs. Different kernels consider different types of substructures. However, in some cases they have similar predictive performances, probably because the substructures can be interpreted as approximations of the subgraphs they induce. In this paper, we propose to associate to each feature a piece of information about the context in which the feature appears in the graph. A substructure appearing in two different graphs will match only if it appears with the same context in both graphs. We propose a kernel based on this idea that considers trees as substructures, and where the contexts are features too. The kernel is inspired from the framework in [6], even if it is not part of it. We give an efficient algorithm for computing the kernel and show promising results on real-world graph classification datasets.Comment: To appear in ICONIP 201

Prta: A System to Support the Analysis of Propaganda Techniques in the News

Recent events, such as the 2016 US Presidential Campaign, Brexit and the COVID-19 "infodemic", have brought into the spotlight the dangers of online disinformation. There has been a lot of research focusing on fact-checking and disinformation detection. However, little attention has been paid to the specific rhetorical and psychological techniques used to convey propaganda messages. Revealing the use of such techniques can help promote media literacy and critical thinking, and eventually contribute to limiting the impact of "fake news" and disinformation campaigns.Prta (Propaganda Persuasion Techniques Analyzer) allows users to explore the articles crawled on a regular basis by highlighting the spans in which propaganda techniques occur and to compare them on the basis of their use of propaganda techniques. The system further reports statistics about the use of such techniques, overall and over time, or according to filtering criteria specified by the user based on time interval, keywords, and/or political orientation of the media. Moreover, it allows users to analyze any text or URL through a dedicated interface or via an API. The system is available online: https://www.tanbih.org/prta

Thread-level information for comment classification in community question answering

Community Question Answering (cQA) is a new application of QA in social contexts (e.g., fora). It presents new interesting challenges and research directions, e.g., exploiting the dependencies between the different comments of a thread to select the best answer for a given question. In this paper, we explored two ways of modeling such dependencies: (i) by designing specific features looking globally at the thread; and (ii) by applying structure prediction models. We trained and evaluated our models on data from SemEval-2015 Task 3 on Answer Selection in cQA. Our experiments show that: (i) the thread-level features consistently improve the performance for a variety of machine learning models, yielding state-of-the-art results; and (ii) sequential dependencies between the answer labels captured by structured prediction models are not enough to improve the results, indicating that more information is needed in the joint model

Overview of the CLEF-2018 CheckThat! Lab on Automatic Identification and Verification of Political Claims. Task 1: Check-Worthiness

We present an overview of the CLEF-2018 CheckThat! Lab on Automatic Identification and Verification of Political Claims, with focus on Task 1: Check-Worthiness. The task asks to predict which claims in a political debate should be prioritized for fact-checking. In particular, given a debate or a political speech, the goal was to produce a ranked list of its sentences based on their worthiness for fact checking. We offered the task in both English and Arabic, based on debates from the 2016 US Presidential Campaign, as well as on some speeches during and after the campaign. A total of 30 teams registered to participate in the Lab and seven teams actually submitted systems for Task 1. The most successful approaches used by the participants relied on recurrent and multi-layer neural networks, as well as on combinations of distributional representations, on matchings claims' vocabulary against lexicons, and on measures of syntactic dependency. The best systems achieved mean average precision of 0.18 and 0.15 on the English and on the Arabic test datasets, respectively. This leaves large room for further improvement, and thus we release all datasets and the scoring scripts, which should enable further research in check-worthiness estimation

Overview of the CLEF-2018 CheckThat! Lab on Automatic Identification and Verification of Political Claims. Task 2: Factuality

We present an overview of the CLEF-2018 CheckThat! Lab on Automatic Identification and Verification of Political Claims, with focus on Task 2: Factuality. The task asked to assess whether a given check-worthy claim made by a politician in the context of a debate/speech is factually true, half-true, or false. In terms of data, we focused on debates from the 2016 US Presidential Campaign, as well as on some speeches during and after the campaign (we also provided translations in Arabic), and we relied on comments and factuality judgments from factcheck.org and snopes.com, which we further refined manually. A total of 30 teams registered to participate in the lab, and five of them actually submitted runs. The most successful approaches used by the participants relied on the automatic retrieval of evidence from the Web. Similarities and other relationships between the claim and the retrieved documents were used as input to classifiers in order to make a decision. The best-performing official submissions achieved mean absolute error of .705 and .658 for the English and for the Arabic test sets, respectively. This leaves plenty of room for further improvement, and thus we release all datasets and the scoring scripts, which should enable further research in fact-checking

Overview of the CLEF-2018 checkthat! lab on automatic identification and verification of political claims

We present an overview of the CLEF-2018 CheckThat! Lab on Automatic Identification and Verification of Political Claims. In its starting year, the lab featured two tasks. Task 1 asked to predict which (potential) claims in a political debate should be prioritized for fact-checking; in particular, given a debate or a political speech, the goal was to produce a ranked list of its sentences based on their worthiness for fact-checking. Task 2 asked to assess whether a given check-worthy claim made by a politician in the context of a debate/speech is factually true, half-true, or false. We offered both tasks in English and in Arabic. In terms of data, for both tasks, we focused on debates from the 2016 US Presidential Campaign, as well as on some speeches during and after the campaign (we also provided translations in Arabic), and we relied on comments and factuality judgments from factcheck.org and snopes.com, which we further refined manually. A total of 30 teams registered to participate in the lab, and 9 of them actually submitted runs. The evaluation results show that the most successful approaches used various neural networks (esp. for Task 1) and evidence retrieval from the Web (esp. for Task 2). We release all datasets, the evaluation scripts, and the submissions by the participants, which should enable further research in both check-worthiness estimation and automatic claim verification

Crop residue grazing and tillage systems effects on soil physical properties and corn (Zea Mays L.) performance

Crop-livestock systems under no till (NT) could negatively affect soil physical properties and crop performance, due to the additive effects of reduced soil cover and cattle trampling due to livestock grazing, and the absence of tillage. We evaluated the effects of four grazing strategies and of a shallow tillage (ST) on soil physical properties and corn (Zea mays L.) performance for a mollisol after 15 years under crop-livestock systems under NT in Argentina. Grazing strategies evaluated were: closure (C), one grazing (OG), high stocking rate (HR) and farmer's management (FM), and the tillage systems were: NT and ST. Bulk density (BD), penetration resistance (PR), hydraulic conductivity (ks), plant population, surface root distribution, aboveground dry matter accumulation, aboveground total N (TN) accumulation and corn yield were evaluated. High stocking rate and FM increased RP. On the other hand, ST decreased PR and BD and increased ks. Corn yield was higher under ST than under NT, and under HR than under the other grazing strategies. Total N accumulation was higher under HR than under the rest of grazing strategies. Rational grazing management and use of tillage systems on resilient soils could have prevented soil physical properties be affected beyond critical thresholds.Facultad de Ciencias Agrarias y Forestale

Crop residue grazing and tillage systems effects on soil physical properties and corn (Zea Mays L.) performance

Crop-livestock systems under no till (NT) could negatively affect soil physical properties and crop performance, due to the additive effects of reduced soil cover and cattle trampling due to livestock grazing, and the absence of tillage. We evaluated the effects of four grazing strategies and of a shallow tillage (ST) on soil physical properties and corn (Zea mays L.) performance for a mollisol after 15 years under crop-livestock systems under NT in Argentina. Grazing strategies evaluated were: closure (C), one grazing (OG), high stocking rate (HR) and farmer's management (FM), and the tillage systems were: NT and ST. Bulk density (BD), penetration resistance (PR), hydraulic conductivity (ks), plant population, surface root distribution, aboveground dry matter accumulation, aboveground total N (TN) accumulation and corn yield were evaluated. High stocking rate and FM increased RP. On the other hand, ST decreased PR and BD and increased ks. Corn yield was higher under ST than under NT, and under HR than under the other grazing strategies. Total N accumulation was higher under HR than under the rest of grazing strategies. Rational grazing management and use of tillage systems on resilient soils could have prevented soil physical properties be affected beyond critical thresholds.Facultad de Ciencias Agrarias y Forestale

The CLEF-2023 CheckThat! Lab: Checkworthiness, Subjectivity, Political Bias, Factuality, and Authority

The five editions of the CheckThat! lab so far have focused on the main tasks of the information verification pipeline: check-worthiness, evidence retrieval and pairing, and verification. The 2023 edition of the lab zooms into some of the problems and---for the first time---it offers five tasks in seven languages (Arabic, Dutch, English, German, Italian, Spanish, and Turkish): Task 1 asks to determine whether an item, text or a text plus an image, is check-worthy; Task 2 requires to assess whether a text snippet is subjective or not; Task 3 looks for estimating the political bias of a document or a news outlet; Task 4 requires to determine the level of factuality of a document or a news outlet; and Task 5 is about identifying authorities that should be trusted to verify a contended claim

Методологія вітчизняної кримінально-правової науки: становлення історичного методу та проблеми наступності

Аналізуються методологічні проблеми кримінально-правової науки, зокрема процес становлення історичного методу та його ролі у вивченні питань наступності в кримінальному праві.Анализируются методологические проблемы уголовно-правовой науки, в частности процесс становления исторического метода и его роли в изучении вопросов преемственности в уголовном праве.The paper is devoted to an analysis of methodological problems of a criminal legal sci­ence, namely, an establishing process of historical method and its role in studying succession issues in a criminal law