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

DCU-TCD@LogCLEF 2010: re-ranking document collections and query performance estimation

This paper describes the collaborative participation of Dublin City University and Trinity College Dublin in LogCLEF 2010. Two sets of experiments were conducted. First, different aspects of the TEL query logs were analysed after extracting user sessions of consecutive queries on a topic. The relation between the queries and their length (number of terms) and position (first query or further reformulations) was examined in a session with respect to query performance estimators such as query scope, IDF-based measures, simplified query clarity score, and average inverse document collection frequency. Results of this analysis suggest that only some estimator values show a correlation with query length or position in the TEL logs (e.g. similarity score between collection and query). Second, the relation between three attributes was investigated: the user's country (detected from IP address), the query language, and the interface language. The investigation aimed to explore the influence of the three attributes on the user's collection selection. Moreover, the investigation involved assigning different weights to the three attributes in a scoring function that was used to re-rank the collections displayed to the user according to the language and country. The results of the collection re-ranking show a significant improvement in Mean Average Precision (MAP) over the original collection ranking of TEL. The results also indicate that the query language and interface language have more in uence than the user's country on the collections selected by the users

One-Shot Labeling for Automatic Relevance Estimation

Dealing with unjudged documents ("holes") in relevance assessments is a perennial problem when evaluating search systems with offline experiments. Holes can reduce the apparent effectiveness of retrieval systems during evaluation and introduce biases in models trained with incomplete data. In this work, we explore whether large language models can help us fill such holes to improve offline evaluations. We examine an extreme, albeit common, evaluation setting wherein only a single known relevant document per query is available for evaluation. We then explore various approaches for predicting the relevance of unjudged documents with respect to a query and the known relevant document, including nearest neighbor, supervised, and prompting techniques. We find that although the predictions of these One-Shot Labelers (1SL) frequently disagree with human assessments, the labels they produce yield a far more reliable ranking of systems than the single labels do alone. Specifically, the strongest approaches can consistently reach system ranking correlations of over 0.86 with the full rankings over a variety of measures. Meanwhile, the approach substantially increases the reliability of t-tests due to filling holes in relevance assessments, giving researchers more confidence in results they find to be significant. Alongside this work, we release an easy-to-use software package to enable the use of 1SL for evaluation of other ad-hoc collections or systems.Comment: SIGIR 202

A Survey on Automated Fact-Checking

Fact-checking has become increasingly important due to the speed with which both information and misinformation can spread in the modern media ecosystem. Therefore, researchers have been exploring how factchecking can be automated, using techniques based on natural language processing, machine learning, knowledge representation, and databases to automatically predict the veracity of claims. In this paper, we survey automated fact-checking stemming from natural language processing, and discuss its connections to related tasks and disciplines. In this process, we present an overview of existing datasets and models, aiming to unify the various definitions given and identify common concepts. Finally, we highlight challenges for future research

XSnippets : exploring semi-structured data via snippets

Users are usually not familiar with the content and structure of the data when they explore the data source. However, to improve the exploration usability, they need some primary hints about the data source. These hints should represent the overall picture of the data source and include the trending issues that can be extracted from the query log. In this paper, we propose a two-phase interactive exploratory search framework for the clueless users that exploits the snippets for conducting the search on the XML data. In the first phase, we present the primary snippets that are generated from the keywords of the query log to start the exploration. To retrieve the primary snippets, we develop an A* search-based technique on the keyword space of the query log. To improve the performance of computations, we store the primary snippet computations in an index data structure to reuse it for the next steps. In the second phase, we exploit the co-occurring content of the snippets to generate more specific snippets with the user interaction. To expedite the performance, we design two pruning techniques called inter-snippet and intra-snippet pruning to stop unnecessary computations. Finally, we discuss a termination condition that checks the cardinality of the snippets to stop the interactive phase and present the final Top-l snippets to the user. Our experiments on real datasets verify the effectiveness and efficiency of the proposed framework. © 2019 Elsevier B.V

What Makes a Top-Performing Precision Medicine Search Engine? Tracing Main System Features in a Systematic Way

From 2017 to 2019 the Text REtrieval Conference (TREC) held a challenge task on precision medicine using documents from medical publications (PubMed) and clinical trials. Despite lots of performance measurements carried out in these evaluation campaigns, the scientific community is still pretty unsure about the impact individual system features and their weights have on the overall system performance. In order to overcome this explanatory gap, we first determined optimal feature configurations using the Sequential Model-based Algorithm Configuration (SMAC) program and applied its output to a BM25-based search engine. We then ran an ablation study to systematically assess the individual contributions of relevant system features: BM25 parameters, query type and weighting schema, query expansion, stop word filtering, and keyword boosting. For evaluation, we employed the gold standard data from the three TREC-PM installments to evaluate the effectiveness of different features using the commonly shared infNDCG metric.Comment: Accepted for SIGIR2020, 10 page