The 3rd International Workshop on Narrative Extraction from Texts (Text2Story’20)

Ricardo Campos, Alipio M. Jorge, Adam Jatowt and Sumit Bhatia

Building upon the success of the two first editions of the workshop ([email protected]’18 and [email protected]’19), and the subsequent special issue hosted at IPM journal, we propose to organize the third edition of the Text2Story Workshop on Narrative Extraction from Texts. Although the understanding of natural language has improved over the last couple of years – with research works emerging on the grounds of information extraction and text mining – the problem of constructing consistent narrative structures is yet to be solved. In the third edition of the workshop, we aim to foster the discussion of recent advances in the link between Information Retrieval (IR) and formal narrative representations from texts. Specifically, we aim to provide a common forum to consolidate the multi-disciplinary efforts and foster discussions to identify the wide-ranging issues related to the narrative extraction task.

Bibliometric-enhanced Information Retrieval: 10th International BIR Workshop

Guillaume Cabanac, Ingo Frommholz and Philipp Mayr

The Bibliometric-enhanced Information Retrieval (BIR) workshop series at ECIR tackles issues related to academic search, at the crossroads between Information Retrieval and Bibliometrics. BIR is a hot topic investigated by both academia (e.g., ArnetMiner, CiteSeerχ, DocEar) and the industry (e.g., Google Scholar, Microsoft Academic Search, Semantic Scholar). We propose a one-day workshop to be held at ECIR 2020 in Lisbon, Portugal.

First Workshop on Algorithmic Bias in Search and Recommendation (ALGO-BIAS 2020)

Ludovico Boratto, Mirko Marras, Stefano Faralli and Giovanni Stilo

Both search and recommendation algorithms provide results based on their relevance for the current user. In order to do so, such a relevance is usually computed by models trained on historical data, which is biased in most cases. Hence, the results produced by these algorithms naturally propagate, and frequently reinforce, biases hidden in the data, consequently strengthening inequalities. Being able to measure, characterize, and mitigate these biases while keeping high effectiveness is a topic of central interest for the information retrieval community. In this workshop, we aim to collect novel contributions in this emerging field and to provide a common ground for interested researchers and practitioners.

Semantic Indexing and Information Retrieval for Health from heterogeneous content types and languages

Francisco Couto and Martin Krallinger

There is an increasing interest in exploiting the vast amount of rapidly growing content related to health by means of information retrieval and deep learning strategies. Health-related content is particularly challenging, due to the very specialized domain language, implicit differences in language characteristics depending on the content type (patient-generated content like discussion forum, blogs and other Internet sources, healthcare documentation and clinical records, professional or scientific publications, clinical practice guidelines, clinical trials documentation, etc.). Moreover, it is also critical to provide search solutions for non-English content as well as cross-language or multilingual IR solutions.

This workshop will be a forum where the community can present and discuss current and future directions for the area based on the experience and results obtained in the BioASQ task. Moreover, the workshop proposal, in addition to the MESINESP ( session, will include an open session covering IR technologies for heterogeneous health-related content open to multiple languages with a particular interest in the exploitation of structured controlled vocabularies and entity linking. Among the proposed topics for this open session are: (1) multilingual and non-English health related IR, concept indexing and text categorization strategies, (2) generation of evaluation resources for health and biomedical document IR strategies, (3) scalability, robustness and reproducibility of health and biomedical IR and text mining resources, (4) use of specialized machine translation and advanced deep learning approaches for improving health related search results, (5) medical Question Answering search tools, (6) retrieval of multilingual health related web-content.