ECIR’16 contributions

Last Sunday, Anne Schuth and I gave a tutorial on Living Labs for Online Evaluation. The tutorial’s homepage contains all the slides and reference material.

Experimental evaluation has always been central to Information Retrieval research. The field is increasingly moving towards online evaluation, which involves experimenting with real, unsuspecting users in their natural task environments, a so-called living lab. Specifically, with the recent introduction of the Living Labs for IR Evaluation initiative at CLEF and the OpenSearch track at TREC, researchers can now have direct access to such labs. With these benchmarking platforms in place, we believe that online evaluation will be an exciting area to work on in the future. This half-day tutorial aims to provide a comprehensive overview of the underlying theory and complement it with practical guidance.

Today, Faegheh Hashibi is presenting our work on the reproducibility of the TAGME Entity Linking System. The full paper and resources for this work are available online.

Among the variety of approaches proposed for entity linking, the TAGME system has gained due attention and is considered a must-have baseline. In this paper, we examine the repeatability, reproducibility, and generalizability of TAGME, by comparing results obtained from its public API with (re)implementations from scratch. We find that the results reported in the paper cannot be repeated due to unavailability of data sources. Part of the results are reproducible only through the provided API, while the rest are not reproducible. We further show that the TAGME approach is generalizable to the task of entity linking in queries. Finally, we provide insights gained during this process and formulate lessons learned to inform future reducibility efforts.

PhD vacancy

I am looking for a PhD student to work on understanding complex information needs.

Web search engines have become remarkably effective in providing appropriate answers to queries that are issued frequently. However, when it comes to complex information needs, often formulated as natural language questions, responses become much less satisfactory (e.g., “Which European universities have active Nobel laureates?”). The goal of this project is to investigate how to improve query understanding and answer retrieval for complex information needs, using massive volumes of unstructured data in combination with knowledge bases. Query understanding entails, among others, determining the type (format) of the answer (single fact, list, answer passage, list of documents, etc.) and identifying the series of processing steps (retrieval, filtering, sorting, aggregation, etc.) required to obtain that answer. If the question is not understood or ambiguous, the system should ask for clarification in an interactive way. This could be done in a conversational manner, similarly to how it is done in commercial personal digital assistants, such as SIRI, Cortana, or Google Now.

The successful applicant would join a team of 2 other PhD students working on the FAETE project.

Details and application instructions can be found here.
Application deadline: April 17, 2016.

Important note: there are multiple projects advertised within the call. You need to indicate that you are applying for this specific project. Feel free to contact me directly for more information.