The position that has been advertised before has not been filled and is re-opened. See this page for the application instructions (remember to specify topic #9 Conversational AI for information access and retrieval as your preference). Application deadline: September 30, 2018.
Joining Google (for a year)
Today, I’ve started by sabbatical at Google, London, UK, where I’ll be working as a Staff Visiting Faculty Researcher on conversational recommendations. I still maintain a 20% position at the University of Stavanger to continue supervising my PhD students.
Table generation and retrieval
Tables are powerful and versatile tools for organizing and presenting data. Tables may be viewed as complex information objects, which summarize existing information in a structured form. Therefore, for many information needs, returning tables as search results may be more helpful to users than serving a ranked list of items (documents or entities). We have a line of work, with Shuo Zhang, centered around utilizing (relational) tables as the unit of retrieval (published at WWW’18 and SIGIR’18). I presented our research at this interesting intersection of entity retrieval and data search in my keynote at the DATA:SEARCH’18 workshop at SIGIR’18 (slides are here).
PhD position in Conversational AI
The University of Stavanger invites applications for a fully funded PhD position.
Intelligent personal assistants and chatbots (such as Siri, Cortana, the Google Assistant, and Amazon Alexa) are being used increasingly more for different purposes, including information access and retrieval. These conversational agents differ from traditional search engines in several important ways. They enable more naturalistic human-like interactions, where search becomes a dialog between the user and the machine. Unlike in traditional search engines, where a user-issued query is answered with a search result page, conversational agents can respond in a variety of ways, for example, asking questions back to the user for clarification.
The successful candidate will work on the design, development, and evaluation of conversational search systems. In particular, the candidate is expected to employ and develop deep learning techniques for understanding natural language requests and generating appropriate responses.
The candidate is required to have a background in machine learning or information retrieval.
For detailed information about the PhD position and the application process, please see here. Remember to specify topic #7 Conversational AI for information access and retrieval as your preference.
Application deadline: February 27, 2018
The DBpedia-Entity v2 Test Collection
The DBpedia-Entity collection a standard test set for entity search. It is meant for evaluating retrieval systems that return a ranked list of entities in response to a free text user query. The first version of the collection (DBpedia-Entity v1) was released in 2013, based on DBpedia v3.7. It was created by assembling search queries from a number of entity-oriented benchmarking campaigns (TREC, INEX, SemSearch, etc.) and mapping relevant results to DBpedia. An updated version of the collection, DBpedia-Entity v2, has been released in 2017, as a result of a collaborative effort between the IAI group of the University of Stavanger, the Norwegian University of Science and Technology, Wayne State University, and Carnegie Mellon University. It has been published at the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’17), where it received a Best Short Paper Honorable Mention Award.
DBpedia-Entity v2 is based on DBpedia version 2015-10 (specifically on the English subset) and comes with graded relevance assessments collected via crowdsourcing. We also report on the performance of a selection of retrieval methods using this collection.
The collection is available here.