Mostafa Dehghani

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Recently, Entity Linking and Retrieval turned out to be one of the most interesting tasks in Information Extraction due to its various applications. Entity Linking (EL) is the task of detecting mentioned entities in a text and linking them to the corresponding entries of a Knowledge Base. EL is traditionally composed of three major parts: i)<i>spotting</i>,(More)
Despite the impressive improvements achieved by <i>unsupervised</i> deep neural networks in computer vision and NLP tasks, such improvements have not yet been observed in ranking for information retrieval. The reason may be the complexity of the ranking problem, as it is not obvious how to learn from queries and documents when no supervised signal is(More)
Users tend to articulate their complex information needs in only a few keywords, making underspecified statements of request the main bottleneck for retrieval effectiveness. Taking advantage of feedback information is one of the best ways to enrich the query representation, but can also lead to loss of query focus and harm performance in particular when the(More)
Text alignment is a sub-task in the plagiarism detection process. In this paper we discuss our approach to address this problem. Our approach is based on mapping text alignment to the problem of subsequence matching just as previous works. We have prepared a framework, which lets us combine different feature types and different strategies for merging the(More)
There is an ongoing debate on personalization, adapting results to the unique user exploiting a user's personal history, versus customization, adapting results to a group profile sharing one or more characteristics with the user at hand. Personal profiles are often sparse, due to cold start problems and the fact that users typically search for new items or(More)
Transforming the data into a suitable representation is the first key step of data analysis, and the performance of any data oriented method is heavily depending on it. We study questions on how we can best learn representations for textual entities that are: 1) precise, 2) robust against noisy terms, 3) transferable over time, and 4) interpretable by human(More)
This paper presents the University of Amsterdam’s participation in the TREC 2015 Contextual Suggestion Track. Creating e↵ective profiles for both users and contexts is the main key to build an e↵ective contextual suggestion system. To address these issues, we investigate building users’ and groups’ profiles for e↵ective contextual personalization and(More)