49 research outputs found
Design Patterns for Fusion-Based Object Retrieval
We address the task of ranking objects (such as people, blogs, or verticals)
that, unlike documents, do not have direct term-based representations. To be
able to match them against keyword queries, evidence needs to be amassed from
documents that are associated with the given object. We present two design
patterns, i.e., general reusable retrieval strategies, which are able to
encompass most existing approaches from the past. One strategy combines
evidence on the term level (early fusion), while the other does it on the
document level (late fusion). We demonstrate the generality of these patterns
by applying them to three different object retrieval tasks: expert finding,
blog distillation, and vertical ranking.Comment: Proceedings of the 39th European conference on Advances in
Information Retrieval (ECIR '17), 201
Modeling Documents as Mixtures of Persons for Expert Finding
In this paper we address the problem of searching for knowledgeable
persons within the enterprise, known as the expert finding (or
expert search) task. We present a probabilistic algorithm using the assumption
that terms in documents are produced by people who are mentioned
in them.We represent documents retrieved to a query as mixtures
of candidate experts language models. Two methods of personal language
models extraction are proposed, as well as the way of combining
them with other evidences of expertise. Experiments conducted with the
TREC Enterprise collection demonstrate the superiority of our approach
in comparison with the best one among existing solutions
The right expert at the right time and place: From expertise identification to expertise selection
We propose a unified and complete solution for expert finding in organizations, including not only expertise identification, but also expertise selection functionality. The latter two include the use of implicit and explicit preferences of users on meeting each other, as well as localization and planning as important auxiliary processes. We also propose a solution for privacy protection, which is urgently required in view of the huge amount of privacy sensitive data involved. Various parts are elaborated elsewhere, and we look forward to a realization and usage of the proposed system as a whole
The State-of-the-arts in Focused Search
The continuous influx of various text data on the Web requires search engines to improve their retrieval abilities for more specific information. The need for relevant results to a userās topic of interest has gone beyond search for domain or type specific documents to more focused result (e.g. document fragments or answers to a query). The introduction of XML provides a format standard for data representation, storage, and exchange. It helps focused search to be carried out at different granularities of a structured document with XML markups. This report aims at reviewing the state-of-the-arts in focused search, particularly techniques for topic-specific document retrieval, passage retrieval, XML retrieval, and entity ranking. It is concluded with highlight of open problems
Unsupervised, Efficient and Semantic Expertise Retrieval
We introduce an unsupervised discriminative model for the task of retrieving
experts in online document collections. We exclusively employ textual evidence
and avoid explicit feature engineering by learning distributed word
representations in an unsupervised way. We compare our model to
state-of-the-art unsupervised statistical vector space and probabilistic
generative approaches. Our proposed log-linear model achieves the retrieval
performance levels of state-of-the-art document-centric methods with the low
inference cost of so-called profile-centric approaches. It yields a
statistically significant improved ranking over vector space and generative
models in most cases, matching the performance of supervised methods on various
benchmarks. That is, by using solely text we can do as well as methods that
work with external evidence and/or relevance feedback. A contrastive analysis
of rankings produced by discriminative and generative approaches shows that
they have complementary strengths due to the ability of the unsupervised
discriminative model to perform semantic matching.Comment: WWW2016, Proceedings of the 25th International Conference on World
Wide Web. 201
Learning to Rank Academic Experts in the DBLP Dataset
Expert finding is an information retrieval task that is concerned with the
search for the most knowledgeable people with respect to a specific topic, and
the search is based on documents that describe people's activities. The task
involves taking a user query as input and returning a list of people who are
sorted by their level of expertise with respect to the user query. Despite
recent interest in the area, the current state-of-the-art techniques lack in
principled approaches for optimally combining different sources of evidence.
This article proposes two frameworks for combining multiple estimators of
expertise. These estimators are derived from textual contents, from
graph-structure of the citation patterns for the community of experts, and from
profile information about the experts. More specifically, this article explores
the use of supervised learning to rank methods, as well as rank aggregation
approaches, for combing all of the estimators of expertise. Several supervised
learning algorithms, which are representative of the pointwise, pairwise and
listwise approaches, were tested, and various state-of-the-art data fusion
techniques were also explored for the rank aggregation framework. Experiments
that were performed on a dataset of academic publications from the Computer
Science domain attest the adequacy of the proposed approaches.Comment: Expert Systems, 2013. arXiv admin note: text overlap with
arXiv:1302.041