WORKSHOP REPORTACM SIGIR Workshop on Mathematical/Formal Methods inInformation Retrieval
MF/IR 2005
Salvador, Brazil
August 15-19, 2005

Sándor Dominich, Iadh Ounis, Jian-Yun Nie

1Introduction

The previous five MF/IR workshops showed that the mathematical/formal results achieved inInformation Retrieval (IR) could be organized into a coherent theoretical framework, that theybrought new knowledge to IR, and that mathematical/formal research in IR can stand as a
specialized research area of IR. Therefore the purpose of the MF/IR 2005 was to promote
discussion and interaction among those with theoretical and applicative research interests inmathematical/formal aspects of Information Retrieval coming from a large spectrum of different
IR fields, and also at being a forum for the presentation of both theoretical and applicative results(e.g., foundational issues; description and/or integration of models; retrieval applications;mathematical/formal techniques, properties and structures in IR; existing and/or new theories andtheoretical aspects, interdisciplinary approaches) using formal and mathematical approaches likeSets, Vectors, Similarity Functions, Probability, Algebra, Topology, Metric Spaces, Geometry,Logics, Graph Theory.

2Papers

The event began with an opening talk given by Jian-Yun Nie on implementing logical retrieval
models using language models. The event continued with the presentation of the following
papers:"Parsimonious Translation Models for Information Retrieval" by Seung-Hoon Na, In-Su Kang,
Jong-Hyeok Lee.

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A modeling approach has a critical problem estimating a query model, which is theprobabilistic model that encodes the user’s information need. For query expansion in initial
retrieval, the translation model had been proposed to involve term co-occurrence statistics.
However, the translation model was difficult to apply, because term co-occurrence statistics must
be constructed in the offline time. In large collections, constructing such a large matrix of termco-occurrences statistics prohibitively increases time and space complexity. More seriously,
reliable retrieval performance cannot be guaranteed because the translation model may comprisenoisy non-topical terms in documents. To resolve these problems, this paper investigates aneffective method to construct co-occurrence statistics and eliminate noisy terms by employing aparsimonious translation model. The parsimonious translation model is a compact version of atranslation model that can reduce the number of terms containing non-zero probabilities by
eliminating non-topical terms in documents. Through experimentation on seven different testcollections, the authors show that the query model estimated from the parsimonious translation
model significantly outperforms not only baseline language modeling but also non-parsimonious
models.
"Extracting Template for Knowledge-based Question-Answering Using Conditional Random
Fields" by Changki Lee, Ji-Hyun Wang, Hyeon-Jin Kim, Myung-Gil Jang .
In this paper, the authors present an information extraction system that extracts templateelements for a question answering (QA) system in the domain of encyclopaedia. They use
Conditional Random Fields to extract templates from the texts of an encyclopedia. Using theproposed approach, the authors could achieve a 74.89% precision and a 55.77% F1 in thetemplate extraction. In the question classification, they could archive an 83.6% precision and a65.4% recall. Finally, in the Knowledge based QA (including template extraction procedure),
they could archive an 81.3% precision and a 33.3% recall. The result demonstrated that theapproach is feasible and effective for template extraction for QA.
"Searching the Future" by Ricardo Baeza-Yates
In this paper the author presents a new retrieval problem: future retrieval. The idea is touse news information to obtain future possible events and then search events related to ourcurrent (or future) information needs. In other words, the approach includes time as a formal
attribute for a document. The author presents a simple ranking model based on time segments, aprototype for it and some special examples. This work also poses new challenges for natural
language processing, information extraction, and answer evaluation.

3Discussion, conclusion

Due to the a low number of papers, the closing discussion allowed detailed interaction between
participants.
The MF/IR 2005 organisers were Sándor Dominich (University of Veszprem, Hungary), andIadh Ounis (University of Glasgow, Scotland, U.K.), who, on behalf of MF/IR 2005 would liketo thank the program committee for their help and time, and all authors and participants forwriting and presenting papers as well as attending this event. They also would like to thank JianYun Nie for the keynote talk, and ACM SIGIR for making this event possible.

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