Information Access in a Multilingual World: Transitioning from Research to Real-World Applications

Multilingual Information Access (MLIA) is at a turning point wherein substantial real-world applications are being introduced after fifteen years of research into cross-language information retrieval, question answering, statistical machine translation and named entity recognition. Previous workshops on this topic have focused on research and small- scale applications. The focus of this workshop was on technology transfer from research to applications and on what future research needs to be done which facilitates MLIA in an increasingly connected multilingual world

Classification of research papers using citation links and citation types: Towards automatic review article generation.

We are investigating automatic generation of a review (or survey) article in a specific subject domain. In a research paper, there are passages where the author describes the essence of a cited paper and the differences between the current paper and the cited paper (we call them citing areas). These passages can be considered as a kind of summary of the cited paper from the current author's viewpoint. We can know the state of the art in a specific subject domain from the collection of citing areas. FUrther, if these citing areas are properly classified and organized, they can act 8.', a kind of a review article. In our previous research, we proposed the automatic extraction of citing areas. Then, with the information in the citing areas, we automatically identified the types of citation relationships that indicate the reasons for citation (we call them citation types). Citation types offer a useful clue for organizing citing areas. In addition, to support writing a review article, it is necessary to take account of the contents of the papers together with the citation links and citation types. In this paper, we propose several methods for classifying papers automatically. We found that our proposed methods BCCT-C, the bibliographic coupling considering only type C citations, which pointed out the problems or gaps in related works, are more effective than others. We also implemented a prototype system to support writing a review article, which is based on our proposed method

Towards Humane Feedback Mechanisms in Exploratory Search

Machine learning (ML) plays a central role in modern information retrieval (IR) systems. We argue that, in IR systems for multi-session exploratory search, there are unexploited opportunities for IR document ranking models to leverage users’ knowledge about the search task to better support users’ search needs. Specifically, we propose a method to enable users to adapt an IR document ranking model according to their information needs, using an interface that supports search strategies and methods for engaging with documents known to be useful when people explore new or complex domains of knowledge. We also discuss the major challenges in creating human-centered machine learning models and interfaces for exploratory search

Learning Strategies for Robust Argument Mining:An Analysis of Variations in Language and Domain

Argument mining has typically been researched for specific corpora belonging to concrete languages and domains independently in each research work. Human argumentation, however, has domain- and language-dependent linguistic features that determine the content and structure of arguments. Also, when deploying argument mining systems in the wild, we might not be able to control some of these features. Therefore, an important aspect that has not been thoroughly investigated in the argument mining literature is the robustness of such systems to variations in language and domain. In this paper, we present a complete analysis across three different languages and three different domains that allow us to have a better understanding on how to leverage the scarce available corpora to design argument mining systems that are more robust to natural language variations.</p

Search as learning (SAL) workshop 2016

The "Search as Learning" (SAL) workshop is focused on an area within the information retrieval fi