22,478 research outputs found
User evaluation of a pilot terminologies server for a distributed multi-scheme environment
The present paper reports on a user-centred evaluation of a pilot terminology service developed as part of the High Level Thesaurus (HILT) project at the Centre for Digital Library Research (CDLR) in the University of Strathclyde in Glasgow. The pilot terminology service was developed as an experimental platform to investigate issues relating to mapping between various subject schemes, namely Dewey Decimal Classification (DDC), Library of Congress Subject Headings (LCSH), the Unesco thesaurus, and the MeSH thesaurus, in order to cater for cross-browsing and cross-searching across distributed digital collections and services. The aim of the evaluation reported here was to investigate users' thought processes, perceptions, and attitudes towards the pilot terminology service and to identify user requirements for developing a full-blown pilot terminology service
Adapting Visual Question Answering Models for Enhancing Multimodal Community Q&A Platforms
Question categorization and expert retrieval methods have been crucial for
information organization and accessibility in community question & answering
(CQA) platforms. Research in this area, however, has dealt with only the text
modality. With the increasing multimodal nature of web content, we focus on
extending these methods for CQA questions accompanied by images. Specifically,
we leverage the success of representation learning for text and images in the
visual question answering (VQA) domain, and adapt the underlying concept and
architecture for automated category classification and expert retrieval on
image-based questions posted on Yahoo! Chiebukuro, the Japanese counterpart of
Yahoo! Answers.
To the best of our knowledge, this is the first work to tackle the
multimodality challenge in CQA, and to adapt VQA models for tasks on a more
ecologically valid source of visual questions. Our analysis of the differences
between visual QA and community QA data drives our proposal of novel
augmentations of an attention method tailored for CQA, and use of auxiliary
tasks for learning better grounding features. Our final model markedly
outperforms the text-only and VQA model baselines for both tasks of
classification and expert retrieval on real-world multimodal CQA data.Comment: Submitted for review at CIKM 201
Semi-automatic annotation process for procedural texts: An application on cooking recipes
Taaable is a case-based reasoning system that adapts cooking recipes to user
constraints. Within it, the preparation part of recipes is formalised as a
graph. This graph is a semantic representation of the sequence of instructions
composing the cooking process and is used to compute the procedure adaptation,
conjointly with the textual adaptation. It is composed of cooking actions and
ingredients, among others, represented as vertices, and semantic relations
between those, shown as arcs, and is built automatically thanks to natural
language processing. The results of the automatic annotation process is often a
disconnected graph, representing an incomplete annotation, or may contain
errors. Therefore, a validating and correcting step is required. In this paper,
we present an existing graphic tool named \kcatos, conceived for representing
and editing decision trees, and show how it has been adapted and integrated in
WikiTaaable, the semantic wiki in which the knowledge used by Taaable is
stored. This interface provides the wiki users with a way to correct the case
representation of the cooking process, improving at the same time the quality
of the knowledge about cooking procedures stored in WikiTaaable
Domain-Specific Web Search with Keyword Spices
Domain-specific web search engines are effective tools for reducing the difficulty in acquiring information from the web. Existing methods for building domain-specific web search engines require human expertise or specific facilities. However, we can build a domain-specific search engine simply by adding domain specific keywords called "keyword spices" to the user's input query and forwarding it to a generalpurpose web search engine. Keyword spices can be effectively discovered from web documents using machine learning technologies. This paper will describe domain-specific web search engines that use keyword spices for locating cooking recipes, restaurants, and used cars. To fully automate the construction of domain-specific search engines, we also present trials of using web pages in an existing web directory as training examples
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