16,196 research outputs found
Illustrating answers: an evaluation of automatically retrieved illustrations of answers to medical questions
In this paper we discuss and evaluate a method for automatic text illustration, applied to answers to medical questions. Our method for selecting illustrations is based on the idea that similarities between the answers and picture-related text (the picture’s caption or the section/paragraph that includes the picture) can be used as evidence that the picture would be appropriate to illustrate the answer.In a user study, participants rated answer presentations consisting of a textual component and a picture. The textual component was a manually written reference answer; the picture was automatically retrieved by measuring the similarity between the text and either the picture’s caption or its section. The caption-based selection method resulted in more attractive presentations than the section-based method; the caption-based method was also more consistent in selecting informative pictures and showed a greater correlation between user-rated informativeness and the confidence of relevance of the system.When compared to manually selected pictures, we found that automatically selected pictures were rated similarly to decorative pictures, but worse than informative pictures
CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap
After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in
multimedia search engines, we have identified and analyzed gaps within European research effort during our second year.
In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio-
economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown
of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on
requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the
community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our
Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as
National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core
technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research
challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal
challenges
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How can we better understand the mechanisms behind multi-turn information
seeking dialogues? How can we use these insights to design a dialogue system
that does not require explicit query formulation upfront as in question
answering? To answer these questions, we collected observations of human
participants performing a similar task to obtain inspiration for the system
design. Then, we studied the structure of conversations that occurred in these
settings and used the resulting insights to develop a grounded theory, design
and evaluate a first system prototype. Evaluation results show that our
approach is effective and can complement query-based information retrieval
approaches. We contribute new insights about information-seeking behavior by
analyzing and providing automated support for a type of information-seeking
strategy that is effective when the clarity of the information need and
familiarity with the collection content are low
Automatic summarising: factors and directions
This position paper suggests that progress with automatic summarising demands
a better research methodology and a carefully focussed research strategy. In
order to develop effective procedures it is necessary to identify and respond
to the context factors, i.e. input, purpose, and output factors, that bear on
summarising and its evaluation. The paper analyses and illustrates these
factors and their implications for evaluation. It then argues that this
analysis, together with the state of the art and the intrinsic difficulty of
summarising, imply a nearer-term strategy concentrating on shallow, but not
surface, text analysis and on indicative summarising. This is illustrated with
current work, from which a potentially productive research programme can be
developed
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation
This paper surveys the current state of the art in Natural Language
Generation (NLG), defined as the task of generating text or speech from
non-linguistic input. A survey of NLG is timely in view of the changes that the
field has undergone over the past decade or so, especially in relation to new
(usually data-driven) methods, as well as new applications of NLG technology.
This survey therefore aims to (a) give an up-to-date synthesis of research on
the core tasks in NLG and the architectures adopted in which such tasks are
organised; (b) highlight a number of relatively recent research topics that
have arisen partly as a result of growing synergies between NLG and other areas
of artificial intelligence; (c) draw attention to the challenges in NLG
evaluation, relating them to similar challenges faced in other areas of Natural
Language Processing, with an emphasis on different evaluation methods and the
relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118
pages, 8 figures, 1 tabl
Survey on Evaluation Methods for Dialogue Systems
In this paper we survey the methods and concepts developed for the evaluation
of dialogue systems. Evaluation is a crucial part during the development
process. Often, dialogue systems are evaluated by means of human evaluations
and questionnaires. However, this tends to be very cost and time intensive.
Thus, much work has been put into finding methods, which allow to reduce the
involvement of human labour. In this survey, we present the main concepts and
methods. For this, we differentiate between the various classes of dialogue
systems (task-oriented dialogue systems, conversational dialogue systems, and
question-answering dialogue systems). We cover each class by introducing the
main technologies developed for the dialogue systems and then by presenting the
evaluation methods regarding this class
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An intelligent question: answering system for natural language
As applications of information storage and retrieval systems are becoming more widespread, there is an increased need to be able to communicate with these systems in a natural way. Natural Language applications in the 1990s, as well as in the foreseeable future, have more demanding requirements. Current Natural Language Processing approaches alone have proven to be insufficient as they lack to obtain linguistic understanding. A more suitable approach would be to adopt Computational Linguistics theories, such as the Lexical-Functional Grammar (LFG) theory complemented with Artificial Intelligence representation and processing techniques.
A prototype Question-Answering System has been developed. It takes Natural Language parsed interrogatives, produces the Functional and Semantic structures according to the LFG representation. It compares the functional behaviour of verbs and their linguistic associations in a given query with a general Object Model in that specific domain. It will then attempt to deduce more information from the given processed text and represent it for possible queries. The structural rules of the LFG and the deduced common-sense domain specific information resolve most of the common ambiguities found in Natural Languages and enhance the understanding ability of the proposed prototype.
The LFG theory has been adopted and extended: (i) to examine the constituents of the theoretical, syntactic and semantic of Arabic interrogatives, an area which has not been thoroughly investigated, (ii) to represent the Functional and Semantic Structures of the Arabic interrogatives, (iii) to overcome the word-order problem associated with some Natural languages such as Arabic, (iv) to add understanding capabilities by capturing the common-sense domain specific knowledge within a specific domain
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