Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: NL

Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: N

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

Analyzing collaborative learning processes automatically

In this article we describe the emerging area of text classification research focused on the problem of collaborative learning process analysis both from a broad perspective and more specifically in terms of a publicly available tool set called TagHelper tools. Analyzing the variety of pedagogically valuable facets of learners’ interactions is a time consuming and effortful process. Improving automated analyses of such highly valued processes of collaborative learning by adapting and applying recent text classification technologies would make it a less arduous task to obtain insights from corpus data. This endeavor also holds the potential for enabling substantially improved on-line instruction both by providing teachers and facilitators with reports about the groups they are moderating and by triggering context sensitive collaborative learning support on an as-needed basis. In this article, we report on an interdisciplinary research project, which has been investigating the effectiveness of applying text classification technology to a large CSCL corpus that has been analyzed by human coders using a theory-based multidimensional coding scheme. We report promising results and include an in-depth discussion of important issues such as reliability, validity, and efficiency that should be considered when deciding on the appropriateness of adopting a new technology such as TagHelper tools. One major technical contribution of this work is a demonstration that an important piece of the work towards making text classification technology effective for this purpose is designing and building linguistic pattern detectors, otherwise known as features, that can be extracted reliably from texts and that have high predictive power for the categories of discourse actions that the CSCL community is interested in

Technologically Improving Textualism

The textualist approach to construing statutes, regulations, contracts, and other documents remains dominant but has drawbacks, most significantly its tendency to disregard probative evidence of textual meaning in favor of isolated judicial impressions and dictionary definitions. Although a broader, contextual, “integrative” approach to interpretation is preferable, the hegemony of textualism, even extreme textualism, is unlikely to recede soon. Textualism can be substantially improved, however, through effective use of a form of big data—the corpus linguistics approach to discerning word meaning. By enlarging the universe of sources about how words are actually used, corpus linguistics represents a significant improvement over imperial judicial pronouncements about word meaning along with episodic and inconsistent use of dictionary definitions for deciding cases. If deployed as tool of textualism rather than formulaic use of a bigger dictionary, corpus linguistics analysis can, at a minimum, serve as a useful supplement to traditional textualist tools

An Emergent Approach to Text Analysis Based on a Connectionist Model and the Web

In this paper, we present a method to provide proactive assistance in text checking, based on usage relationships between words structuralized on the Web. For a given sentence, the method builds a connectionist structure of relationships between word n-grams. Such structure is then parameterized by means of an unsupervised and language agnostic optimization process. Finally, the method provides a representation of the sentence that allows emerging the least prominent usage-based relational patterns, helping to easily find badly-written and unpopular text. The study includes the problem statement and its characterization in the literature, as well as the proposed solving approach and some experimental use

Judging Ordinary Meaning

Judges generally begin their interpretive task by looking for the ordinary meaning of the language of the law. And they often end there - out of respect for the notice function of the law or deference to the presumed intent of the lawmaker. Most everyone agrees on the primacy of the ordinary meaning rule. Yet scholars roundly bemoan the indeterminacy of the communicative content of the language of the law. And they pivot quickly to other grounds for interpretation

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