Summarization of Films and Documentaries Based on Subtitles and Scripts

We assess the performance of generic text summarization algorithms applied to films and documentaries, using the well-known behavior of summarization of news articles as reference. We use three datasets: (i) news articles, (ii) film scripts and subtitles, and (iii) documentary subtitles. Standard ROUGE metrics are used for comparing generated summaries against news abstracts, plot summaries, and synopses. We show that the best performing algorithms are LSA, for news articles and documentaries, and LexRank and Support Sets, for films. Despite the different nature of films and documentaries, their relative behavior is in accordance with that obtained for news articles.Comment: 7 pages, 9 tables, 4 figures, submitted to Pattern Recognition Letters (Elsevier

From MASK knowledge management methodology to learning activities described with IMS - LD

In this paper we present how knowledge capitalized using the Knowledge Management Mask methodology can be used to design E-learning activities by matching Mask models and the concepts proposed by the IMS-Learning Design modelling language. Our study consists in highlighting the e-learning aspects encapsulated in these MASK models carried out around a domain of activity, via a writing these elements in the description language IMS - Learning Design, in a preoccupation of reusability and reengineering.Knowledge Management, E-learning

Empowering Doctoral Candidates in Finding Relevant Concepts in a Literature Set

Research activity connects prior findings with new and emerging ideas in the hope of attracting attention of those in the community who are able to make some sort of profit from the application of those ideas. Finding relevant concepts from among the vast repository of already published ideas is an important challenge for all researchers. Nowadays, there is ample opportunity to take advantage of the empowering aspects of developments in information technology. In this study, we share some ideas to efficiently and effectively find relevant concepts in a given literature set. Our need was to find and tag predetermined concepts from among the scores of research articles that our literature search had delivered – necessarily this was a large enough task to warrant the development of some automated support to find and tag the relevant concepts. We had prior exposure to Adobe® Acrobat® Pro and noted that it provides superior ‘search and find’ facilities; we decided to trial this use for our purposes in the ‘Tribes and Cultures’ research project. We have devised and trialled a seven step, semi-automated method to assist in finding relevant concepts from within collections of research articles, retrieved as Portable Document Format (PDF) files, from bibliographic databases and digital libraries

Supervised and Unsupervised Transfer Learning for Question Answering

Although transfer learning has been shown to be successful for tasks like object and speech recognition, its applicability to question answering (QA) has yet to be well-studied. In this paper, we conduct extensive experiments to investigate the transferability of knowledge learned from a source QA dataset to a target dataset using two QA models. The performance of both models on a TOEFL listening comprehension test (Tseng et al., 2016) and MCTest (Richardson et al., 2013) is significantly improved via a simple transfer learning technique from MovieQA (Tapaswi et al., 2016). In particular, one of the models achieves the state-of-the-art on all target datasets; for the TOEFL listening comprehension test, it outperforms the previous best model by 7%. Finally, we show that transfer learning is helpful even in unsupervised scenarios when correct answers for target QA dataset examples are not available.Comment: To appear in NAACL HLT 2018 (long paper

Rank-aware, Approximate Query Processing on the Semantic Web

Search over the Semantic Web corpus frequently leads to queries having large result sets. So, in order to discover relevant data elements, users must rely on ranking techniques to sort results according to their relevance. At the same time, applications oftentimes deal with information needs, which do not require complete and exact results. In this thesis, we face the problem of how to process queries over Web data in an approximate and rank-aware fashion

Synopsis of biological data on the cobia Rachycentron canadum (Pisces: Rachycentridae)

Information on the biology and fisheries of cobia, Rachycentron canadum, is compiled and reviewed in the FAD species synopsis style. Topics include taxonomy, morphology, distribution, reproduction, pre-adult and adult stages, food, growth, migration, population characteristics, and various aspects of exploitation. Data and information were obtained from unpublished as well as published sources. Cobia, the only species in the family Rachycentridae, is a migratory pelagic fish that occurs in tropical and subtropical seas of the world, except in the central and eastern Pacific Ocean. In the western Atlantic Ocean, spawning occurs during the warm months. Eggs and larvae are planktonic. Females grow faster than males: at 1 year, females are 36 cm FL and 0.4 kg; at 4 years, 99 cm and 11 kg; and at 8 years, 137 cm and 31 kg. Comparable data for males are: at 1 year, 31 cm and 0.3 kg; 4 years, 82 cm and 6 kg; and 8 years, 108 cm and 15 kg. Sexual maturity is attained by males at about 52 cm FL in their second year and by females at about 70 cm in their third year. Fecundity for females 100-125 cm FL varies from 1.9 to 5.4 million eggs. Cobia favor crustaceans for food, but will feed on other invertebrates and fishes as well. They attain a maximum size of over 60 kg. Cobia are fished both commercially and recreationally. Commercially, they are usually caught incidentally in both hook-and-Iine and net fISheries. In the United States, which ranks behind Pakistan, Mexico, and the Philippines in commercial production of cobia, recreational landings exceed commercial landings by more than ten-fold. (PDF file contains 32 pages.

Movie Plot Analysis via Turning Point Identification

According to screenwriting theory, turning points (e.g., change of plans, major setback, climax) are crucial narrative moments within a screenplay: they define the plot structure, determine its progression and segment the screenplay into thematic units (e.g., setup, complications, aftermath). We propose the task of turning point identification in movies as a means of analyzing their narrative structure. We argue that turning points and the segmentation they provide can facilitate processing long, complex narratives, such as screenplays, for summarization and question answering. We introduce a dataset consisting of screenplays and plot synopses annotated with turning points and present an end-to-end neural network model that identifies turning points in plot synopses and projects them onto scenes in screenplays. Our model outperforms strong baselines based on state-of-the-art sentence representations and the expected position of turning points.Comment: Accepted to appear at EMNLP 201

A knowledge server including tools for professional know-how transfer

This paper presents a research in progress on the use of knowledge engineering and knowledge management techniques for the development of a strategic approach for the transfer of professional know-how. This transfer is based on the design of devices for sharing and learning clearly identified knowledge in the oil industry domains. This work is based on a pilot study which was carried out in the PED department (Petroleum Engineering & Development) and it deals with upstream activity of the oil group Sonatrach. After the different phases of knowledge mapping, critical knowledge assessment, and strategic alignment, the KM process focus on knowledge elicitation, sharing, transfer and learning, based on design and implementation of specific tools called Knowledge Server, including Knowledge Books and e-Learning.E-learning, Knowledge management, Knowledge transfer, Knowledge engineering, Knowledge servers, Computer assisted human learning, Case study