Proceedings of the First Workshop on Computing News Storylines (CNewsStory 2015)

This volume contains the proceedings of the 1st Workshop on Computing News Storylines (CNewsStory 2015) held in conjunction with the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2015) at the China National Convention Center in Beijing, on July 31st 2015. Narratives are at the heart of information sharing. Ever since people began to share their experiences, they have connected them to form narratives. The study od storytelling and the field of literary theory called narratology have developed complex frameworks and models related to various aspects of narrative such as plots structures, narrative embeddings, characters’ perspectives, reader response, point of view, narrative voice, narrative goals, and many others. These notions from narratology have been applied mainly in Artificial Intelligence and to model formal semantic approaches to narratives (e.g. Plot Units developed by Lehnert (1981)). In recent years, computational narratology has qualified as an autonomous field of study and research. Narrative has been the focus of a number of workshops and conferences (AAAI Symposia, Interactive Storytelling Conference (ICIDS), Computational Models of Narrative). Furthermore, reference annotation schemes for narratives have been proposed (NarrativeML by Mani (2013)). The workshop aimed at bringing together researchers from different communities working on representing and extracting narrative structures in news, a text genre which is highly used in NLP but which has received little attention with respect to narrative structure, representation and analysis. Currently, advances in NLP technology have made it feasible to look beyond scenario-driven, atomic extraction of events from single documents and work towards extracting story structures from multiple documents, while these documents are published over time as news streams. Policy makers, NGOs, information specialists (such as journalists and librarians) and others are increasingly in need of tools that support them in finding salient stories in large amounts of information to more effectively implement policies, monitor actions of “big players” in the society and check facts. Their tasks often revolve around reconstructing cases either with respect to specific entities (e.g. person or organizations) or events (e.g. hurricane Katrina). Storylines represent explanatory schemas that enable us to make better selections of relevant information but also projections to the future. They form a valuable potential for exploiting news data in an innovative way.JRC.G.2-Global security and crisis managemen

Lexicography and its interdisciplinary contacts, with special reference to Linguistics and Onomasiology

The article attempts a first conspectus of what is known about interdisciplinary con-tacts between the fields of Lexicography and Linguistics, and in the process asks a range of fundamental questions on whether and how the subject matter is shared, and how we can improve mutual relations. Firstly, some opinions and arguments in the literature are reviewed, with par-ticular attention to (a) what criteria determine the status of a discipline, (b) how various relations between Lexicography and Linguistics can be modelled, (c) how practice and theory interact, and (d) what parallels there are between the two disciplines, in terms of such notions as description, codification and remediation. Secondly, views from the position of Linguistics are categorised his-torically and systematically, first by tracing the development from historical comparative or dia-chronic linguistics (philology) to structural-descriptive (synchronic) linguistics as well as applied linguistics, and then by combining five of the linguistic 'levels' with the three semiotic 'dimensions', and relating them to various interdisciplinary or problem-solving fields, such as sociolinguistics and computational linguistics. Thirdly, several points of view in Lexicography are presented, and an explicit framework is developed for displaying its place relative to its 'mother', 'sister' and 'daughter' disciplines as well as some of its 'data-supplying' disciplines, paying special attention to the problem of methodology. Fourthly, as a special case study, the territory of 'onomasiology' is discussed to illustrate the limitations and possibilities of various approaches to the analysis, de-scription and lexicographic presentation of synonym and antonym sets of vocabulary. The conclusion is reached that ways must be found to understand the interdisciplinary nature of Lexico-graphy, and a plea is made to move from mutual neglect to deliberate collaboration. (Several dia-grams are used to display networks of relations; bibliographical references focus on recent work and include representative reference works for the major disciplines mentioned.) Keywords: lexicography, metalexicography, theory and practice, de-scription, codification, remediation, application, interdisciplinary con-tacts, linguistics, onomasiology, mother disciplines, sister disciplines, daughter disciplines, data-supplying disciplines, methodology, reference science, semiotic

Framing university small group talk : knowledge construction through lexical concepts

PhD ThesisKnowledge construction in educational discourse continues to interest practitioners and researchers due to the conceptually “natural” connection between knowledge and learning for professional development. Frames have conceptual and practical advantages over other units of inquiry concerning meaning negotiation for knowledge construction. They are relatively stable data-structures representing prototypical situations retrieved from real world experiences, cover larger units of meaning beyond the immediate sequential mechanism at interaction, and have been inherently placed at the semantic-pragmatic interface for empirical observation. Framing in a particular context – university small group talk has been an under-researched field, while the relationship between talk and knowledge through collaborative work has been identified below/at the Higher Educational level. Involving higher level cognitive activities and distinct interactional patterns, university small group talk is worth close examination and systematic investigation. This study applies Corpus Linguistics and Interactional Linguistics approaches to examine a subset of a one-million-word corpus of university small group talk at a UK university. Specifically, it provides a detailed examination of the participants’ framing behaviours for knowledge construction through their talk of disciplinary lexical concepts. Analysis reveals how the participants draw upon schematized knowledge structures evoked by particular lexical choices and how they invoke expanded scenarios via pragmatic mappings in the ongoing interaction. Additionally, it is demonstrated how the framing moves are related to the structural uniqueness of university small group talk, the contextualized speaker roles and the institutional procedures and routines. This study deepens the understanding of the relationship between linguistically constructed knowledge and the way interlocutors conceptualize the world through institutionalized collaboration, building upon the existing research on human reliance upon structures to interpret reality at both the conceptual and the action levels. The study also addresses interaction research in Higher Educational settings, by discussing how the cognitive-communicative duality of framing is sensitive to various contextual resources, distinct discourse structures and task procedures through the group dynamics

Improving Knowledge Management Programs Using Marginal Utility in a Metric Space Generated by Conceptual Graphs

Knowledge management has emerged as a field of endeavor that blends a systems approach with methods drawn from organizational management and learning. In contrast, knowledge representation, a branch of artificial intelligence, is grounded in formal methods. Research in the separate behavioral and the structural disciplines – knowledge management and knowledge engineering - has not traditionally cross-pollinated. This has prevented the development of many practical practices useful in organizations. Organization managers - line and senior - lack guidance in where to direct improvement efforts targeted at specific groups of company knowledge workers. Demonstrated here is Knowledge Improvement Measurement Space (KIMS), a model providing a solution to that improvement problem. It employs marginal utility theory in a metric space, with formal reasoning via software agents realized in Sowa\u27s conceptual graphs, operating over a knowledge management conceptual structure. These components allow repeated evaluation of knowledge improvement measurements. Knowledge representation technology was applied to organize and encourage knowledge sharing, to achieve competitive advantage, and to measure progress toward that achievement. The KlMS reentrant process, a method of using the KIMS model, was shown to consist of metrics data calculated by executing joined conceptual graphs, consolidated into a distance variable to be estimated via a Minkowski metrics space. The metric space was shown to be equivalent to a marginal utility, which may be evaluated to determine the new level of knowledge capability. The procedure may be repeated until knowledge management goals are achieved. The solution took into account the body of knowledge related human understanding and learning, and formal methods of knowledge organization. These were shown to include surface ontologies based in a knowledge management program, principles of business strategy, and organizational learning. KIMS was validated through a demonstration based on empirical data collected over a five-year program in a large aerospace company during its progress in applying the Software Engineering Institute Capability Maturity Model

Harmonic Analysis and Machine Learning

This dissertation considers data representations that lie at the interesection of harmonic analysis and neural networks. The unifying theme of this work is the goal for robust and reliable machine learning. Our specific contributions include a new variant of scattering transforms based on a Haar-type directional wavelet, a new study of deep neural network instability in the context of remote sensing problems, and new empirical studies of biomedical applications of neural networks

Empirical modelling of translation and interpreting

"Empirical research is carried out in a cyclic way: approaching a research area bottom-up, data lead to interpretations and ideally to the abstraction of laws, on the basis of which a theory can be derived. Deductive research is based on a theory, on the basis of which hypotheses can be formulated and tested against the background of empirical data. Looking at the state-of-the-art in translation studies, either theories as well as models are designed or empirical data are collected and interpreted. However, the final step is still lacking: so far, empirical data has not lead to the formulation of theories or models, whereas existing theories and models have not yet been comprehensively tested with empirical methods. This publication addresses these issues from several perspectives: multi-method product- as well as process-based research may gain insights into translation as well as interpreting phenomena. These phenomena may include cognitive and organizational processes, procedures and strategies, competence and performance, translation properties and universals, etc. Empirical findings about the deeper structures of translation and interpreting will reduce the gap between translation and interpreting practice and model and theory building. Furthermore, the availability of more large-scale empirical testing triggers the development of models and theories concerning translation and interpreting phenomena and behavior based on quantifiable, replicable and transparent data.

Essential Speech and Language Technology for Dutch: Results by the STEVIN-programme

Computational Linguistics; Germanic Languages; Artificial Intelligence (incl. Robotics); Computing Methodologie