26,088 research outputs found
Tagging time in prolog : the temporality effect project
This article combines a brief introduction into a particular philosophical theory of "time" with a demonstration of how this theory has been implemented in a Literary Studies oriented Humanities Computing project. The aim of the project was to create a model of text-based time cognition and design customized markup and text analysis tools that help to understand ‘‘how time works’’: more precisely, how narratively organised and communicated information motivates readers to generate the mental image of a chronologically organized world. The approach presented is based on the unitary model of time originally proposed by McTaggart, who distinguished between two perspectives onto time, the so-called A- and B-series. The first step towards a functional Humanities Computing implementation of this theoretical approach was the development of TempusMarker—a software tool providing automatic and semi-automatic markup routines for the tagging of temporal expressions in natural language texts. In the second step we discuss the principals underlying TempusParser—an analytical tool that can reconstruct temporal order in events by way of an algorithm-driven process of analysis and recombination of textual segments during which the "time stamp" of each segment as indicated by the temporal tags is interpreted
Towards a Unified Knowledge-Based Approach to Modality Choice
This paper advances a unified knowledge-based approach to the process of choosing the most appropriate modality or combination of modalities in multimodal output generation. We propose a Modality Ontology (MO) that models the knowledge needed to support the two most fundamental processes determining modality choice – modality allocation (choosing the modality or set of modalities that can best support a particular type of information) and modality combination (selecting an optimal final combination of modalities). In the proposed ontology we model the main levels which collectively determine the characteristics of each modality and the specific relationships between different modalities that are important for multi-modal meaning making. This ontology aims to support the automatic selection of modalities and combinations of modalities that are suitable to convey the meaning of the intended message
Natural Language Dialogue Service for Appointment Scheduling Agents
Appointment scheduling is a problem faced daily by many individuals and
organizations. Cooperating agent systems have been developed to partially
automate this task. In order to extend the circle of participants as far as
possible we advocate the use of natural language transmitted by e-mail. We
describe COSMA, a fully implemented German language server for existing
appointment scheduling agent systems. COSMA can cope with multiple dialogues in
parallel, and accounts for differences in dialogue behaviour between human and
machine agents. NL coverage of the sublanguage is achieved through both
corpus-based grammar development and the use of message extraction techniques.Comment: 8 or 9 pages, LaTeX; uses aclap.sty, epsf.te
Learning Sentence-internal Temporal Relations
In this paper we propose a data intensive approach for inferring
sentence-internal temporal relations. Temporal inference is relevant for
practical NLP applications which either extract or synthesize temporal
information (e.g., summarisation, question answering). Our method bypasses the
need for manual coding by exploiting the presence of markers like after", which
overtly signal a temporal relation. We first show that models trained on main
and subordinate clauses connected with a temporal marker achieve good
performance on a pseudo-disambiguation task simulating temporal inference
(during testing the temporal marker is treated as unseen and the models must
select the right marker from a set of possible candidates). Secondly, we assess
whether the proposed approach holds promise for the semi-automatic creation of
temporal annotations. Specifically, we use a model trained on noisy and
approximate data (i.e., main and subordinate clauses) to predict
intra-sentential relations present in TimeBank, a corpus annotated rich
temporal information. Our experiments compare and contrast several
probabilistic models differing in their feature space, linguistic assumptions
and data requirements. We evaluate performance against gold standard corpora
and also against human subjects
Dialogue as Data in Learning Analytics for Productive Educational Dialogue
This paper provides a novel, conceptually driven stance on the state of the contemporary analytic challenges faced in the treatment of dialogue as a form of data across on- and offline sites of learning. In prior research, preliminary steps have been taken to detect occurrences of such dialogue using automated analysis techniques. Such advances have the potential to foster effective dialogue using learning analytic techniques that scaffold, give feedback on, and provide pedagogic contexts promoting such dialogue. However, the translation of much prior learning science research to online contexts is complex, requiring the operationalization of constructs theorized in different contexts (often face-to-face), and based on different datasets and structures (often spoken dialogue). In this paper, we explore what could constitute the effective analysis of productive online dialogues, arguing that it requires consideration of three key facets of the dialogue: features indicative of productive dialogue; the unit of segmentation; and the interplay of features and segmentation with the temporal underpinning of learning contexts. The paper thus foregrounds key considerations regarding the analysis of dialogue data in emerging learning analytics environments, both for learning-science and for computationally oriented researchers
Neuro-fuzzy knowledge processing in intelligent learning environments for improved student diagnosis
In this paper, a neural network implementation for a fuzzy logic-based model of the diagnostic process is proposed as a means to achieve accurate student diagnosis and updates of the student model in Intelligent Learning Environments. The neuro-fuzzy synergy allows the diagnostic model to some extent "imitate" teachers in diagnosing students' characteristics, and equips the intelligent learning environment with reasoning capabilities that can be further used to drive pedagogical decisions depending on the student learning style. The neuro-fuzzy implementation helps to encode both structured and non-structured teachers' knowledge: when teachers' reasoning is available and well defined, it can be encoded in the form of fuzzy rules; when teachers' reasoning is not well defined but is available through practical examples illustrating their experience, then the networks can be trained to represent this experience. The proposed approach has been tested in diagnosing aspects of student's learning style in a discovery-learning environment that aims to help students to construct the concepts of vectors in physics and mathematics. The diagnosis outcomes of the model have been compared against the recommendations of a group of five experienced teachers, and the results produced by two alternative soft computing methods. The results of our pilot study show that the neuro-fuzzy model successfully manages the inherent uncertainty of the diagnostic process; especially for marginal cases, i.e. where it is very difficult, even for human tutors, to diagnose and accurately evaluate students by directly synthesizing subjective and, some times, conflicting judgments
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
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