1,918 research outputs found
Advanced Knowledge Technologies at the Midterm: Tools and Methods for the Semantic Web
The University of Edinburgh and research sponsors are authorised to reproduce and distribute reprints and on-line copies for their purposes notwithstanding any copyright annotation hereon. The views and conclusions contained herein are the author’s and shouldn’t be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of other parties.In a celebrated essay on the new electronic media, Marshall McLuhan wrote in 1962:Our private senses are not closed systems but are endlessly translated into each other in that experience which we call consciousness. Our extended senses, tools, technologies, through the ages, have been closed systems incapable of interplay or collective awareness. Now, in the electric age, the very
instantaneous nature of co-existence among our technological instruments has created a crisis quite new in human history. Our extended faculties and senses now constitute a single field of experience which demands that they become collectively conscious. Our technologies, like our private senses, now demand an interplay and ratio that makes rational co-existence possible. As long as our technologies were as slow as the wheel or the alphabet or money, the fact that
they were separate, closed systems was socially and psychically supportable. This is not true now when sight and sound and movement are simultaneous and global in extent. (McLuhan 1962, p.5, emphasis in original)Over forty years later, the seamless interplay that McLuhan demanded between our
technologies is still barely visible. McLuhan’s predictions of the spread, and increased importance, of electronic media have of course been borne out, and the worlds of business, science and knowledge storage and transfer have been revolutionised. Yet
the integration of electronic systems as open systems remains in its infancy.Advanced Knowledge Technologies (AKT) aims to address this problem, to create a view of knowledge and its management across its lifecycle, to research and create the
services and technologies that such unification will require. Half way through its sixyear span, the results are beginning to come through, and this paper will explore some of the services, technologies and methodologies that have been developed. We hope to give a sense in this paper of the potential for the next three years, to discuss the insights and lessons learnt in the first phase of the project, to articulate the challenges and issues that remain.The WWW provided the original context that made the AKT approach to knowledge
management (KM) possible. AKT was initially proposed in 1999, it brought together an interdisciplinary consortium with the technological breadth and complementarity to create the conditions for a unified approach to knowledge across its lifecycle. The
combination of this expertise, and the time and space afforded the consortium by the
IRC structure, suggested the opportunity for a concerted effort to develop an approach
to advanced knowledge technologies, based on the WWW as a basic infrastructure.The technological context of AKT altered for the better in the short period between the development of the proposal and the beginning of the project itself with the development of the semantic web (SW), which foresaw much more intelligent manipulation and querying of knowledge. The opportunities that the SW provided for e.g., more intelligent retrieval, put AKT in the centre of information technology innovation and knowledge management services; the AKT skill set would clearly be central for the exploitation of those opportunities.The SW, as an extension of the WWW, provides an interesting set of constraints to
the knowledge management services AKT tries to provide. As a medium for the
semantically-informed coordination of information, it has suggested a number of ways in which the objectives of AKT can be achieved, most obviously through the
provision of knowledge management services delivered over the web as opposed to the creation and provision of technologies to manage knowledge.AKT is working on the assumption that many web services will be developed and provided for users. The KM problem in the near future will be one of deciding which services are needed and of coordinating them. Many of these services will be largely or entirely legacies of the WWW, and so the capabilities of the services will vary. As well as providing useful KM services in their own right, AKT will be aiming to exploit this opportunity, by reasoning over services, brokering between them, and providing essential meta-services for SW knowledge service management.Ontologies will be a crucial tool for the SW. The AKT consortium brings a lot of expertise on ontologies together, and ontologies were always going to be a key part of the strategy. All kinds of knowledge sharing and transfer activities will be mediated by ontologies, and ontology management will be an important enabling task. Different
applications will need to cope with inconsistent ontologies, or with the problems that will follow the automatic creation of ontologies (e.g. merging of pre-existing
ontologies to create a third). Ontology mapping, and the elimination of conflicts of
reference, will be important tasks. All of these issues are discussed along with our
proposed technologies.Similarly, specifications of tasks will be used for the deployment of knowledge services over the SW, but in general it cannot be expected that in the medium term there will be standards for task (or service) specifications. The brokering metaservices
that are envisaged will have to deal with this heterogeneity.The emerging picture of the SW is one of great opportunity but it will not be a wellordered, certain or consistent environment. It will comprise many repositories of legacy data, outdated and inconsistent stores, and requirements for common understandings across divergent formalisms. There is clearly a role for standards to play to bring much of this context together; AKT is playing a significant role in these efforts. But standards take time to emerge, they take political power to enforce, and they have been known to stifle innovation (in the short term). AKT is keen to understand the balance between principled inference and statistical processing of web content. Logical inference on the Web is tough. Complex queries using traditional AI inference methods bring most distributed computer systems to their knees. Do we set up semantically well-behaved areas of the Web? Is any part of the Web in which
semantic hygiene prevails interesting enough to reason in? These and many other
questions need to be addressed if we are to provide effective knowledge technologies
for our content on the web
CLSE: Corpus of Linguistically Significant Entities
One of the biggest challenges of natural language generation (NLG) is the
proper handling of named entities. Named entities are a common source of
grammar mistakes such as wrong prepositions, wrong article handling, or
incorrect entity inflection. Without factoring linguistic representation, such
errors are often underrepresented when evaluating on a small set of arbitrarily
picked argument values, or when translating a dataset from a linguistically
simpler language, like English, to a linguistically complex language, like
Russian. However, for some applications, broadly precise grammatical
correctness is critical -- native speakers may find entity-related grammar
errors silly, jarring, or even offensive.
To enable the creation of more linguistically diverse NLG datasets, we
release a Corpus of Linguistically Significant Entities (CLSE) annotated by
linguist experts. The corpus includes 34 languages and covers 74 different
semantic types to support various applications from airline ticketing to video
games. To demonstrate one possible use of CLSE, we produce an augmented version
of the Schema-Guided Dialog Dataset, SGD-CLSE. Using the CLSE's entities and a
small number of human translations, we create a linguistically representative
NLG evaluation benchmark in three languages: French (high-resource), Marathi
(low-resource), and Russian (highly inflected language). We establish quality
baselines for neural, template-based, and hybrid NLG systems and discuss the
strengths and weaknesses of each approach.Comment: Proceedings of the 2nd Workshop on Natural Language Generation,
Evaluation, and Metrics (GEM 2022) at EMNLP 202
Knowledge Elicitation Methods for Affect Modelling in Education
Research on the relationship between affect and cognition in Artificial Intelligence in Education (AIEd) brings an important dimension to our understanding of how learning occurs and how it can be facilitated. Emotions are crucial to learning, but their nature, the conditions under which they occur, and their exact impact on learning for different learners in diverse contexts still needs to be mapped out. The study of affect during learning can be challenging, because emotions are subjective, fleeting phenomena that are often difficult for learners to report accurately and for observers to perceive reliably. Context forms an integral part of learners’ affect and the study thereof. This review provides a synthesis of the current knowledge elicitation methods that are used to aid the study of learners’ affect and to inform the design of intelligent technologies for learning. Advantages and disadvantages of the specific methods are discussed along with their respective potential for enhancing research in this area, and issues related to the interpretation of data that emerges as the result of their use. References to related research are also provided together with illustrative examples of where the individual methods have been used in the past. Therefore, this review is intended as a resource for methodological decision making for those who want to study emotions and their antecedents in AIEd contexts, i.e. where the aim is to inform the design and implementation of an intelligent learning environment or to evaluate its use and educational efficacy
The Impact Of Translation On Constructed Action and Constructed Dialogue In ASL Texts
Depiction, a phenomenon similar to iconicity, involves representing what something âlooks like or is likeâ (Streeck 2008:289). Because depiction is used more heavily in sign languages than spoken languages (Dudis 2007), people interpreting or translating spoken/written texts into signed languages struggle to use depiction naturally (Thumann 2011). This thesis analyzes constructed action (CA) and constructed dialogue (CD), two types of depiction in which the signerâs hands represent those of a discourse participant. Using Tannen (1989) & Metzgerâs (1995) framework of non-directly-quoted CACD and Quinto-Pozos & Mehtaâs (2010) degrees of CA, I examine differences between narratives originally composed in ASL and narrative segments from Genesis and Exodus that have been translated into ASL, all of which were signed by Deaf users of ASL. This analysis indicates that translated texts are slower and use less non-directly-quoted CD than non-translated texts. Measuring depiction can improve the naturalness of these and other translations
Accessing spoken interaction through dialogue processing [online]
Zusammenfassung
Unser Leben, unsere Leistungen und unsere Umgebung, alles wird
derzeit durch Schriftsprache dokumentiert. Die rasante
Fortentwicklung der technischen Möglichkeiten Audio, Bilder und
Video aufzunehmen, abzuspeichern und wiederzugeben kann genutzt
werden um die schriftliche Dokumentation von menschlicher
Kommunikation, zum Beispiel Meetings, zu unterstützen, zu
ergänzen oder gar zu ersetzen. Diese neuen Technologien können
uns in die Lage versetzen Information aufzunehmen, die
anderweitig verloren gehen, die Kosten der Dokumentation zu
senken und hochwertige Dokumente mit audiovisuellem Material
anzureichern. Die Indizierung solcher Aufnahmen stellt die
Kerntechnologie dar um dieses Potential auszuschöpfen. Diese
Arbeit stellt effektive Alternativen zu schlüsselwortbasierten
Indizes vor, die Suchraumeinschränkungen bewirken und teilweise
mit einfachen Mitteln zu berechnen sind.
Die Indizierung von Sprachdokumenten kann auf verschiedenen
Ebenen erfolgen: Ein Dokument gehört stilistisch einer
bestimmten Datenbasis an, welche durch sehr einfache Merkmale
bei hoher Genauigkeit automatisch bestimmt werden kann.
Durch diese Art von Klassifikation kann eine Reduktion des
Suchraumes um einen Faktor der Größenordnung 4Â10 erfolgen. Die
Anwendung von thematischen Merkmalen zur Textklassifikation
bei einer Nachrichtendatenbank resultiert in einer Reduktion um
einen Faktor 18. Da Sprachdokumente sehr lang sein können müssen
sie in thematische Segmente unterteilt werden. Ein neuer
probabilistischer Ansatz sowie neue Merkmale (SprecherinitiaÂ
tive und Stil) liefern vergleichbare oder bessere Resultate als
traditionelle schlüsselwortbasierte Ansätze. Diese thematische
Segmente können durch die vorherrschende Aktivität
charakterisiert werden (erzählen, diskutieren, planen, ...),
die durch ein neuronales Netz detektiert werden kann. Die
Detektionsraten sind allerdings begrenzt da auch Menschen
diese Aktivitäten nur ungenau bestimmen. Eine maximale
Reduktion des Suchraumes um den Faktor 6 ist bei den verwendeten
Daten theoretisch möglich. Eine thematische Klassifikation
dieser Segmente wurde ebenfalls auf einer Datenbasis
durchgeführt, die Detektionsraten für diesen Index sind jedoch
gering.
Auf der Ebene der einzelnen Äußerungen können Dialogakte wie
Aussagen, Fragen, Rückmeldungen (aha, ach ja, echt?, ...) usw.
mit einem diskriminativ trainierten Hidden Markov Model erkannt
werden. Dieses Verfahren kann um die Erkennung von kurzen Folgen
wie Frage/AntwortÂSpielen erweitert werden (Dialogspiele).
Dialogakte und Âspiele können eingesetzt werden um
Klassifikatoren für globale Sprechstile zu bauen. Ebenso
könnte ein Benutzer sich an eine bestimmte Dialogaktsequenz
erinnern und versuchen, diese in einer grafischen
Repräsentation wiederzufinden.
In einer Studie mit sehr pessimistischen Annahmen konnten
Benutzer eines aus vier ähnlichen und gleichwahrscheinlichen
Gesprächen mit einer Genauigkeit von ~ 43% durch eine graphische
Repräsentation von Aktivität bestimmt.
Dialogakte könnte in diesem Szenario ebenso nützlich sein, die
Benutzerstudie konnte aufgrund der geringen Datenmenge darüber
keinen endgültigen Aufschluß geben. Die Studie konnte allerdings
für detailierte Basismerkmale wie Formalität und
Sprecheridentität keinen Effekt zeigen.
Abstract
Written language is one of our primary means for documenting our
lives, achievements, and environment. Our capabilities to
record, store and retrieve audio, still pictures, and video are
undergoing a revolution and may support, supplement or even
replace written documentation. This technology enables us to
record information that would otherwise be lost, lower the cost
of documentation and enhance highÂquality documents with
original audiovisual material.
The indexing of the audio material is the key technology to
realize those benefits. This work presents effective
alternatives to keyword based indices which restrict the search
space and may in part be calculated with very limited resources.
Indexing speech documents can be done at a various levels:
Stylistically a document belongs to a certain database which can
be determined automatically with high accuracy using very simple
features. The resulting factor in search space reduction is in
the order of 4Â10 while topic classification yielded a factor
of 18 in a news domain.
Since documents can be very long they need to be segmented into
topical regions. A new probabilistic segmentation framework as
well as new features (speaker initiative and style) prove to be
very effective compared to traditional keyword based methods. At
the topical segment level activities (storytelling, discussing,
planning, ...) can be detected using a machine learning approach
with limited accuracy; however even human annotators do not
annotate them very reliably. A maximum search space reduction
factor of 6 is theoretically possible on the databases used. A
topical classification of these regions has been attempted
on one database, the detection accuracy for that index, however,
was very low.
At the utterance level dialogue acts such as statements,
questions, backchannels (aha, yeah, ...), etc. are being
recognized using a novel discriminatively trained HMM procedure.
The procedure can be extended to recognize short sequences such
as question/answer pairs, so called dialogue games.
Dialog acts and games are useful for building classifiers for
speaking style. Similarily a user may remember a certain dialog
act sequence and may search for it in a graphical
representation.
In a study with very pessimistic assumptions users are able to
pick one out of four similar and equiprobable meetings correctly
with an accuracy ~ 43% using graphical activity information.
Dialogue acts may be useful in this situation as well but the
sample size did not allow to draw final conclusions. However the
user study fails to show any effect for detailed basic features
such as formality or speaker identity
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