744 research outputs found
Using the Annotated Bibliography as a Resource for Indicative Summarization
We report on a language resource consisting of 2000 annotated bibliography
entries, which is being analyzed as part of our research on indicative document
summarization. We show how annotated bibliographies cover certain aspects of
summarization that have not been well-covered by other summary corpora, and
motivate why they constitute an important form to study for information
retrieval. We detail our methodology for collecting the corpus, and overview
our document feature markup that we introduced to facilitate summary analysis.
We present the characteristics of the corpus, methods of collection, and show
its use in finding the distribution of types of information included in
indicative summaries and their relative ordering within the summaries.Comment: 8 pages, 3 figure
Using librarian techniques in automatic text summarization for information retrieval
A current application of automatic text summarization is to provide an overview of relevant documents coming from an information retrieval (IR) system. This paper examines how Centrifuser, one such summarization system, was designed with respect to methods used in the library community. We have reviewed these librarian expert techniques to assist information seekers and codified them into eight distinct strategies. We detail how we have operationalized six of these strategies in Centrifuser by computing an informative extract, indicative differences between documents, as well as navigational links to narrow or broaden a user's query. We conclude the paper with results from a preliminary evaluation
NLP Driven Models for Automatically Generating Survey Articles for Scientific Topics.
This thesis presents new methods that use natural language processing (NLP) driven models for summarizing research in scientific fields. Given a topic query in the form of a text string, we present methods for finding research articles relevant to the topic as well as summarization algorithms that use lexical and discourse information present in the text of these articles to generate coherent and readable extractive summaries of past research on the topic. In addition to summarizing prior research, good survey articles should also forecast future trends. With this motivation, we present work on forecasting future impact of scientific publications using NLP driven features.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113407/1/rahuljha_1.pd
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 410 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 (Sprecherinitia
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/AntwortSpielen erweitert werden (Dialogspiele).
Dialogakte und spiele 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 highquality 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 410 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
Argumentative zoning information extraction from scientific text
Let me tell you, writing a thesis is not always a barrel of laughs—and strange things can happen, too. For example, at the height of my thesis paranoia, I had a re-current dream in which my cat Amy gave me detailed advice on how to restructure the thesis chapters, which was awfully nice of her. But I also had a lot of human help throughout this time, whether things were going fine or beserk. Most of all, I want to thank Marc Moens: I could not have had a better or more knowledgable supervisor. He always took time for me, however busy he might have been, reading chapters thoroughly in two days. He both had the calmness of mind to give me lots of freedom in research, and the right judgement to guide me away, tactfully but determinedly, from the occasional catastrophe or other waiting along the way. He was great fun to work with and also became a good friend. My work has profitted from the interdisciplinary, interactive and enlightened atmosphere at the Human Communication Centre and the Centre for Cognitive Science (which is now called something else). The Language Technology Group was a great place to work in, as my research was grounded in practical applications develope
Language modelling for clinical natural language understanding and generation
One of the long-standing objectives of Artificial Intelligence (AI) is to design and develop algorithms for social good including tackling public health challenges. In the era of digitisation, with an unprecedented amount of healthcare data being captured in digital form, the analysis of the healthcare data at scale can lead to better research of diseases, better monitoring patient conditions and more importantly improving patient outcomes. However, many AI-based analytic algorithms rely solely on structured healthcare data such as bedside measurements and test results which only account for 20% of all healthcare data, whereas the remaining 80% of healthcare data is unstructured including textual data such as clinical notes and discharge summaries which is still underexplored.
Conventional Natural Language Processing (NLP) algorithms that are designed for clinical applications rely on the shallow matching, templates and non-contextualised word embeddings which lead to limited understanding of contextual semantics. Though recent advances in NLP algorithms have demonstrated promising performance on a variety of NLP tasks in the general domain with contextualised language models, most of these generic NLP algorithms struggle at specific clinical NLP tasks which require biomedical knowledge and reasoning. Besides, there is limited research to study generative NLP algorithms to generate clinical reports and summaries automatically by considering salient clinical information.
This thesis aims to design and develop novel NLP algorithms especially clinical-driven contextualised language models to understand textual healthcare data and generate clinical narratives which can potentially support clinicians, medical scientists and patients. The first contribution of this thesis focuses on capturing phenotypic information of patients from clinical notes which is important to profile patient situation and improve patient outcomes. The thesis proposes a novel self-supervised language model, named Phenotypic Intelligence Extraction (PIE), to annotate phenotypes from clinical notes with the detection of contextual synonyms and the enhancement to reason with numerical values. The second contribution is to demonstrate the utility and benefits of using phenotypic features of patients in clinical use cases by predicting patient outcomes in Intensive Care Units (ICU) and identifying patients at risk of specific diseases with better accuracy and model interpretability. The third contribution is to propose generative models to generate clinical narratives to automate and accelerate the process of report writing and summarisation by clinicians. This thesis first proposes a novel summarisation language model named PEGASUS which surpasses or is on par with the state-of-the-art performance on 12 downstream datasets including biomedical literature from PubMed. PEGASUS is further extended to generate medical scientific documents from input tabular data.Open Acces
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