ATLAS: A flexible and extensible architecture for linguistic annotation

We describe a formal model for annotating linguistic artifacts, from which we derive an application programming interface (API) to a suite of tools for manipulating these annotations. The abstract logical model provides for a range of storage formats and promotes the reuse of tools that interact through this API. We focus first on ``Annotation Graphs,'' a graph model for annotations on linear signals (such as text and speech) indexed by intervals, for which efficient database storage and querying techniques are applicable. We note how a wide range of existing annotated corpora can be mapped to this annotation graph model. This model is then generalized to encompass a wider variety of linguistic ``signals,'' including both naturally occuring phenomena (as recorded in images, video, multi-modal interactions, etc.), as well as the derived resources that are increasingly important to the engineering of natural language processing systems (such as word lists, dictionaries, aligned bilingual corpora, etc.). We conclude with a review of the current efforts towards implementing key pieces of this architecture.Comment: 8 pages, 9 figure

Access to recorded interviews: A research agenda

Recorded interviews form a rich basis for scholarly inquiry. Examples include oral histories, community memory projects, and interviews conducted for broadcast media. Emerging technologies offer the potential to radically transform the way in which recorded interviews are made accessible, but this vision will demand substantial investments from a broad range of research communities. This article reviews the present state of practice for making recorded interviews available and the state-of-the-art for key component technologies. A large number of important research issues are identified, and from that set of issues, a coherent research agenda is proposed

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Final FLaReNet deliverable: Language Resources for the Future - The Future of Language Resources

Language Technologies (LT), together with their backbone, Language Resources (LR), provide an essential support to the challenge of Multilingualism and ICT of the future. The main task of language technologies is to bridge language barriers and to help creating a new environment where information flows smoothly across frontiers and languages, no matter the country, and the language, of origin. To achieve this goal, all players involved need to act as a community able to join forces on a set of shared priorities. However, until now the field of Language Resources and Technology has long suffered from an excess of individuality and fragmentation, with a lack of coherence concerning the priorities for the field, the direction to move, not to mention a common timeframe. The context encountered by the FLaReNet project was thus represented by an active field needing a coherence that can only be given by sharing common priorities and endeavours. FLaReNet has contributed to the creation of this coherence by gathering a wide community of experts and making them participate in the definition of an exhaustive set of recommendations

Spoken content retrieval: A survey of techniques and technologies

Speech media, that is, digital audio and video containing spoken content, has blossomed in recent years. Large collections are accruing on the Internet as well as in private and enterprise settings. This growth has motivated extensive research on techniques and technologies that facilitate reliable indexing and retrieval. Spoken content retrieval (SCR) requires the combination of audio and speech processing technologies with methods from information retrieval (IR). SCR research initially investigated planned speech structured in document-like units, but has subsequently shifted focus to more informal spoken content produced spontaneously, outside of the studio and in conversational settings. This survey provides an overview of the field of SCR encompassing component technologies, the relationship of SCR to text IR and automatic speech recognition and user interaction issues. It is aimed at researchers with backgrounds in speech technology or IR who are seeking deeper insight on how these fields are integrated to support research and development, thus addressing the core challenges of SCR

The European Language Resources and Technologies Forum: Shaping the Future of the Multilingual Digital Europe

Proceedings of the 1st FLaReNet Forum on the European Language Resources and Technologies, held in Vienna, at the Austrian Academy of Science, on 12-13 February 2009

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

Factoid question answering for spoken documents

In this dissertation, we present a factoid question answering system, specifically tailored for Question Answering (QA) on spoken documents. This work explores, for the first time, which techniques can be robustly adapted from the usual QA on written documents to the more difficult spoken documents scenario. More specifically, we study new information retrieval (IR) techniques designed for speech, and utilize several levels of linguistic information for the speech-based QA task. These include named-entity detection with phonetic information, syntactic parsing applied to speech transcripts, and the use of coreference resolution. Our approach is largely based on supervised machine learning techniques, with special focus on the answer extraction step, and makes little use of handcrafted knowledge. Consequently, it should be easily adaptable to other domains and languages. In the work resulting of this Thesis, we have impulsed and coordinated the creation of an evaluation framework for the task of QA on spoken documents. The framework, named QAst, provides multi-lingual corpora, evaluation questions, and answers key. These corpora have been used in the QAst evaluation that was held in the CLEF workshop for the years 2007, 2008 and 2009, thus helping the developing of state-of-the-art techniques for this particular topic. The presentend QA system and all its modules are extensively evaluated on the European Parliament Plenary Sessions English corpus composed of manual transcripts and automatic transcripts obtained by three different Automatic Speech Recognition (ASR) systems that exhibit significantly different word error rates. This data belongs to the CLEF 2009 track for QA on speech transcripts. The main results confirm that syntactic information is very useful for learning to rank question candidates, improving results on both manual and automatic transcripts unless the ASR quality is very low. Overall, the performance of our system is comparable or better than the state-of-the-art on this corpus, confirming the validity of our approach.En aquesta Tesi, presentem un sistema de Question Answering (QA) factual, especialment ajustat per treballar amb documents orals. En el desenvolupament explorem, per primera vegada, quines tècniques de les habitualment emprades en QA per documents escrit són suficientment robustes per funcionar en l'escenari més difícil de documents orals. Amb més especificitat, estudiem nous mètodes de Information Retrieval (IR) dissenyats per tractar amb la veu, i utilitzem diversos nivells d'informació linqüística. Entre aquests s'inclouen, a saber: detecció de Named Entities utilitzant informació fonètica, "parsing" sintàctic aplicat a transcripcions de veu, i també l'ús d'un sub-sistema de detecció i resolució de la correferència. La nostra aproximació al problema es recolza en gran part en tècniques supervisades de Machine Learning, estant aquestes enfocades especialment cap a la part d'extracció de la resposta, i fa servir la menor quantitat possible de coneixement creat per humans. En conseqüència, tot el procés de QA pot ser adaptat a altres dominis o altres llengües amb relativa facilitat. Un dels resultats addicionals de la feina darrere d'aquesta Tesis ha estat que hem impulsat i coordinat la creació d'un marc d'avaluació de la taska de QA en documents orals. Aquest marc de treball, anomenat QAst (Question Answering on Speech Transcripts), proporciona un corpus de documents orals multi-lingüe, uns conjunts de preguntes d'avaluació, i les respostes correctes d'aquestes. Aquestes dades han estat utilitzades en les evaluacionis QAst que han tingut lloc en el si de les conferències CLEF en els anys 2007, 2008 i 2009; d'aquesta manera s'ha promogut i ajudat a la creació d'un estat-de-l'art de tècniques adreçades a aquest problema en particular. El sistema de QA que presentem i tots els seus particulars sumbòduls, han estat avaluats extensivament utilitzant el corpus EPPS (transcripcions de les Sessions Plenaries del Parlament Europeu) en anglès, que cónté transcripcions manuals de tots els discursos i també transcripcions automàtiques obtingudes mitjançant tres reconeixedors automàtics de la parla (ASR) diferents. Els reconeixedors tenen característiques i resultats diferents que permetes una avaluació quantitativa i qualitativa de la tasca. Aquestes dades pertanyen a l'avaluació QAst del 2009. Els resultats principals de la nostra feina confirmen que la informació sintàctica és mol útil per aprendre automàticament a valorar la plausibilitat de les respostes candidates, millorant els resultats previs tan en transcripcions manuals com transcripcions automàtiques, descomptat que la qualitat de l'ASR sigui molt baixa. En general, el rendiment del nostre sistema és comparable o millor que els altres sistemes pertanyents a l'estat-del'art, confirmant així la validesa de la nostra aproximació