Proceedings of the sixth international workshop on Exploiting semantic annotations in information retrieval
Notice bibliographique
Résumé
These proceedings contain the contributed papers of the Sixth Workshop on Exploiting Semantic Annotations in Information Retrieval (ESAIR 2013), held at CIKM 2013 in San Francisco, on October 28, 2013. After successful workshops at ECIR'08 in Glasgow, WSDM'09 in Barcelona, CIKM'10 in Toronto, CIKM'11 in Glasgow, and CIKM'12 at Maui, this year's workshop will focus on the need to include large-scale knowledge resources and on annotations beyond the topical dimension. There is a need to include the currently emerging knowledge resources (such as DBpedia, Freebase) as underlying semantic model giving access to an unprecedented scope and detail of factual information. There is also a need to include annotations beyond the topical dimension (think of sentiment, reading level, prerequisite level, etc) that contain vital cues for matching the specific needs and profile of the searcher at hand. ESAIR'13 will be a real workshop where researchers from these different disciplines will work together to identify natural use cases, barriers to success, and work on ways of addressing them: Application/Use Case: What are use cases that make obvious the need for semantic annotation of information? What tasks cannot be solved by document retrieval using the traditional bagof- words? What is keeping searchers from exploring these powerful search requests? What impact has the web of data with more and more information in preprocessed form? Annotations: What types of annotation are available? Are there crucial differences between author-, software-, user-, and machine-generated annotations? Do we annotate types/classes/categories (person) or instances (Albert Einstein)? How similar or different are linked data and annotated text? What are the limitations of the current annotations schemes, and how to overcome them? Rich Context: Do we annotate text? Or also search requests and interactions, and their broader context? Besides personalization and geo-positional information, mobiles have a wide and growing range of locational, mechanical and even biometrical sensor data available to them. Can kick-start the query by inferring task and situational context? (Un)certainty: How should we interpret the annotations? Can we reliably link textual annotations to known entity catalogs? Can expect a messy world to be captured in a clean set of meaningful categories? Or is all information fundamentally uncertain and only partly known? How can we fruitfully combine information retrieval and semantic web approaches? These and other related questions will be discussed at this open format workshop -- the aim is to provide paths for further research to change the way we understand information access today! The workshop will consist of three main parts: Three keynotes to help us frame the problem, and create a common understanding of the challenges: Kevyn Collins-Thompson (University of Michigan); Marti A. Hearst (University of California, Berkeley); and Dan Roth (University of Illinois at Urbana-Champaign). A boaster and poster session with 14 papers selected by the program committee from 21 submissions (a 67% acceptance rate). Each paper was reviewed by at least two members of the program committee. Breakout groups on different aspects of exploiting semantic annotations, with reports being discussed in the final session.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,002 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».