Entity-based Enrichment for Information Extraction and Retrieval

The goal of this work is to leverage cross-document entity relationships for improved understanding of queries and documents. We define an entity to be a thing or concept that exists in the world, such as a politician, a battle, a film, or a color. Entity-based enrichment (EBE) is a new expansion model for both queries and documents using features from similar entitymentions in the document collection and external knowledge resources. It uses task-specific features from entities beyond words that include: name aliases, fine-grained entity types, categories, and relationships to other entities. EBE addresses the problem of sparse or noisy local evidence due to multiple topics, implicit context, or informal writing. With the ultimate goal of improving information retrieval effectiveness, we start from unstructured text and through information extraction build up rich entity-based representations linked to external knowledge resources. We study the application ofentity-based enrichment to each step in the pipeline: 1) Named entity recognition, 2) Entity linking, and 3) Ad hoc document retrieval. The empirical results for EBE in each of these tasks shows significant improvements. Our first application of entity-based enrichment is the task of detecting entities in named entity recognition. We enrich the representation of observed words likely to represent entities. We perform weighted feature copying of recognition features from similar tokens in the corpus and external collections. The evaluation shows statistically significant improvements on in-domain newswire accuracy and demonstrates that the models are more robust on out-of-domain data. In the second part of this work, we apply EBE to the task of entity linking. The proposed entity linking method for disambiguating the detected mentions to entries in an external knowledge base is based on information retrieval. Theneighborhood relevance model, an enrichment model, identifies salient associations between an entity mention and otherentity mentions in the document. The results show that the enrichment model outperforms other context models and results in a system that is competitive with leading methods. Using the constructed entity representation, the final task is ad hoc document retrieval. We enrich the query representation with entity features. Retrieval is performed over documents annotated with entities linked to Wikipedia and Freebase from our system as well as the publicly available Google FACC1 annotations. To effectively leverage linked entity features, we extend dependency-based retrieval models to include structured attributes. We also define a new query-specific entity context model which builds a model for disambiguated entities from retrieved documents. Our results demonstrate significant improvements over competitive query expansion baselines for several standard test collections

Human-competitive automatic topic indexing

Topic indexing is the task of identifying the main topics covered by a document. These are useful for many purposes: as subject headings in libraries, as keywords in academic publications and as tags on the web. Knowing a document's topics helps people judge its relevance quickly. However, assigning topics manually is labor intensive. This thesis shows how to generate them automatically in a way that competes with human performance. Three kinds of indexing are investigated: term assignment, a task commonly performed by librarians, who select topics from a controlled vocabulary; tagging, a popular activity of web users, who choose topics freely; and a new method of keyphrase extraction, where topics are equated to Wikipedia article names. A general two-stage algorithm is introduced that first selects candidate topics and then ranks them by significance based on their properties. These properties draw on statistical, semantic, domain-specific and encyclopedic knowledge. They are combined using a machine learning algorithm that models human indexing behavior from examples. This approach is evaluated by comparing automatically generated topics to those assigned by professional indexers, and by amateurs. We claim that the algorithm is human-competitive because it chooses topics that are as consistent with those assigned by humans as their topics are with each other. The approach is generalizable, requires little training data and applies across different domains and languages

Term-driven E-Commerce

Die Arbeit nimmt sich der textuellen Dimension des E-Commerce an. Grundlegende Hypothese ist die textuelle Gebundenheit von Information und Transaktion im Bereich des elektronischen Handels. Überall dort, wo Produkte und Dienstleistungen angeboten, nachgefragt, wahrgenommen und bewertet werden, kommen natürlichsprachige Ausdrücke zum Einsatz. Daraus resultiert ist zum einen, wie bedeutsam es ist, die Varianz textueller Beschreibungen im E-Commerce zu erfassen, zum anderen können die umfangreichen textuellen Ressourcen, die bei E-Commerce-Interaktionen anfallen, im Hinblick auf ein besseres Verständnis natürlicher Sprache herangezogen werden

Statistical learning techniques for text categorization with sparse labeled data

Many applications involve learning a supervised classifier from very few explicitly labeled training examples, since the cost of manually labeling the training data is often prohibitively high. For instance, we expect a good classifier to learn our interests from a few example books or movies we like, and recommend similar ones in the future, or we expect a search engine to give more personalized search results based on whatever little it learned about our past queries and clicked documents. There is thus a need for classification techniques capable of learning from sparse labeled data, by exploiting additional information about the classification task at hand (e.g., background knowledge) or by employing more sophisticated features (e.g., n-gram sequences, trees, graphs). In this thesis, we focus on two approaches for overcoming the bottleneck of sparse labeled data. We first propose the Inductive/Transductive Latent Model (ILM/TLM), which is a new generative model for text documents. ILM/TLM has various building blocks designed to facilitate the integration of background knowledge (e.g., unlabeled documents, ontologies of concepts, encyclopedia) into the process of learning from small training data. Our method can be used for inductive and transductive learning and achieves significant gains over state-of-the-art methods for very small training sets. Second, we propose Structured Logistic Regression (SLR), which is a new coordinate-wise gradient ascent technique for learning logistic regression in the space of all (word or character) sequences in the training data. SLR exploits the inherent structure of the n-gram feature space in order to automatically provide a compact set of highly discriminative n-gram features. Our detailed experimental study shows that while SLR achieves similar classification results to those of the state-of-the-art methods (which use all n-gram features given explicitly), it is more than an order of magnitude faster than its opponents. The techniques presented in this thesis can be used to advance the technologies for automatically and efficiently building large training sets, therefore reducing the need for spending human computation on this task.Viele Anwendungen benutzen Klassifikatoren, die auf dünn gesäten Trainingsdaten lernen müssen, da es oft aufwändig ist, Trainingsdaten zur Verfügung zu stellen. Ein Beispiel für solche Anwendungen sind Empfehlungssysteme, die auf der Basis von sehr wenigen Büchern oder Filmen die Interessen des Benutzers erraten müssen, um ihm ähnliche Bücher oder Filme zu empfehlen. Ein anderes Beispiel sind Suchmaschinen, die sich auf den Benutzer einzustellen versuchen, auch wenn sie bisher nur sehr wenig Information über den Benutzer in Form von gestellten Anfragen oder geklickten Dokumenten besitzen. Wir benötigen also Klassifikationstechniken, die von dünn gesäten Trainingsdaten lernen können. Dies kann geschehen, indem zusätzliche Information über die Klassifikationsaufgabe ausgenutzt wird (z.B. mit Hintergrundwissen) oder indem raffiniertere Merkmale verwendet werden (z.B. n-Gram-Folgen, Bäume oder Graphen). In dieser Arbeit stellen wir zwei Ansätze vor, um das Problem der dünn gesäten Trainingsdaten anzugehen. Als erstes schlagen wir das Induktiv-Transduktive Latente Modell (ILM/TLM) vor, ein neues generatives Modell für Text-Dokumente. Das ILM/TLM verfügt über mehrere Komponenten, die es erlauben, Hintergrundwissen (wie z.B. nicht Klassifizierte Dokumente, Konzeptontologien oder Enzyklopädien) in den Lernprozess mit einzubeziehen. Diese Methode kann sowohl für induktives als auch für transduktives Lernen eingesetzt werden. Sie schlägt die modernsten Alternativmethoden signifikant bei dünn gesäten Trainingsdaten. Zweitens schlagen wir Strukturierte Logistische Regression (SLR) vor, ein neues Gradientenverfahren zum koordinatenweisen Lernen von logistischer Regression im Raum aller Wortfolgen oder Zeichenfolgen in den Trainingsdaten. SLR nutzt die inhärente Struktur des n-Gram-Raums aus, um automatisch hoch-diskriminative Merkmale zu finden. Unsere detaillierten Experimente zeigen, dass SLR ähnliche Ergebnisse erzielt wie die modernsten Konkurrenzmethoden, allerdings dabei um mehr als eine Größenordnung schneller ist. Die in dieser Arbeit vorgestellten Techniken verbessern das Maschinelle Lernen auf dünn gesäten Trainingsdaten und verringern den Bedarf an manueller Arbeit

Semantic enrichment of knowledge sources supported by domain ontologies

This thesis introduces a novel conceptual framework to support the creation of knowledge representations based on enriched Semantic Vectors, using the classical vector space model approach extended with ontological support. One of the primary research challenges addressed here relates to the process of formalization and representation of document contents, where most existing approaches are limited and only take into account the explicit, word-based information in the document. This research explores how traditional knowledge representations can be enriched through incorporation of implicit information derived from the complex relationships (semantic associations) modelled by domain ontologies with the addition of information presented in documents. The relevant achievements pursued by this thesis are the following: (i) conceptualization of a model that enables the semantic enrichment of knowledge sources supported by domain experts; (ii) development of a method for extending the traditional vector space, using domain ontologies; (iii) development of a method to support ontology learning, based on the discovery of new ontological relations expressed in non-structured information sources; (iv) development of a process to evaluate the semantic enrichment; (v) implementation of a proof-of-concept, named SENSE (Semantic Enrichment kNowledge SourcEs), which enables to validate the ideas established under the scope of this thesis; (vi) publication of several scientific articles and the support to 4 master dissertations carried out by the department of Electrical and Computer Engineering from FCT/UNL. It is worth mentioning that the work developed under the semantic referential covered by this thesis has reused relevant achievements within the scope of research European projects, in order to address approaches which are considered scientifically sound and coherent and avoid “reinventing the wheel”.European research projects - CoSpaces (IST-5-034245), CRESCENDO (FP7-234344) and MobiS (FP7-318452

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

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges