Learning Relatedness Measures for Entity Linking

Entity Linking is the task of detecting, in text documents, relevant mentions to entities of a given knowledge base. To this end, entity-linking algorithms use several signals and features extracted from the input text or from the knowl- edge base. The most important of such features is entity relatedness. Indeed, we argue that these algorithms benefit from maximizing the relatedness among the relevant enti- ties selected for annotation, since this minimizes errors in disambiguating entity-linking. The definition of an e↵ective relatedness function is thus a crucial point in any entity-linking algorithm. In this paper we address the problem of learning high-quality entity relatedness functions. First, we formalize the problem of learning entity relatedness as a learning-to-rank problem. We propose a methodology to create reference datasets on the basis of manually annotated data. Finally, we show that our machine-learned entity relatedness function performs better than other relatedness functions previously proposed, and, more importantly, improves the overall performance of dif- ferent state-of-the-art entity-linking algorithms

SEL: A unified algorithm for entity linking and saliency detection

The Entity Linking task consists in automatically identifying and linking the entities mentioned in a text to their URIs in a given Knowledge Base, e.g., Wikipedia. Entity Linking has a large impact in several text analysis and information retrieval related tasks. This task is very challenging due to natural language ambiguity. However, not all the entities mentioned in a document have the same relevance and utility in understanding the topics being discussed. Thus, the related problem of identifying the most relevant entities present in a document, also known as Salient Entities, is attracting increasing interest. In this paper we propose SEL, a novel supervised two-step algorithm comprehensively addressing both entity linking and saliency detection. The first step is based on a classifier aimed at identifying a set of candidate entities that are likely to be mentioned in the document, thus maximizing the precision of the method without hindering its recall. The second step is still based on machine learning, and aims at choosing from the previous set the entities that actually occur in the document. Indeed, we tested two different versions of the second step, one aimed at solving only the entity linking task, and the other that, besides detecting linked entities, also scores them according to their saliency. Experiments conducted on two different datasets show that the proposed algorithm outperforms state-of-the-art competitors, and is able to detect salient entities with high accuracy

The Impact of Negative Samples on Learning to Rank

Learning-to-Rank (LtR) techniques leverage machine learning algorithms and large amounts of training data to induce high-quality ranking functions. Given a set of documents and a user query, these functions are able to predict a score for each of the documents that is in turn exploited to induce a relevance ranking. .e e.ectiveness of these learned functions has been proved to be signi.cantly a.ected by the data used to learn them. Several analysis and document selection strategies have been proposed in the past to deal with this aspect. In this paper we review the state-of-the-art proposals and we report the results of a preliminary investigation of a new sampling strategy aimed at reducing the number of not relevant query-document pairs, so to signi.cantly decrease the training time of the learning algorithm and to increase the .nal e.ectiveness of the model by reducing noise and redundancy in the training set

Improving the Efficiency and Effectiveness of Document Understanding in Web Search

Web Search Engines (WSEs) are probably nowadays the most complex information systems since they need to handle an ever-increasing amount of web pages and match them with the information needs expressed in short and often ambiguous queries by a multitude of heterogeneous users. In addressing this challenging task they have to deal at an unprecedented scale with two classic and contrasting IR problems: the satisfaction of effectiveness requirements and efficiency constraints. While the former refers to the user-perceived quality of query results, the latter regards the time spent by the system in retrieving and presenting them to the user. Due to the importance of text data in the Web, natural language understanding techniques acquired popularity in the latest years and are profitably exploited by WSEs to overcome ambiguities in natural language queries given for example by polysemy and synonymy. A promising approach in this direction is represented by the so-called Web of Data, a paradigm shift which originates from the Semantic Web and promotes the enrichment of Web documents with the semantic concepts they refer to. Enriching unstructured text with an entity-based representation of documents - where entities can precisely identify persons, companies, locations, etc. - allows in fact, a remarkable improvement of retrieval effectiveness to be achieved. In this thesis, we argue that it is possible to improve both efficiency and effectiveness of document understanding in Web search by exploiting learning-to-rank, i.e., a supervised technique aimed at learning effective ranking functions from training data. Indeed, on one hand, enriching documents with machine-learnt semantic annotations leads to an improvement of WSE effectiveness, since the retrieval of relevant documents can exploit a finer comprehension of the documents. On the other hand, by enhancing the efficiency of learning to rank techniques we can improve both WSE efficiency and effectiveness, since a faster ranking technique can reduce query processing time or, alternatively, allow a more complex and accurate ranking model to be deployed. The contribution of this thesis are manifold: i) we discuss a novel machine- learnt measure for estimating the relatedness among entities mentioned in a document, thus enhancing the accuracy of text disambiguation tech- niques for document understanding; ii) we propose novel machine-learnt technique to label the mentioned entities according to a notion of saliency, where the most salient entities are those that have the highest utility in understanding the topics discussed; iii) we enhance state-of-the-art ensemble-based ranking models by means of a general learning-to-rank framework that is able to iteratively prune the less useful part of the ensemble and re-weight the remaining part accordingly to the loss function adopted. Finally, we share with the research community working in this area several open source tools to promote collaborative developments and favoring the reproducibility of research results

Developmental risks and psychosocial adjustment among low-income Brazilian youth

Exposure to developmental risks in three domains (community, economic, and family), and relations between risks and psychosocial well-being, were examined among 918 impoverished Brazilian youth aged 14-19 (M = 15.8 years, 51.9% female) recruited in low-income neighborhoods in one city in Southern Brazil. High levels of developmental risks were reported, with levels and types of risks varying by gender, age, and (to a lesser extent) race. Associations between levels of risks in the various domains and indicators of psychological (e.g., self-esteem, negative emotionality) and behavioral (e.g., substance use) adjustment differed for male and female respondents. Findings build on prior research investigating the development of young people in conditions of pervasive urban poverty and reinforce the value of international research in this endeavor

Cite-as-you-Write: Raccomandazione di citazioni per articoli scientifici

La presente tesi discute lo sviluppo e la valutazione di un sistema di raccomandazione di citazioni di pubblicazioni scientifiche durante la scrittura di un articolo. This thesis describes the development and the evaluation of an online recommender system for citations of scientific papers that aims to provide suggestions for extending the bibliography of a work in progress text

Dexter 2.0 - an Open Source Tool for Semantically Enriching Data

Abstract. Entity Linking (EL) enables to automatically link unstruc-tured data with entities in a Knowledge Base. Linking unstructured data (like news, blog posts, tweets) has several important applications: for ex-ample it allows to enrich the text with external useful contents or to improve the categorization and the retrieval of documents. In the latest years many effective approaches for performing EL have been proposed but only a few authors published the code to perform the task. In this work we describe Dexter 2.0, a major revision of our open source frame-work to experiment with different EL approaches. We designed Dexter in order to make it easy to deploy and to use. The new version provides several important features: the possibility to adopt different EL strate-gies at run-time and to annotate semi-structured documents, as well as a well-documented REST-API. In this demo we present the current state of the system, the improvements made, its architecture and the APIs provided.

Selective Gradient Boosting for Effective Learning to Rank

Learning an effective ranking function from a large number of query-document examples is a challenging task. Indeed, training sets where queries are associated with a few relevant documents and a large number of irrelevant ones are required to model real scenarios of Web search production systems, where a query can possibly retrieve thousands of matching documents, but only a few of them are actually relevant. In this paper, we propose Selective Gradient Boosting (SelGB), an algorithm addressing the Learning-to-Rank task by focusing on those irrelevant documents that are most likely to be mis-ranked, thus severely hindering the quality of the learned model. SelGB exploits a novel technique minimizing the mis-ranking risk, i.e., the probability that two randomly drawn instances are ranked incorrectly, within a gradient boosting process that iteratively generates an additive ensemble of decision trees. Specifically, at every iteration and on a per query basis, SelGB selectively chooses among the training instances a small sample of negative examples enhancing the discriminative power of the learned model. Reproducible and comprehensive experiments conducted on a publicly available dataset show that SelGB exploits the diversity and variety of the negative examples selected to train tree ensembles that outperform models generated by state-of-the-art algorithms by achieving improvements of NDCG@10 up to 3.2%

X-Dart: Blending dropout and pruning for efiicient learning to rank

In this paperwe propose X-Dart, a newLearning to Rank algorithm focusing on the training of robust and compact ranking models. Motivated from the observation that the last trees of MART models impact the prediction of only a few instances of the training set, we borrow from the Dart algorithm the dropout strategy consisting in temporarily dropping some of the trees from the ensemble while new weak learners are trained. However, differently from this algorithm we drop permanently these trees on the basis of smart choices driven by accuracy measured on the validation set. Experiments conducted on publicly available datasets shows that X-Dart outperforms Dart in training models providing the same effectiveness by employing up to 40% less trees