Semantic Entities

Entity retrieval has seen a lot of interest from the research community over the past decade. Ten years ago, the expertise retrieval task gained popularity in the research community during the TREC Enterprise Track [10]. It has remained relevant ever since, while broadening to social media, to tracking the dynamics of expertise [1-5, 8, 11], and, more generally, to a range of entity retrieval tasks. In the talk, which will be given by the second author, we will point out that existing methods to entity or expert retrieval fail to address key challenges: (1) Queries and expert documents use different representations to describe the same concepts [6, 7]. Term mismatches between entities and experts [7] occur due to the inability of widely used maximum-likelihood language models to make use of semantic similarities between words [9]. (2) As the amount of available data increases, the need for more powerful approaches with greater learning capabilities than smoothed maximum-likelihood language models is obvious [13]. (3) Supervised methods for entity or expertise retrieval [5, 8] were introduced at the turn of the last decade. However, the acceleration of data availability has the major disadvantage that, in the case of supervised methods, manual annotation efforts need to sustain a similar order of growth. This calls for the further development of unsupervised methods. (4) According to some entity or expertise retrieval methods, a language model is constructed for every document in the collection. These methods lack efficient query capabilities for large document collections, as each query term needs to be matched against every document [2]. In the talk we will discuss a recently proposed solution [12] that has a strong emphasis on unsupervised model construction, efficient query capabilities and, most importantly, semantic matching between query terms and candidate entities. We show that the proposed approach improves retrieval performance compared to generative language models mainly due to its ability to perform semantic matching [7]. The proposed method does not require any annotations or supervised relevance judgments and is able to learn from raw textual evidence and document-candidate associations alone. The purpose of the proposal is to provide insight in how we avoid explicit annotations and feature engineering and still obtain semantically meaningful retrieval results. In the talk we will provide a comparative error analysis between the proposed semantic entity retrieval model and traditional generative language models that perform exact matching, which yields important insights in the relative strengths of semantic matching and exact matching for the expert retrieval task in particular and entity retrieval in general. We will also discuss extensions of the proposed model that are meant to deal with scalability and dynamic aspects of entity and expert retrieval

Short Text Categorization using World Knowledge

The content of the World Wide Web is drastically multiplying, and thus the amount of available online text data is increasing every day. Today, many users contribute to this massive global network via online platforms by sharing information in the form of a short text. Such an immense amount of data covers subjects from all the existing domains (e.g., Sports, Economy, Biology, etc.). Further, manually processing such data is beyond human capabilities. As a result, Natural Language Processing (NLP) tasks, which aim to automatically analyze and process natural language documents have gained significant attention. Among these tasks, due to its application in various domains, text categorization has become one of the most fundamental and crucial tasks. However, the standard text categorization models face major challenges while performing short text categorization, due to the unique characteristics of short texts, i.e., insufficient text length, sparsity, ambiguity, etc. In other words, the conventional approaches provide substandard performance, when they are directly applied to the short text categorization task. Furthermore, in the case of short text, the standard feature extraction techniques such as bag-of-words suffer from limited contextual information. Hence, it is essential to enhance the text representations with an external knowledge source. Moreover, the traditional models require a significant amount of manually labeled data and obtaining labeled data is a costly and time-consuming task. Therefore, although recently proposed supervised methods, especially, deep neural network approaches have demonstrated notable performance, the requirement of the labeled data remains the main bottleneck of these approaches. In this thesis, we investigate the main research question of how to perform \textit{short text categorization} effectively \textit{without requiring any labeled data} using knowledge bases as an external source. In this regard, novel short text categorization models, namely, Knowledge-Based Short Text Categorization (KBSTC) and Weakly Supervised Short Text Categorization using World Knowledge (WESSTEC) have been introduced and evaluated in this thesis. The models do not require any hand-labeled data to perform short text categorization, instead, they leverage the semantic similarity between the short texts and the predefined categories. To quantify such semantic similarity, the low dimensional representation of entities and categories have been learned by exploiting a large knowledge base. To achieve that a novel entity and category embedding model has also been proposed in this thesis. The extensive experiments have been conducted to assess the performance of the proposed short text categorization models and the embedding model on several standard benchmark datasets

Arcabouço de arquitetura da informação para ciclo de vida de projeto de vocabulário controlado : uma aplicação em Engenharia de Software

Tese (doutorado)—Universidade de Brasília, Faculdade de Ciência da Informação, Programa de Pós-Graduação em Ciência da Informação, 2017.A pesquisa que resultou nesta tese investigou processos de desenvolvimento e de avaliação de vocabulários controlados. Esta tese inclui os seguintes elementos: resultado de pesquisa bibliográfica sobre arquitetura da informação, recuperação da informação, organização da informação e representação da informação; proposta de arcabouço para ciclo de vida de projeto de vocabulário controlado; e exemplo de uso de elementos desse arcabouço na construção de um protótipo de vocabulário controlado no domínio da Engenharia de Software. O arcabouço proposto é composto por arquitetura de referência, modelo de domínio, modelo de qualidade e lista de atividades. Entre os elementos do modelo de qualidade proposto, existe uma lista de características de qualidade de vocabulários controlados. Os modelos propostos estão parcialmente alinhados a ferramentas semânticas existentes.The research that resulted in this thesis has investigated development and evaluation processes of controlled vocabularies. This thesis includes the following elements: results of a bibliographic research on information architecture, information recovery, information organization and information representation; proposal of a framework for controlled vocabulary project life cycle; and example of use of this framework during the construction of a prototype of a controlled vocabulary on the Software Engineering domain. The proposed framework is composed of reference architecture, domain model, quality model and list of activities. Among the elements of the proposed quality model, there is a list of controlled vocabulary quality characteristics. The proposed models are partially aligned to existing semantic tools