An Algorithmic Solution for Management of Related Text Objects with Application in Phytopharmacy

This paper presents an algorithmic solution for management of related text objects, in which are integrated algorithms for their extraction from paper or electronic format, for their storage and processing in a relational database. The developed algorithms for data extraction and data analysis enable one to find specific features and relations between the text objects from the database. The algorithmic solution is applied to data from the field of phytopharmacy in Bulgaria. It can be used as a tool and methodology for other subject areas where there are complex relationships between text objects

Utilisation de PLSI en recherche d'information

The PLSI model (“Probabilistic Latent Semantic Indexing”) offers a document indexing scheme based on probabilistic latent category models. It entailed applications in diverse fields, notably in information retrieval (IR). Nevertheless, PLSI cannot process documents not seen during parameter inference, a major liability for queries in IR. A method known as “folding-in” allows to circumvent this problem up to a point, but has its own weaknesses. The present paper introduces a new document-query similarity measure for PLSI based on language models that entirely avoids the problem a query projection. We compare this similarity to Fisher kernels, the state of the art similarities for PLSI. Moreover, we present an evaluation of PLSI on a particularly large training set of almost 7500 document and over one million term occurrence large, created from the TREC–AP collection

Rôle de la matrice d'information et pondération des composantes dans les noyaux de Fisher pour PLSI

ABSTRACT. An information-geometric approach for document similarities in the framework of “Probabilistic Latent Semantic Indexing” was first proposed by T. Hofmann (2000) and later extended (“revisited”) by Nyffenegger et al. (2006). This paper presents an in-depth study and revision of these models by (1) providing a simpler unified description framework, (2) investigating the role of the Fisher Information Matrix G(θ), and (3) analyzing the impact of latent “topic” parameters in such models. It furthermore provides new experimental results on larger collections coming from the TREC–AP evaluation corpus

Topic models for short text data

Topic models are known to suffer from sparsity when applied to short text data. The problem is caused by a reduced number of observations available for a reliable inference (i.e.: the words in a document). A popular heuristic utilized to overcome this problem is to perform before training some form of document aggregation by context (e.g.: author, hashtag). We dedicated one part of this dissertation to modeling explicitly the implicit assumptions of the document aggregation heuristic and applying it to two well known model architectures: a mixture and an admixture. Our findings indicate that an admixture model benefits more from aggregation compared to a mixture model which rarely improved over its baseline (the standard mixture). We also find that the state of the art in short text data can be surpassed as long as every context is shared by a small number of documents. In the second part of the dissertation we develop a more general purpose topic model which can also be used when contextual information is not available. The proposed model is formulated around the observation that in normal text data, a classic topic model like an admixture works well because patterns of word co-occurrences arise across the documents. However, the possibility of such patterns to arise in a short text dataset is reduced. The model assumes every document is a bag of word co-occurrences, where each co-occurrence belongs to a latent topic. The documents are enhanced a priori with related co-occurrences from the other documents, such that the collection will have a greater chance of exhibiting word patterns. The proposed model performs well managing to surpass the state of the art and popular topic model baselines

Topic models for short text data

Topic models are known to suffer from sparsity when applied to short text data. The problem is caused by a reduced number of observations available for a reliable inference (i.e.: the words in a document). A popular heuristic utilized to overcome this problem is to perform before training some form of document aggregation by context (e.g.: author, hashtag). We dedicated one part of this dissertation to modeling explicitly the implicit assumptions of the document aggregation heuristic and applying it to two well known model architectures: a mixture and an admixture. Our findings indicate that an admixture model benefits more from aggregation compared to a mixture model which rarely improved over its baseline (the standard mixture). We also find that the state of the art in short text data can be surpassed as long as every context is shared by a small number of documents. In the second part of the dissertation we develop a more general purpose topic model which can also be used when contextual information is not available. The proposed model is formulated around the observation that in normal text data, a classic topic model like an admixture works well because patterns of word co-occurrences arise across the documents. However, the possibility of such patterns to arise in a short text dataset is reduced. The model assumes every document is a bag of word co-occurrences, where each co-occurrence belongs to a latent topic. The documents are enhanced a priori with related co-occurrences from the other documents, such that the collection will have a greater chance of exhibiting word patterns. The proposed model performs well managing to surpass the state of the art and popular topic model baselines

Mining and Analyzing the Academic Network

Social Network research has attracted the interests of many researchers, not only in analyzing the online social networking applications, such as Facebook and Twitter, but also in providing comprehensive services in scientific research domain. We define an Academic Network as a social network which integrates scientific factors, such as authors, papers, affiliations, publishing venues, and their relationships, such as co-authorship among authors and citations among papers. By mining and analyzing the academic network, we can provide users comprehensive services as searching for research experts, published papers, conferences, as well as detecting research communities or the evolutions hot research topics. We can also provide recommendations to users on with whom to collaborate, whom to cite and where to submit.In this dissertation, we investigate two main tasks that have fundamental applications in the academic network research. In the first, we address the problem of expertise retrieval, also known as expert finding or ranking, in which we identify and return a ranked list of researchers, based upon their estimated expertise or reputation, to user-specified queries. In the second, we address the problem of research action recommendation (prediction), specifically, the tasks of publishing venue recommendation, citation recommendation and coauthor recommendation. For both tasks, to effectively mine and integrate heterogeneous information and therefore develop well-functioning ranking or recommender systems is our principal goal. For the task of expertise retrieval, we first proposed or applied three modified versions of PageRank-like algorithms into citation network analysis; we then proposed an enhanced author-topic model by simultaneously modeling citation and publishing venue information; we finally incorporated the pair-wise learning-to-rank algorithm into traditional topic modeling process, and further improved the model by integrating groups of author-specific features. For the task of research action recommendation, we first proposed an improved neighborhood-based collaborative filtering approach for publishing venue recommendation; we then applied our proposed enhanced author-topic model and demonstrated its effectiveness in both cited author prediction and publishing venue prediction; finally we proposed an extended latent factor model that can jointly model several relations in an academic environment in a unified way and verified its performance in four recommendation tasks: the recommendation on author-co-authorship, author-paper citation, paper-paper citation and paper-venue submission. Extensive experiments conducted on large-scale real-world data sets demonstrated the superiority of our proposed models over other existing state-of-the-art methods