Looking at Vector Space and Language Models for IR using Density Matrices

In this work, we conduct a joint analysis of both Vector Space and Language Models for IR using the mathematical framework of Quantum Theory. We shed light on how both models allocate the space of density matrices. A density matrix is shown to be a general representational tool capable of leveraging capabilities of both VSM and LM representations thus paving the way for a new generation of retrieval models. We analyze the possible implications suggested by our findings.Comment: In Proceedings of Quantum Interaction 201

Sentence simplification, compression, and disaggregation for summarization of sophisticated documents

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134176/1/asi23576.pd

"Potentialities or Possibilities": Towards Quantum Information Science?

The use of quantum concepts and formalisms in the information sciences is assessed through an analysis of published literature. Five categories are identified: use of loose analogies and metaphors between concepts in quantum physics and library/information science; use of quantum concepts and formalisms in information retrieval; use of quantum concepts and formalisms in studying meaning and concepts; quantum social science, in areas adjacent to information science; and the qualitative application of quantum concepts in the information disciplines. Quantum issues have led to demonstrable progress in information retrieval and semantic modelling, with less clear-cut progress elsewhere. Whether there may be a future “quantum turn” in the information sciences is debated, the implications of such a turn are considered, and a research agenda outlined

Mind the gap: transitions between concepts of information in varied domains

The concept of 'information' in five different realms – technological, physical, biological, social and philosophical – is briefly examined. The 'gaps' between these conceptions are discussed, and unifying frameworks of diverse nature, including those of Shannon/Wiener, Landauer, Stonier, Bates and Floridi, are examined. The value of attempting to bridge the gaps, while avoiding shallow analogies, is explained. With information physics gaining general acceptance, and biology gaining the status of an information science, it seems rational to look for links, relationships, analogies and even helpful metaphors between them and the library/information sciences. Prospects for doing so, involving concepts of complexity and emergence, are suggested

NLP Driven Models for Automatically Generating Survey Articles for Scientific Topics.

This thesis presents new methods that use natural language processing (NLP) driven models for summarizing research in scientific fields. Given a topic query in the form of a text string, we present methods for finding research articles relevant to the topic as well as summarization algorithms that use lexical and discourse information present in the text of these articles to generate coherent and readable extractive summaries of past research on the topic. In addition to summarizing prior research, good survey articles should also forecast future trends. With this motivation, we present work on forecasting future impact of scientific publications using NLP driven features.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113407/1/rahuljha_1.pd

Mind the Gap: Transitions Between Concepts of Information in Varied Domains

The concept of 'information' in five different realms – technological, physical, biological, social and philosophical – is briefly examined. The 'gaps' between these conceptions are dis‐ cussed, and unifying frameworks of diverse nature, including those of Shannon/Wiener, Landauer, Stonier, Bates and Floridi, are examined. The value of attempting to bridge the gaps, while avoiding shallow analogies, is explained. With information physics gaining general acceptance, and biology gaining the status of an information science, it seems rational to look for links, relationships, analogies and even helpful metaphors between them and the library/information sciences. Prospects for doing so, involving concepts of complexity and emergence, are suggested

Características da informação na Teoria Quântica e suas possíveis interpretações para um objeto informacional na Ciência da Informação

Tese (doutorado)—Universidade de Brasília, Faculdade de Ciência da Informação, Programa de Pós-Graduação em Ciência da Informação, 2014.A tese é estruturada em dois momentos. O primeiro deles representa a construção de base teórica interdisciplinar que produz inter-relações entre as características da informação, na Teoria Quântica, e o fenômeno da informação como objeto da Ciência da Informação. Como resultado dessa inter-relação, é apresentado quadro de referência das características quânticas da informação, utilizado para orientar a interpretação do objeto informacional do trabalho. Tal abordagem inclui, também, a discussão do próprio conceito de informação na Ciência da Informação, a qual se dá à luz de categorias advindas das ciências naturais. No segundo momento, o quadro é aplicado para a interpretação de objeto informacional delimitado: interceptações telefônicas judiciais. O resultado pretendido, enquanto hipótese, é a demonstração de que o entendimento de características quânticas da informação, na perspectiva da Ciência da Informação, contribui para a compreensão da arquitetura da informação que descreve um objeto informacional delimitado.This thesis is structured in two moments. The first moment represents the construction of a theoretical interdisciplinary basis, aiming at producing inter-relations between the characteristics of the information - as understood in Quantum Theory - and the information phenomenon as Information Science object. The result of this inter-relation is a reference framework on the quantum characteristics of the information, used to guide the interpretation of the informational object. That approach also includes the discussion of the concept of information, in Information Science, considered the categories deriving from the natural sciences. In the second moment, the framework is applied to the interpretation of delimited informational object: judicial telephone interceptions. The result, hypothetically, is the demonstration that the understanding of quantum characteristics of the information, in view of the Information Science, contributes to the understanding of the information architecture that describes a specific informational object

Selecting and Generating Computational Meaning Representations for Short Texts

Language conveys meaning, so natural language processing (NLP) requires representations of meaning. This work addresses two broad questions: (1) What meaning representation should we use? and (2) How can we transform text to our chosen meaning representation? In the first part, we explore different meaning representations (MRs) of short texts, ranging from surface forms to deep-learning-based models. We show the advantages and disadvantages of a variety of MRs for summarization, paraphrase detection, and clustering. In the second part, we use SQL as a running example for an in-depth look at how we can parse text into our chosen MR. We examine the text-to-SQL problem from three perspectives—methodology, systems, and applications—and show how each contributes to a fuller understanding of the task.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143967/1/cfdollak_1.pd

Learning representations for Information Retrieval

La recherche d'informations s'intéresse, entre autres, à répondre à des questions comme: est-ce qu'un document est pertinent à une requête ? Est-ce que deux requêtes ou deux documents sont similaires ? Comment la similarité entre deux requêtes ou documents peut être utilisée pour améliorer l'estimation de la pertinence ? Pour donner réponse à ces questions, il est nécessaire d'associer chaque document et requête à des représentations interprétables par ordinateur. Une fois ces représentations estimées, la similarité peut correspondre, par exemple, à une distance ou une divergence qui opère dans l'espace de représentation. On admet généralement que la qualité d'une représentation a un impact direct sur l'erreur d'estimation par rapport à la vraie pertinence, jugée par un humain. Estimer de bonnes représentations des documents et des requêtes a longtemps été un problème central de la recherche d'informations. Le but de cette thèse est de proposer des nouvelles méthodes pour estimer les représentations des documents et des requêtes, la relation de pertinence entre eux et ainsi modestement avancer l'état de l'art du domaine. Nous présentons quatre articles publiés dans des conférences internationales et un article publié dans un forum d'évaluation. Les deux premiers articles concernent des méthodes qui créent l'espace de représentation selon une connaissance à priori sur les caractéristiques qui sont importantes pour la tâche à accomplir. Ceux-ci nous amènent à présenter un nouveau modèle de recherche d'informations qui diffère des modèles existants sur le plan théorique et de l'efficacité expérimentale. Les deux derniers articles marquent un changement fondamental dans l'approche de construction des représentations. Ils bénéficient notamment de l'intérêt de recherche dont les techniques d'apprentissage profond par réseaux de neurones, ou deep learning, ont fait récemment l'objet. Ces modèles d'apprentissage élicitent automatiquement les caractéristiques importantes pour la tâche demandée à partir d'une quantité importante de données. Nous nous intéressons à la modélisation des relations sémantiques entre documents et requêtes ainsi qu'entre deux ou plusieurs requêtes. Ces derniers articles marquent les premières applications de l'apprentissage de représentations par réseaux de neurones à la recherche d'informations. Les modèles proposés ont aussi produit une performance améliorée sur des collections de test standard. Nos travaux nous mènent à la conclusion générale suivante: la performance en recherche d'informations pourrait drastiquement être améliorée en se basant sur les approches d'apprentissage de représentations.Information retrieval is generally concerned with answering questions such as: is this document relevant to this query? How similar are two queries or two documents? How query and document similarity can be used to enhance relevance estimation? In order to answer these questions, it is necessary to access computational representations of documents and queries. For example, similarities between documents and queries may correspond to a distance or a divergence defined on the representation space. It is generally assumed that the quality of the representation has a direct impact on the bias with respect to the true similarity, estimated by means of human intervention. Building useful representations for documents and queries has always been central to information retrieval research. The goal of this thesis is to provide new ways of estimating such representations and the relevance relationship between them. We present four articles that have been published in international conferences and one published in an information retrieval evaluation forum. The first two articles can be categorized as feature engineering approaches, which transduce a priori knowledge about the domain into the features of the representation. We present a novel retrieval model that compares favorably to existing models in terms of both theoretical originality and experimental effectiveness. The remaining two articles mark a significant change in our vision and originate from the widespread interest in deep learning research that took place during the time they were written. Therefore, they naturally belong to the category of representation learning approaches, also known as feature learning. Differently from previous approaches, the learning model discovers alone the most important features for the task at hand, given a considerable amount of labeled data. We propose to model the semantic relationships between documents and queries and between queries themselves. The models presented have also shown improved effectiveness on standard test collections. These last articles are amongst the first applications of representation learning with neural networks for information retrieval. This series of research leads to the following observation: future improvements of information retrieval effectiveness has to rely on representation learning techniques instead of manually defining the representation space