From Frequency to Meaning: Vector Space Models of Semantics

Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics are beginning to address these limits. This paper surveys the use of VSMs for semantic processing of text. We organize the literature on VSMs according to the structure of the matrix in a VSM. There are currently three broad classes of VSMs, based on term-document, word-context, and pair-pattern matrices, yielding three classes of applications. We survey a broad range of applications in these three categories and we take a detailed look at a specific open source project in each category. Our goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs for those who are already familiar with the area, and to provide pointers into the literature for those who are less familiar with the field

An Analysis of Using Expert Systems and Intelligent Agents for the Virtual Library Project at the Naval Surface Warfare Center-Carderock Division

The Virtual Library Project1 at the Naval Surface Warfare Center/Carderock Division (NSWC/CD) is being developed to facilitate the incorporation and use of library documents via the Internet. These documents typically relate to the design and manufacture of ships for the U.S. Navy Fleet. As such, the libraries will store documents that contain not only text but also images, graphs and design configurations. Because of the dynamic nature of digital documents, particularly those related to design, rapid and effective cataloging of these documents becomes challenging. We conducted a research study to analyze the use of expert systems and intelligent agents to support the function of cataloging digital documents. This chapter provides an overview of past research in the use of expert systems and intelligent agents for cataloging digital documents and discusses our recommendations based on NSWC/CD’s requirements

Similarity of Semantic Relations

There are at least two kinds of similarity. Relational similarity is correspondence between relations, in contrast with attributional similarity, which is correspondence between attributes. When two words have a high degree of attributional similarity, we call them synonyms. When two pairs of words have a high degree of relational similarity, we say that their relations are analogous. For example, the word pair mason:stone is analogous to the pair carpenter:wood. This paper introduces Latent Relational Analysis (LRA), a method for measuring relational similarity. LRA has potential applications in many areas, including information extraction, word sense disambiguation, and information retrieval. Recently the Vector Space Model (VSM) of information retrieval has been adapted to measuring relational similarity, achieving a score of 47% on a collection of 374 college-level multiple-choice word analogy questions. In the VSM approach, the relation between a pair of words is characterized by a vector of frequencies of predefined patterns in a large corpus. LRA extends the VSM approach in three ways: (1) the patterns are derived automatically from the corpus, (2) the Singular Value Decomposition (SVD) is used to smooth the frequency data, and (3) automatically generated synonyms are used to explore variations of the word pairs. LRA achieves 56% on the 374 analogy questions, statistically equivalent to the average human score of 57%. On the related problem of classifying semantic relations, LRA achieves similar gains over the VSM

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

Human-Level Performance on Word Analogy Questions by Latent Relational Analysis

This paper introduces Latent Relational Analysis (LRA), a method for measuring relational similarity. LRA has potential applications in many areas, including information extraction, word sense disambiguation, machine translation, and information retrieval. Relational similarity is correspondence between relations, in contrast with attributional similarity, which is correspondence between attributes. When two words have a high degree of attributional similarity, we call them synonyms. When two pairs of words have a high degree of relational similarity, we say that their relations are analogous. For example, the word pair mason/stone is analogous to the pair carpenter/wood; the relations between mason and stone are highly similar to the relations between carpenter and wood. Past work on semantic similarity measures has mainly been concerned with attributional similarity. For instance, Latent Semantic Analysis (LSA) can measure the degree of similarity between two words, but not between two relations. Recently the Vector Space Model (VSM) of information retrieval has been adapted to the task of measuring relational similarity, achieving a score of 47% on a collection of 374 college-level multiple-choice word analogy questions. In the VSM approach, the relation between a pair of words is characterized by a vector of frequencies of predefined patterns in a large corpus. LRA extends the VSM approach in three ways: (1) the patterns are derived automatically from the corpus (they are not predefined), (2) the Singular Value Decomposition (SVD) is used to smooth the frequency data (it is also used this way in LSA), and (3) automatically generated synonyms are used to explore reformulations of the word pairs. LRA achieves 56% on the 374 analogy questions, statistically equivalent to the average human score of 57%. On the related problem of classifying noun-modifier relations, LRA achieves similar gains over the VSM, while using a smaller corpus

Application of Information Retrieval Techniques to Heterogeneous Databases in the Virtual Distributed Laboratory

The Department of Defense (DoD) maintains thousands of Synthetic Aperture Radar (SAR), Infrared (IR), Hyper-Spectral intelligence imagery and Electro-Optical (EO) target signature data. These images are essential to evaluating and testing individual algorithm methodologies and development techniques within the Automatic Target Recognition (ATR) community. The Air Force Research Laboratory Sensors Directorate (AFRL/SN) has proposed the Virtual Distributed Laboratory (VDL) to maintain a central collection of the associated imagery metadata and a query mechanism to retrieve the desired imagery. All imagery metadata is stored in relational database format for access from agencies throughout the federal government and large civilian universities. Each set of imagery is independently maintained at each agency s location along with a local copy of the associated metadata that is periodically updated and sent to the VDL. This research focuses on applying information retrieval techniques to the multiple heterogeneous imagery metadata databases to present users the most relevant images based on user defined search criteria. More specifically, it defines a hierarchical concept thesaurus development methodology to handle the complexities of heterogeneous databases and the application of two classic information retrieval models. The results indicate this type of thesaurus-based approach can significantly increase the precision and recall levels of retrieving relevant documents

Improving search engines with open Web-based SKOS vocabularies

Dissertação para obtenção do Grau de Mestre em Engenharia InformáticaThe volume of digital information is increasingly larger and even though organiza-tions are making more of this information available, without the proper tools users have great difficulties in retrieving documents about subjects of interest. Good infor-mation retrieval mechanisms are crucial for answering user information needs. Nowadays, search engines are unavoidable - they are an essential feature in docu-ment management systems. However, achieving good relevancy is a difficult problem particularly when dealing with specific technical domains where vocabulary mismatch problems can be prejudicial. Numerous research works found that exploiting the lexi-cal or semantic relations of terms in a collection attenuates this problem. In this dissertation, we aim to improve search results and user experience by inves-tigating the use of potentially connected Web vocabularies in information retrieval en-gines. In the context of open Web-based SKOS vocabularies we propose a query expan-sion framework implemented in a widely used IR system (Lucene/Solr), and evaluated using standard IR evaluation datasets. The components described in this thesis were applied in the development of a new search system that was integrated with a rapid applications development tool in the context of an internship at Quidgest S.A.Fundação para a Ciência e Tecnologia - ImTV research project, in the context of the UTAustin-Portugal collaboration (UTA-Est/MAI/0010/2009); QSearch project (FCT/Quidgest

Collaborative tagging as a knowledge organisation and resource discovery tool

The purpose of the paper is to provide an overview of the collaborative tagging phenomenon and explore some of the reasons for its emergence. Design/methodology/approach - The paper reviews the related literature and discusses some of the problems associated with, and the potential of, collaborative tagging approaches for knowledge organisation and general resource discovery. A definition of controlled vocabularies is proposed and used to assess the efficacy of collaborative tagging. An exposition of the collaborative tagging model is provided and a review of the major contributions to the tagging literature is presented. Findings - There are numerous difficulties with collaborative tagging systems (e.g. low precision, lack of collocation, etc.) that originate from the absence of properties that characterise controlled vocabularies. However, such systems can not be dismissed. Librarians and information professionals have lessons to learn from the interactive and social aspects exemplified by collaborative tagging systems, as well as their success in engaging users with information management. The future co-existence of controlled vocabularies and collaborative tagging is predicted, with each appropriate for use within distinct information contexts: formal and informal. Research limitations/implications - Librarians and information professional researchers should be playing a leading role in research aimed at assessing the efficacy of collaborative tagging in relation to information storage, organisation, and retrieval, and to influence the future development of collaborative tagging systems. Practical implications - The paper indicates clear areas where digital libraries and repositories could innovate in order to better engage users with information. Originality/value - At time of writing there were no literature reviews summarising the main contributions to the collaborative tagging research or debate