2,334 research outputs found
Applying Wikipedia to Interactive Information Retrieval
There are many opportunities to improve the interactivity of information retrieval systems beyond the ubiquitous search box. One idea is to use knowledge bases—e.g. controlled vocabularies, classification schemes, thesauri and ontologies—to organize, describe and navigate the information space. These resources are popular in libraries and specialist collections, but have proven too expensive and narrow to be applied to everyday webscale search. Wikipedia has the potential to bring structured knowledge into more widespread use. This online, collaboratively generated encyclopaedia is one of the largest and most consulted reference works in existence. It is broader, deeper and more agile than the knowledge bases put forward to assist retrieval in the past. Rendering this resource machine-readable is a challenging task that has captured the interest of many researchers. Many see it as a key step required to break the knowledge acquisition bottleneck that crippled previous efforts. This thesis claims that the roadblock can be sidestepped: Wikipedia can be applied effectively to open-domain information retrieval with minimal natural language processing or information extraction. The key is to focus on gathering and applying human-readable rather than machine-readable knowledge. To demonstrate this claim, the thesis tackles three separate problems: extracting knowledge from Wikipedia; connecting it to textual documents; and applying it to the retrieval process. First, we demonstrate that a large thesaurus-like structure can be obtained directly from Wikipedia, and that accurate measures of semantic relatedness can be efficiently mined from it. Second, we show that Wikipedia provides the necessary features and training data for existing data mining techniques to accurately detect and disambiguate topics when they are mentioned in plain text. Third, we provide two systems and user studies that demonstrate the utility of the Wikipedia-derived knowledge base for interactive information retrieval
User modeling for exploratory search on the Social Web. Exploiting social bookmarking systems for user model extraction, evaluation and integration
Exploratory search is an information seeking strategy that extends be- yond the query-and-response paradigm of traditional Information Retrieval models. Users browse through information to discover novel content and to learn more about the newly discovered things. Social bookmarking systems integrate well with exploratory search, because they allow one to search, browse, and filter social bookmarks.
Our contribution is an exploratory tag search engine that merges social bookmarking with exploratory search. For this purpose, we have applied collaborative filtering to recommend tags to users. User models are an im- portant prerequisite for recommender systems. We have produced a method to algorithmically extract user models from folksonomies, and an evaluation method to measure the viability of these user models for exploratory search. According to our evaluation web-scale user modeling, which integrates user models from various services across the Social Web, can improve exploratory search. Within this thesis we also provide a method for user model integra- tion.
Our exploratory tag search engine implements the findings of our user model extraction, evaluation, and integration methods. It facilitates ex- ploratory search on social bookmarks from Delicious and Connotea and pub- lishes extracted user models as Linked Data
Learning to Associate Words and Images Using a Large-scale Graph
We develop an approach for unsupervised learning of associations between
co-occurring perceptual events using a large graph. We applied this approach to
successfully solve the image captcha of China's railroad system. The approach
is based on the principle of suspicious coincidence. In this particular
problem, a user is presented with a deformed picture of a Chinese phrase and
eight low-resolution images. They must quickly select the relevant images in
order to purchase their train tickets. This problem presents several
challenges: (1) the teaching labels for both the Chinese phrases and the images
were not available for supervised learning, (2) no pre-trained deep
convolutional neural networks are available for recognizing these Chinese
phrases or the presented images, and (3) each captcha must be solved within a
few seconds. We collected 2.6 million captchas, with 2.6 million deformed
Chinese phrases and over 21 million images. From these data, we constructed an
association graph, composed of over 6 million vertices, and linked these
vertices based on co-occurrence information and feature similarity between
pairs of images. We then trained a deep convolutional neural network to learn a
projection of the Chinese phrases onto a 230-dimensional latent space. Using
label propagation, we computed the likelihood of each of the eight images
conditioned on the latent space projection of the deformed phrase for each
captcha. The resulting system solved captchas with 77% accuracy in 2 seconds on
average. Our work, in answering this practical challenge, illustrates the power
of this class of unsupervised association learning techniques, which may be
related to the brain's general strategy for associating language stimuli with
visual objects on the principle of suspicious coincidence.Comment: 8 pages, 7 figures, 14th Conference on Computer and Robot Vision 201
Formal concept matching and reinforcement learning in adaptive information retrieval
The superiority of the human brain in information retrieval (IR) tasks seems to come firstly
from its ability to read and understand the concepts, ideas or meanings central to documents, in
order to reason out the usefulness of documents to information needs, and secondly from its
ability to learn from experience and be adaptive to the environment. In this work we attempt to
incorporate these properties into the development of an IR model to improve document
retrieval. We investigate the applicability of concept lattices, which are based on the theory of
Formal Concept Analysis (FCA), to the representation of documents. This allows the use of
more elegant representation units, as opposed to keywords, in order to better capture
concepts/ideas expressed in natural language text. We also investigate the use of a
reinforcement leaming strategy to learn and improve document representations, based on the
information present in query statements and user relevance feedback. Features or concepts of
each document/query, formulated using FCA, are weighted separately with respect to the
documents they are in, and organised into separate concept lattices according to a subsumption
relation. Furthen-nore, each concept lattice is encoded in a two-layer neural network structure
known as a Bidirectional Associative Memory (BAM), for efficient manipulation of the
concepts in the lattice representation. This avoids implementation drawbacks faced by other
FCA-based approaches. Retrieval of a document for an information need is based on concept
matching between concept lattice representations of a document and a query. The learning
strategy works by making the similarity of relevant documents stronger and non-relevant
documents weaker for each query, depending on the relevance judgements of the users on
retrieved documents. Our approach is radically different to existing FCA-based approaches in
the following respects: concept formulation; weight assignment to object-attribute pairs; the
representation of each document in a separate concept lattice; and encoding concept lattices in
BAM structures. Furthermore, in contrast to the traditional relevance feedback mechanism, our
learning strategy makes use of relevance feedback information to enhance document
representations, thus making the document representations dynamic and adaptive to the user
interactions. The results obtained on the CISI, CACM and ASLIB Cranfield collections are
presented and compared with published results. In particular, the performance of the system is
shown to improve significantly as the system learns from experience.The School of Computing,
University of Plymouth, UK
Computational Mechanisms of Language Understanding and Use in the Brain and Behaviour
Linguistic communication is a unique characteristic of intelligent behaviour
that distinguishes humans from non-human animals. Natural language is
a structured, complex communication system supported by a variety of cognitive
functions, realized by hundreds of millions of neurons in the brain. Artificial
neural networks typically used in natural language processing (NLP) are often
designed to focus on benchmark performance, where one of the main goals is
reaching the state-of-the-art performance on a set of language tasks. Although
the advances in NLP have been tremendous in the past decade, such networks
provide only limited insights into biological mechanisms underlying linguistic
processing in the brain.
In this thesis, we propose an integrative approach to the study of
computational mechanisms underlying fundamental language processes, spanning
biologically plausible neural networks, and learning of basic communicative
abilities through environmentally grounded behaviour. In doing so, we argue for
the usage-based approach to language, where language is supported by a variety
of cognitive functions and learning mechanisms. Thus, we focus on the three
following questions: How are basic linguistic units, such as words, represented
in the brain? Which neural mechanisms operate on those representations in
cognitive tasks? How can aspects of such representations, such as associative
similarity and structure, be learned in a usage-based framework?
To answer the first two questions, we build novel, biologically realistic
models of neural function that perform different semantic processing tasks: the
Remote Associates Test (RAT) and the semantic fluency task. Both tasks have
been used in experimental and clinical environments to study organizational
principles and retrieval mechanisms from semantic memory. The models we propose
realize the mental lexicon and cognitive retrieval processes operating on that
lexicon using associative mechanisms in a biologically plausible manner. We
argue that such models are the first and only biologically plausible models
that propose specific mechanisms as well as reproduce a wide range of human
behavioural data on those tasks, further corroborating their plausibility.
To address the last question, we use an interactive, collaborative agent-based
reinforcement learning setup in a navigation task where agents learn to
communicate to solve the task. We argue that agents in such a setup learn to
jointly coordinate their actions, and develop a communication protocol that is
often optimal for the performance on the task, while exhibiting some core
properties of language, such as representational similarity structure and
compositionality, essential for associative mechanisms underlying cognitive
representations
Computational explorations of semantic cognition
Motivated by the widespread use of distributional models of semantics within the cognitive science community, we follow a computational modelling approach in order to better understand and expand the applicability of such models, as well as to test potential ways in which they can be improved and extended. We review evidence in favour of the assumption that distributional models capture important aspects of semantic cognition. We look at the models’ ability to account for behavioural data and fMRI patterns of brain activity, and investigate the structure of model-based, semantic networks. We test whether introducing affective information, obtained from a neural network model designed to predict emojis from co-occurring text, can improve the performance of linguistic and linguistic-visual models of semantics, in accounting for similarity/relatedness ratings. We find that adding visual and affective representations improves performance, especially for concrete and abstract words, respectively. We describe a processing model based on distributional semantics, in which activation spreads throughout a semantic network, as dictated by the patterns of semantic similarity between words. We show that the activation profile of the network, measured at various time points, can account for response time and accuracies in lexical and semantic decision tasks, as well as for concreteness/imageability and similarity/relatedness ratings. We evaluate the differences between concrete and abstract words, in terms of the structure of the semantic networks derived from distributional models of semantics. We examine how the structure is related to a number of factors that have been argued to differ between concrete and abstract words, namely imageability, age of acquisition, hedonic valence, contextual diversity, and semantic diversity. We use distributional models to explore factors that might be responsible for the poor linguistic performance of children suffering from Developmental Language Disorder. Based on the assumption that certain model parameters can be given a psychological interpretation, we start from “healthy” models, and generate “lesioned” models, by manipulating the parameters. This allows us to determine the importance of each factor, and their effects with respect to learning concrete vs abstract words
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Human concept cognition and semantic relations in the unified medical language system: A coherence analysis.
There is almost a universal agreement among scholars in information retrieval (IR) research that knowledge representation needs improvement. As core component of an IR system, improvement of the knowledge representation system has so far involved manipulation of this component based on principles such as vector space, probabilistic approach, inference network, and language modeling, yet the required improvement is still far from fruition. One promising approach that is highly touted to offer a potential solution exists in the cognitive paradigm, where knowledge representation practice should involve, or start from, modeling the human conceptual system. This study based on two related cognitive theories: the theory-based approach to concept representation and the psychological theory of semantic relations, ventured to explore the connection between the human conceptual model and the knowledge representation model (represented by samples of concepts and relations from the unified medical language system, UMLS). Guided by these cognitive theories and based on related and appropriate data-analytic tools, such as nonmetric multidimensional scaling, hierarchical clustering, and content analysis, this study aimed to conduct an exploratory investigation to answer four related questions. Divided into two groups, a total of 89 research participants took part in two sets of cognitive tasks. The first group (49 participants) sorted 60 food names into categories followed by simultaneous description of the derived categories to explain the rationale for category judgment. The second group (40 participants) performed sorting 47 semantic relations (the nonhierarchical associative types) into 5 categories known a priori. Three datasets resulted as a result of the cognitive tasks: food-sorting data, relation-sorting data, and free and unstructured text of category descriptions. Using the data analytic tools mentioned, data analysis was carried out and important results and findings were obtained that offer plausible explanations to the 4 research questions. Major results include the following: (a) through discriminant analysis category members were predicted consistently in 70% of the time; (b) the categorization bases are largely simplified rules, naïve explanations, and feature-based; (c) individuals theoretical explanation remains valid and stays stable across category members; (d) the human conceptual model can be fairly reconstructed in a low-dimensional space where 93% of the variance in the dimensional space is accounted for by the subjects performance; (e) participants consistently classify 29 of the 47 semantic relations; and, (f) individuals perform better in the functional and spatial dimensions of the semantic relations classification task and perform poorly in the conceptual dimension
Visualization and analytics of codicological data of Hebrew books
The goal is to provide a proper data model, using a common vocabulary, to
decrease the heterogenous nature of these datasets as well as its inherent uncertainty
caused by the descriptive nature of the field of Codicology. This research project was
developed with the goal of applying data visualization and data mining techniques to the
field of Codicology and Digital Humanities. Using Hebrew manuscript data as a starting
point, this dissertation proposes an environment for exploratory analysis to be used by
Humanities experts to deepen their understanding of codicological data, to formulate new,
or verify existing, research hypotheses, and to communicate their findings in a richer way.
To improve the scope of visualizations and knowledge discovery we will try to use data
mining methods such as Association Rule Mining and Formal Concept Analysis. The
present dissertation aims to retrieve information and structure from Hebrew manuscripts
collected by codicologists. These manuscripts reflect the production of books of a specific
region, namely "Sefarad" region, within the period between 10th and 16th.A presente dissertação tem como objetivo obter conhecimento estruturado de
manuscritos hebraicos coletados por codicologistas. Estes manuscritos refletem a
produção de livros de uma região específica, nomeadamente a região "Sefarad", no
período entre os séculos X e XVI. O objetivo é fornecer um modelo de dados apropriado,
usando um vocabulário comum, para diminuir a natureza heterogénea desses conjuntos
de dados, bem como sua incerteza inerente causada pela natureza descritiva no campo da
Codicologia. Este projeto de investigação foi desenvolvido com o objetivo de aplicar
técnicas de visualização de dados e "data mining" no campo da Codicologia e Humanidades
Digitais. Usando os dados de manuscritos hebraicos como ponto de partida, esta
dissertação propõe um ambiente para análise exploratória a ser utilizado por especialistas
em Humanidades Digitais e Codicologia para aprofundar a compreensão dos dados
codicológicos, formular novas hipóteses de pesquisa, ou verificar existentes, e comunicar
as suas descobertas de uma forma mais rica. Para melhorar as visualizações e descoberta
de conhecimento, tentaremos usar métodos de data mining, como a "Association Rule
Mining" e "Formal Concept Analysis"
A usage-based model for the acquisition of syntactic constructions and its application in spoken language understanding
Gaspers J. A usage-based model for the acquisition of syntactic constructions and its application in spoken language understanding. Bielefeld: Universitätsbibliothek Bielefeld; 2014
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