127 research outputs found
Deep Learning for Period Classification of Historical Texts
In this study, we address the interesting task of classifying historical texts by their assumed period of writing. This task is useful in digital humanity studies where many texts have unidentified publication dates. For years, the typical approach for temporal text classification was supervised using machine-learning algorithms. These algorithms require careful feature engineering and considerable domain expertise to design a feature extractor to transform the raw text into a feature vector from which the classifier could learn to classify any unseen valid input. Recently, deep learning has produced extremely promising results for various tasks in natural language processing (NLP). The primary advantage of deep learning is that human engineers did not design the feature layers, but the features were extrapolated from data with a general-purpose learning procedure. We investigated deep learning models for period classification of historical texts. We compared three common models: paragraph vectors, convolutional neural networks (CNN), and recurrent neural networks (RNN). We demonstrate that the CNN and RNN models outperformed the paragraph vector model and supervised machine-learning algorithms. In addition, we constructed word embeddings for each time period and analyzed semantic changes of word meanings over time
In Search of a Common Thread: Enhancing the LBD Workflow with a view to its Widespread Applicability
Literature-Based Discovery (LBD) research focuses on discovering implicit knowledge
linkages in existing scientific literature to provide impetus to innovation and research
productivity. Despite significant advancements in LBD research, previous studies contain
several open problems and shortcomings that are hindering its progress. The overarching
goal of this thesis is to address these issues, not only to enhance the discovery
component of LBD, but also to shed light on new directions that can further strengthen
the existing understanding of the LBD work
ow. In accordance with this goal, the thesis
aims to enhance the LBD work
ow with a view to ensuring its widespread applicability.
The goal of widespread applicability is twofold. Firstly, it relates to the adaptability of
the proposed solutions to a diverse range of problem settings. These problem settings
are not necessarily application areas that are closely related to the LBD context, but
could include a wide range of problems beyond the typical scope of LBD, which has traditionally
been applied to scientific literature. Adapting the LBD work
ow to problems
outside the typical scope of LBD is a worthwhile goal, since the intrinsic objective of
LBD research, which is discovering novel linkages in text corpora is valid across a vast
range of problem settings.
Secondly, the idea of widespread applicability also denotes the capability of the proposed
solutions to be executed in new environments. These `new environments' are various
academic disciplines (i.e., cross-domain knowledge discovery) and publication languages
(i.e., cross-lingual knowledge discovery). The application of LBD models to new environments
is timely, since the massive growth of the scientific literature has engendered
huge challenges to academics, irrespective of their domain.
This thesis is divided into five main research objectives that address the following topics:
literature synthesis, the input component, the discovery component, reusability, and
portability. The objective of the literature synthesis is to address the gaps in existing
LBD reviews by conducting the rst systematic literature review. The input component
section aims to provide generalised insights on the suitability of various input types in the
LBD work
ow, focusing on their role and potential impact on the information retrieval
cycle of LBD.
The discovery component section aims to intermingle two research directions that have
been under-investigated in the LBD literature, `modern word embedding techniques'
and `temporal dimension' by proposing diachronic semantic inferences. Their potential
positive in
uence in knowledge discovery is veri ed through both direct and indirect
uses. The reusability section aims to present a new, distinct viewpoint on these LBD
models by verifying their reusability in a timely application area using a methodical reuse
plan. The last section, portability, proposes an interdisciplinary LBD framework that
can be applied to new environments. While highly cost-e cient and easily pluggable, this framework also gives rise to a new perspective on knowledge discovery through its
generalisable capabilities.
Succinctly, this thesis presents novel and distinct viewpoints to accomplish five main
research objectives, enhancing the existing understanding of the LBD work
ow. The
thesis offers new insights which future LBD research could further explore and expand
to create more eficient, widely applicable LBD models to enable broader community
benefits.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 202
A language-based approach to categorical analysis
Thesis (S.M.)--Massachusetts Institute of Technology, Program in Media Arts & Sciences, 2001.Includes bibliographical references (p. 79-81).With the digitization of media, computers can be employed to help us with the process of classification, both by learning from our behavior to perform the task for us and by exposing new ways for us to think about our information. Given that most of our media comes in the form of electronic text, research in this area focuses on building automatic text classification systems. The standard representation employed by these systems, known as the bag-of-words approach to information retrieval, represents documents as collections of words. As a byproduct of this model, automatic classifiers have difficulty distinguishing between different meanings of a single word. This research presents a new computational model of electronic text, called a synchronic imprint, which uses structural information to contextualize the meaning of words. Every concept in the body of a text is described by its relationships with other concepts in the same text, allowing classification systems to distinguish between alternative meanings of the same word. This representation is applied to both the standard problem of text classification and also to the task of enabling people to better identify large bodies of text. The latter is achieved through the development of a visualization tool named flux that models synchronic imprints as a spring network.by Cameron Alexander Marlow.S.M
Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation
Peer reviewe
Mixed-Language Arabic- English Information Retrieval
Includes abstract.Includes bibliographical references.This thesis attempts to address the problem of mixed querying in CLIR. It proposes mixed-language (language-aware) approaches in which mixed queries are used to retrieve most relevant documents, regardless of their languages. To achieve this goal, however, it is essential firstly to suppress the impact of most problems that are caused by the mixed-language feature in both queries and documents and which result in biasing the final ranked list. Therefore, a cross-lingual re-weighting model was developed. In this cross-lingual model, term frequency, document frequency and document length components in mixed queries are estimated and adjusted, regardless of languages, while at the same time the model considers the unique mixed-language features in queries and documents, such as co-occurring terms in two different languages. Furthermore, in mixed queries, non-technical terms (mostly those in non-English language) would likely overweight and skew the impact of those technical terms (mostly those in English) due to high document frequencies (and thus low weights) of the latter terms in their corresponding collection (mostly the English collection). Such phenomenon is caused by the dominance of the English language in scientific domains. Accordingly, this thesis also proposes reasonable re-weighted Inverse Document Frequency (IDF) so as to moderate the effect of overweighted terms in mixed queries
New perspectives on cohesion and coherence: Implications for translation
The contributions to this volume investigate relations of cohesion and coherence as well as instantiations of discourse phenomena and their interaction with information structure in multilingual contexts. Some contributions concentrate on procedures to analyze cohesion and coherence from a corpus-linguistic perspective. Others have a particular focus on textual cohesion in parallel corpora that include both originals and translated texts. Additionally, the papers in the volume discuss the nature of cohesion and coherence with implications for human and machine translation.The contributors are experts on discourse phenomena and textuality who address these issues from an empirical perspective. The chapters in this volume are grounded in the latest research making this book useful to both experts of discourse studies and computational linguistics, as well as advanced students with an interest in these disciplines. We hope that this volume will serve as a catalyst to other researchers and will facilitate further advances in the development of cost-effective annotation procedures, the application of statistical techniques for the analysis of linguistic phenomena and the elaboration of new methods for data interpretation in multilingual corpus linguistics and machine translation
New perspectives on cohesion and coherence: Implications for translation
The contributions to this volume investigate relations of cohesion and coherence as well as instantiations of discourse phenomena and their interaction with information structure in multilingual contexts. Some contributions concentrate on procedures to analyze cohesion and coherence from a corpus-linguistic perspective. Others have a particular focus on textual cohesion in parallel corpora that include both originals and translated texts. Additionally, the papers in the volume discuss the nature of cohesion and coherence with implications for human and machine translation.The contributors are experts on discourse phenomena and textuality who address these issues from an empirical perspective. The chapters in this volume are grounded in the latest research making this book useful to both experts of discourse studies and computational linguistics, as well as advanced students with an interest in these disciplines. We hope that this volume will serve as a catalyst to other researchers and will facilitate further advances in the development of cost-effective annotation procedures, the application of statistical techniques for the analysis of linguistic phenomena and the elaboration of new methods for data interpretation in multilingual corpus linguistics and machine translation
New perspectives on cohesion and coherence: Implications for translation
The contributions to this volume investigate relations of cohesion and coherence as well as instantiations of discourse phenomena and their interaction with information structure in multilingual contexts. Some contributions concentrate on procedures to analyze cohesion and coherence from a corpus-linguistic perspective. Others have a particular focus on textual cohesion in parallel corpora that include both originals and translated texts. Additionally, the papers in the volume discuss the nature of cohesion and coherence with implications for human and machine translation.The contributors are experts on discourse phenomena and textuality who address these issues from an empirical perspective. The chapters in this volume are grounded in the latest research making this book useful to both experts of discourse studies and computational linguistics, as well as advanced students with an interest in these disciplines. We hope that this volume will serve as a catalyst to other researchers and will facilitate further advances in the development of cost-effective annotation procedures, the application of statistical techniques for the analysis of linguistic phenomena and the elaboration of new methods for data interpretation in multilingual corpus linguistics and machine translation
New perspectives on cohesion and coherence: Implications for translation
The contributions to this volume investigate relations of cohesion and coherence as well as instantiations of discourse phenomena and their interaction with information structure in multilingual contexts. Some contributions concentrate on procedures to analyze cohesion and coherence from a corpus-linguistic perspective. Others have a particular focus on textual cohesion in parallel corpora that include both originals and translated texts. Additionally, the papers in the volume discuss the nature of cohesion and coherence with implications for human and machine translation.The contributors are experts on discourse phenomena and textuality who address these issues from an empirical perspective. The chapters in this volume are grounded in the latest research making this book useful to both experts of discourse studies and computational linguistics, as well as advanced students with an interest in these disciplines. We hope that this volume will serve as a catalyst to other researchers and will facilitate further advances in the development of cost-effective annotation procedures, the application of statistical techniques for the analysis of linguistic phenomena and the elaboration of new methods for data interpretation in multilingual corpus linguistics and machine translation
- …