Computing and Information Systems - Theses
Permanent URI for this collection
Search Results
1 - 10 of 15
-
ItemCompositional morphology through deep learning( 2018)Most human languages have sophisticated morphological systems. In order to build successful models of language processing, we need to focus on morphology, the internal structure of words. In this thesis, we study two morphological processes: inflection (word change rules, e.g. run -- runs) and derivation (word formation rules, e.g. run -- runner). We first evaluate the ability of contemporary models that are trained using the distributional hypothesis, which states that a word's meaning can be expressed by the context in which it appears, to capture these types of morphology. Our study reveals that inflections are predicted at high accuracy whereas derivations are more challenging due to irregularity of meaning change. We then demonstrate that supplying the model with character-level information improves predictions and makes usage of language resources more efficient, especially in morphologically rich languages. We then address the question of to what extent and which information about word properties (such as gender, case, number) can be predicted entirely from a word's sentential content. To this end, we introduce a novel task of contextual inflection prediction. Our experiments on prediction of morphological features and a corresponding word form from sentential context show that the task is challenging, and as morphological complexity increases, performance significantly drops. We found that some morphological categories (e.g., verbal tense) are inherent and typically cannot be predicted from context while others (e.g., adjective number and gender) are contextual and inferred from agreement. Compared to morphological inflection tasks, where morphological features are explicitly provided, and the system has to predict only the form, accuracy on this task is much lower. Finally, we turn to word formation, derivation. Experiments with derivations show that they are less regular and systematic. We study how much a sentential context is indicative of a meaning change type. Our results suggest that even though inflections are more productive and regular than derivations, the latter also present cases of high regularity of meaning and form change, but often require extra information such as etymology, word frequency, and more fine-grained annotation in order to be predicted at high accuracy.
-
ItemAnalysing the interplay of location, language and links utilising geotagged Twitter content( 2018)Language use and interactions on social media are geographically biased. In this work we utilise this bias in predictive models of user geolocation and lexical dialectology. User geolocation is an important component of applications such as personalised search and recommendation systems. We propose text-based and network-based geolocation models, and compare them over benchmark datasets yielding state-of-the- art performance. We also propose hybrid and joint text and network geolocation models that improve upon text or network only models and show that the joint models are able to achieve reasonable performance in minimal supervision scenarios, as often happens in real world datasets. Finally, we also propose the use of continuous representations of location, which enables regression modelling of geolocation and lexical dialectology. We show that our proposed data-driven lexical dialectology model provides qualitative insights in studying geographical lexical variation.
-
ItemCrowdsourcing lexical semantic judgements from bilingual dictionary users( 2017)Words can take on many meanings, and collecting and identifying example usages representative of the full variety of meanings words can take is a bottleneck to the study of lexical semantics using statistical approaches. To perform supervised word sense disambiguation (WSD), or to evaluate knowledge-based methods, a corpus of texts annotated with senses from a dictionary may be constructed by paid experts. However, the cost usually prohibits more than a small sample of words and senses being represented in the corpus. Crowdsourcing methods promise to acquire data more cheaply, albeit with a greater challenge for quality control. Most crowdsourcing to date has incentivised participation in the form of a payment or by gamification of the resource construction task. However, with paid crowdsourcing the cost of human labour scales linearly with the output size, and while game playing volunteers may be free, gamification studies must compete with a multi-billion dollar games industry for players. In this thesis we develop and evaluate resources for computational semantics, working towards a crowdsourcing method that extracts information from naturally occurring human activities. A number of software products exist for glossing Japanese text with entries from a dictionary for English speaking students. However, the most popular ones have a tendency to either present an overwhelming amount of information containing every sense of every word or else hide too much information and risk removing senses with particular relevance to a specific text. By offering a glossing application with interactive features for exploring word senses, we create an opportunity to crowdsource human judgements about word senses and record human interaction with semantic NLP.
-
ItemSupervised algorithms for complex relation extraction( 2017)Binary relation extraction is an essential component of information extraction systems, wherein the aim is to extract meaningful relations that might exist between a pair of entities within a sentence. Binary relation extraction systems have witnessed a significant improvement over past three decades, ranging from rule-based systems to statistical natural language techniques including supervised, semi-supervised and unsupervised machine learning approaches. Modern question answering and summarization systems have motivated the need for extracting complex relations wherein the number of related entities is more than two. Complex relation extraction (CRE) systems are highly domain specific and often rely on traditional binary relation extraction techniques employed in a pipeline fashion, thus susceptible to processing-induced error propagation. In this thesis, we investigate and develop approaches to extract complex relations directly from natural language text. In particular, we deviate from the traditional disintegration of complex relations into constituent binary relations and propose usage of shortest dependency parse spanning the n related entities as an alternative to facilitate direct CRE. We investigate this proposed approach by a comprehensive study of supervised learning algorithms with a special focus on training support vector machines, convolutional neural networks and deep learning ensemble algorithms. Research in the domain of CRE is stymied by paucity of annotated data. To facilitate future exploration, we create two new datasets to evaluate our proposed CRE approaches on a pilot biographical fact extraction task. An evaluation of results on new and standard datasets concludes that usage of shortest path dependency parse in a supervised setting enables direct CRE with an improved accuracy, beating current state-of-the-art CRE systems. We further show the application of CRE to achieve state-of-the-art performance for directly extracting events without the need of disintegrating them into event trigger and event argument extraction processes.
-
ItemCoreference resolution for biomedical pathway data( 2017)The study of biological pathways is a major activity in the life sciences. Biological pathways provide understanding and interpretation of many different kinds of biological mechanisms such as metabolism, sending of signals between cells, regulation of gene expression, and production of cells. If there are defects in a pathway, the result may be a disease. Thus, biological pathways are used to support diagnosis of disease, more effective drug prescription, or personalised treatments. Even though there are many pathway resources providing useful information discovered with manual efforts, a great deal of relevant information concerning in such pathways is scattered through the vast biomedical literature. With the growth in the volume of the biomedical literature, many natural language processing methods for automatic information extraction have been studied, but there still exist a variety of challenges such as complex or hidden representations due to the use of coreference expressions in texts. Linguistic expressions such as it, they, or the gene are frequently used by authors to avoid repeating the names of entities or repeating complex descriptions that have previously been introduced in the same text. This thesis addresses three research goals: (1) examining whether an existing coreference resolution approach in the general domain can be adapted to the biomedical domain; (2) investigation of a heuristic strategy for coreference resolution in the biomedical literature; and (3) examining how coreference resolution can improve biological pathway data from the perspectives of information extraction, and of evaluation of existing pathway resources. In this thesis, we propose a new categorical framework that provides detailed analysis of performance of coreference resolution systems, based on analysis of syntactic and semantic characteristics of coreference relations in the biomedical domain. The framework not only can identify weaknesses of existing approaches, but also can provide insights into strategies for further improvement. We propose an approach to biomedical domain-specific coreference resolution that combines a set of syntactically and semantically motivated rules in terms of coreference type. Finally, we demonstrate that coreference resolution is a valuable process for pathway information discovery, through case studies. Our results show that an approach incorporating a coreference resolution process significantly improves information extraction performance.
-
ItemUnsupervised all-words sense distribution learning( 2016)There has recently been significant interest in unsupervised methods for learning word sense distributions, or most frequent sense information, in particular for applications where sense distinctions are needed. In addition to their direct application to word sense disambiguation (WSD), particularly where domain adaptation is required, these methods have successfully been applied to diverse problems such as novel sense detection or lexical simplification. Furthermore, they could be used to supplement or replace existing sources of sense frequencies, such as SemCor, which have many significant flaws. However, a major gap in the past work on sense distribution learning is that it has never been optimised for large-scale application to the entire vocabularies of a languages, as would be required to replace sense frequency resources such as SemCor. In this thesis, we develop an unsupervised method for all-words sense distribution learning, which is suitable for language-wide application. We first optimise and extend HDP-WSI, an existing state-of-the-art sense distribution learning method based on HDP topic modelling. This is mostly achieved by replacing HDP with the more efficient HCA topic modelling algorithm in order to create HCA-WSI, which is over an order of magnitude faster than HDP-WSI and more robust. We then apply HCA-WSI across the vocabularies of several languages to create LexSemTm, which is a multilingual sense frequency resource of unprecedented size. Of note, LexSemTm contains sense frequencies for approximately 88% of polysemous lemmas in Princeton WordNet, compared to only 39% for SemCor, and the quality of data in each is shown to be roughly equivalent. Finally, we extend our sense distribution learning methodology to multiword expressions (MWEs), which to the best of our knowledge is a novel task (as is applying any kind of general-purpose WSD methods to MWEs). We demonstrate that sense distribution learning for MWEs is comparable to that for simplex lemmas in all important respects, and we expand LexSemTm with MWE sense frequency data.
-
ItemImproving the utility of social media with Natural Language Processing( 2014)Social media has been an attractive target for many natural language processing (NLP) tasks and applications in recent years. However, the unprecedented volume of data and the non-standard language register cause problems for off-the-shelf NLP tools. This thesis investigates the broad question of how NLP-based text processing can improve the utility (i.e., the effectiveness and efficiency) of social media data. In particular, text normalisation and geolocation prediction are closely examined in the context of Twitter text processing. Text normalisation is the task of restoring non-standard words to their standard forms. For instance, earthquick and 2morrw should be transformed into “earthquake” and “tomorrow”, respectively. Non-standard words often cause problems for existing tools trained on edited text sources such as newswire text. By applying text normalisation to reduce unknown non-standard words, the accuracy of NLP tools and downstream applications is expected to increase. In this thesis, I explore and develop lexical normalisation methods for Twitter text. I shift the focus of text normalisation from a cascaded token-based approach to a type-based approach using a combined lexicon, based on the analysis of existing and developed text normalisation methods. The type-based method achieved the state-of-the-art end-to-end normalisation accuracy at the time of publication, i.e., 0.847 precision and 0.630 recall on a benchmark dataset. Furthermore, it is simple, lightweight and easily integrable which is particularly well suited to large-scale data processing. Additionally, the effectiveness of the proposed normalisation method is shown in non-English text normalisation and other NLP tasks and applications. Geolocation prediction estimates a user’s primary location based on the text of their posts. It enables location-based data partitioning, which is crucial to a range of tasks and applications such as local event detection. The partitioned location data can improve both the efficiency and the effectiveness of NLP tools and applications. In this thesis, I identify and explore several factors that affect the accuracy of text-based geolocation prediction in a unified framework. In particular, an extensive range of feature selection methods is compared to determine the optimised feature set for the geolocation prediction model. The results suggest feature selection is an effective method for improving the prediction accuracy regardless of geolocation model and location partitioning. Additionally, I examine the influence of other factors including non-geotagged data, user metadata, tweeting language, temporal influence, user geolocatability, and geolocation prediction confidence. The proposed stacking-based prediction model achieved 40.6% city-level accuracy and 40km median error distance for English Twitter users on a recent benchmark dataset. These investigations provide practical insights into the design of a text-based normalisation system, as well as the basis for further research on this task. Overall, the exploration of these two text processing tasks enhances the utility of social media data for relevant NLP tasks and downstream applications. The developed method and experimental results have immediate impact on future social media research.
-
ItemImproving the efficiency and capabilities of document structuring( 2007)Natural language generation (NLG), the problem of creating human-readable documents by computer, is one of the major fields of research in computational linguistics The task of creating a document is extremely common in many fields of activity. Accordingly, there are many potential applications for NLG - almost any document creation task could potentially be automated by an NLG system. Advanced forms of NLG could also be used to generate a document in multiple languages, or as an output interface for other programs, which might ordinarily produce a less-manageable collection of data. They may also be able to create documents tailored to the needs of individual users. This thesis deals with document structure, a recent theory which describes those aspects of a document’s layout which affect its meaning. As well as its theoretical interest, it is a useful intermediate representation in the process of NLG. There is a well-defined process for generating a document structure using constraint programming. We show how this process can be made considerably more efficient. This in turn allows us to extend the document structuring task to allow for summarisation and finer control of the document layout. This thesis is organised as follows. Firstly, we review the necessary background material in both natural language processing and constraint programming.
-
ItemScaling conditional random fields for natural language processing( 2007-01)This thesis deals with the use of Conditional Random Fields (CRFs; Lafferty et al. (2001)) for Natural Language Processing (NLP). CRFs are probabilistic models for sequence labelling which are particularly well suited to NLP. They have many compelling advantages over other popular models such as Hidden Markov Models and Maximum Entropy Markov Models (Rabiner, 1990; McCallum et al., 2001), and have been applied to a number of NLP tasks with considerable success (e.g., Sha and Pereira (2003) and Smith et al. (2005)). Despite their apparent success, CRFs suffer from two main failings. Firstly, they often over-fit the training sample. This is a consequence of their considerable expressive power, and can be limited by a prior over the model parameters (Sha and Pereira, 2003; Peng and McCallum, 2004). Their second failing is that the standard methods for CRF training are often very slow, sometimes requiring weeks of processing time. This efficiency problem is largely ignored in current literature, although in practise the cost of training prevents the application of CRFs to many new more complex tasks, and also prevents the use of densely connected graphs, which would allow for much richer feature sets. (For complete abstract open document)
-
ItemAutomatic identification of locative expressions from informal text( 2013)Informal place descriptions that are rich in locative expressions can be found in various contexts. The ability to extract locative expressions from such informal place descriptions is at the centre of improving the quality of services, such as interpreting geographical queries and emergency calls. While much attention has been focused on the identification of formal place references (e.g., Rathmines Road) from natu- ral language, people tend to make heavy use of informal place references (e.g., my bedroom). This research addresses the problem by developing a model that is able to automatically identify locative expressions from informal text. Moreover, we study and discover insights of what aspects are helpful in the identification task. Utilising an existing manually annotated corpus, we re-annotate locative expressions and use them as the gold standard. Having the gold standard ready, we take a machine learning approach to the identification task with well-reasoned features based on observation and intuition. Further, we study the impacts of various feature setups on the performance of the model and provide analyses of experiment results. With the best performing feature setup, the model is able to achieve significant increase in performance over the baseline systems.