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Tohoku University徳山豪課

Text Analytics to Predict Time and Cause of Death from Verbal Autopsies

This thesis describes the first Text Analytics approach to predicting Causes of Death (CoD) from Verbal Autopsies (VA). VA is an alternative technique recommended by the World Health Organisation for ascertaining CoD in low and middle-income countries (LMIC). CoD information is vitally important in the provision of healthcare. CoD information from VA can be obtained via two main approaches: manual, also referred to as the physician-review and automatic. The automatic-based approach is an active research area due to its efficiency and cost effectiveness over the manual approach. VA contains both closed responses and open narrative text. However, the open narrative text has been ignored by the state-of-art automatic approaches and this remains a challenge and an important research issue. We hypothesise that it is feasible to predict CoD from the narratives of VA. We further contend that an automatic approach that could utilise the information contained in both narrative and closed response text of VA could lead to an improved prediction accuracy of CoD. This research has been formulated as a Text Classification problem, which employs Corpus and Computational Linguistics, Natural Language Processing and Machine Learning techniques to automatically classify VA documents according to CoD. Firstly, the research uses a VA corpus built from a sample collection of over 11,400 VA documents collected during a 10 year period in Ghana, West Africa. About 80 per cent of these documents have been annotated with CoD by medical experts. Secondly, we design experiments to identify Machine Learning techniques (algorithm, feature representation scheme, and feature reduction strategy) suitable for classifying VA open narratives (VAModel1). Thirdly, we propose novel methods of extracting features to build a model that predicts CoD from VA narratives using the annotated VA corpus as training and testing set. Furthermore, we develop two additional models: only closed responses based (VAModel2); and a hybrid of closed and open narrative based model (VAModel3). Our VAModel1 performs reasonably better than our baseline model, suggesting the feasibility of predicting the CoD from the VA open narratives. Overall, VAModel3 performance was observed to achieve better performance than VAModel1 but not significantly better than VAModel2. Also, in terms of reliability, VAModel1 obtained a moderate agreement (kappa score = 0.4) when compared with the gold standard– medical experts (average annotation agreement between medical experts, kappa score= 0.64). Furthermore, an acceptable agreement was obtained for VAModel2 (kappa score =0.71) and VAModel3 (kappa score =0.75), suggesting the reliability of these two models is better than medical experts. Also, a detailed analysis suggested that combining information from narratives and closed responses leads to an increase in performance for some CoD categories whereas information obtained from the closed responses part is enough for other CoD categories. Our research provides an alternative automatic approach to predicting CoD from VA, which is essential for LMIC. Therefore, further research into various aspects of the modelling process could improve the current performance of automatically predicting CoD from VAs

PARTITION OF UNITY BOUNDARY ELEMENT AND FINITE ELEMENT METHOD: OVERCOMING NONUNIQUENESS AND COUPLING FOR ACOUSTIC SCATTERING IN HETEROGENEOUS MEDIA

The understanding of complex wave phenomenon, such as multiple scattering in heterogeneous media, is often hindered by lack of equations modelling the exact physics. Use of approximate numerical methods, such as Finite Element Method (FEM) and Boundary Element Method (BEM), is therefore needed to understand these complex wave problems. FEM is known for its ability to accurately model the physics of the problem but requires truncating the computational domain. On the other hand, BEM can accurately model waves in unbounded region but is suitable for homogeneous media only. Coupling FEM and BEM therefore is a natural way to solve problems involving a relatively small heterogeneity (to be modelled with FEM) surrounded by an unbounded homogeneous medium (to be modelled with BEM). The use of a classical FEM-BEM coupling can become computationally demanding due to high mesh density requirement at high frequencies. Secondly, BEM is an integral equation based technique and suffers from the problem of non-uniqueness. To overcome the requirement of high mesh density for high frequencies, a technique known as the ‘Partition of Unity’ (PU) method has been developed by previous researchers. The work presented in this thesis extends the concept of PU to BEM (PUBEM) while effectively treating the problem of non-uniqueness. Two of the well-known methods, namely CHIEF and Burton-Miller approaches, to overcome the non-uniqueness problem, are compared for PUBEM. It is shown that the CHIEF method is relatively easy to implement and results in at least one order of magnitude of improvement in the accuracy. A modified ‘PU’ concept is presented to solve the heterogeneous problems with the PU based FEM (PUFEM). It is shown that use of PUFEM results in close to two orders of magnitude improvement over FEM despite using a much coarser mesh. The two methods, namely PUBEM and PUFEM, are then coupled to solve the heterogeneous wave problems in unbounded media. Compared to PUFEM, the coupled PUFEM-PUBEM apporach is shown to result between 30-40% savings in the total degress of freedom required to achieve similar accuracy

Un environnement générique et ouvert pour le traitement des expressions polylexicales

The treatment of multiword expressions (MWEs), like take off, bus stop and big deal, is a challenge for NLP applications. This kind of linguistic construction is not only arbitrary but also much more frequent than one would initially guess. This thesis investigates the behaviour of MWEs across different languages, domains and construction types, proposing and evaluating an integrated methodological framework for their acquisition. There have been many theoretical proposals to define, characterise and classify MWEs. We adopt generic definition stating that MWEs are word combinations which must be treated as a unit at some level of linguistic processing. They present a variable degree of institutionalisation, arbitrariness, heterogeneity and limited syntactic and semantic variability. There has been much research on automatic MWE acquisition in the recent decades, and the state of the art covers a large number of techniques and languages. Other tasks involving MWEs, namely disambiguation, interpretation, representation and applications, have received less emphasis in the field. The first main contribution of this thesis is the proposal of an original methodological framework for automatic MWE acquisition from monolingual corpora. This framework is generic, language independent, integrated and contains a freely available implementation, the mwetoolkit. It is composed of independent modules which may themselves use multiple techniques to solve a specific sub-task in MWE acquisition. The evaluation of MWE acquisition is modelled using four independent axes. We underline that the evaluation results depend on parameters of the acquisition context, e.g., nature and size of corpora, language and type of MWE, analysis depth, and existing resources. The second main contribution of this thesis is the application-oriented evaluation of our methodology proposal in two applications: computer-assisted lexicography and statistical machine translation. For the former, we evaluate the usefulness of automatic MWE acquisition with the mwetoolkit for creating three lexicons: Greek nominal expressions, Portuguese complex predicates and Portuguese sentiment expressions. For the latter, we test several integration strategies in order to improve the treatment given to English phrasal verbs when translated by a standard statistical MT system into Portuguese. Both applications can benefit from automatic MWE acquisition, as the expressions acquired automatically from corpora can both speed up and improve the quality of the results. The promising results of previous and ongoing experiments encourage further investigation about the optimal way to integrate MWE treatment into other applications. Thus, we conclude the thesis with an overview of the past, ongoing and future work

Designing Statistical Language Learners: Experiments on Noun Compounds

The goal of this thesis is to advance the exploration of the statistical language learning design space. In pursuit of that goal, the thesis makes two main theoretical contributions: (i) it identifies a new class of designs by specifying an architecture for natural language analysis in which probabilities are given to semantic forms rather than to more superficial linguistic elements; and (ii) it explores the development of a mathematical theory to predict the expected accuracy of statistical language learning systems in terms of the volume of data used to train them. The theoretical work is illustrated by applying statistical language learning designs to the analysis of noun compounds. Both syntactic and semantic analysis of noun compounds are attempted using the proposed architecture. Empirical comparisons demonstrate that the proposed syntactic model is significantly better than those previously suggested, approaching the performance of human judges on the same task, and that the proposed semantic model, the first statistical approach to this problem, exhibits significantly better accuracy than the baseline strategy. These results suggest that the new class of designs identified is a promising one. The experiments also serve to highlight the need for a widely applicable theory of data requirements.Comment: PhD thesis (Macquarie University, Sydney; December 1995), LaTeX source, xii+214 page