Statistical Parsing by Machine Learning from a Classical Arabic Treebank

Research into statistical parsing for English has enjoyed over a decade of successful results. However, adapting these models to other languages has met with difficulties. Previous comparative work has shown that Modern Arabic is one of the most difficult languages to parse due to rich morphology and free word order. Classical Arabic is the ancient form of Arabic, and is understudied in computational linguistics, relative to its worldwide reach as the language of the Quran. The thesis is based on seven publications that make significant contributions to knowledge relating to annotating and parsing Classical Arabic. Classical Arabic has been studied in depth by grammarians for over a thousand years using a traditional grammar known as i’rāb (إعغاة ). Using this grammar to develop a representation for parsing is challenging, as it describes syntax using a hybrid of phrase-structure and dependency relations. This work aims to advance the state-of-the-art for hybrid parsing by introducing a formal representation for annotation and a resource for machine learning. The main contributions are the first treebank for Classical Arabic and the first statistical dependency-based parser in any language for ellipsis, dropped pronouns and hybrid representations. A central argument of this thesis is that using a hybrid representation closely aligned to traditional grammar leads to improved parsing for Arabic. To test this hypothesis, two approaches are compared. As a reference, a pure dependency parser is adapted using graph transformations, resulting in an 87.47% F1-score. This is compared to an integrated parsing model with an F1-score of 89.03%, demonstrating that joint dependency-constituency parsing is better suited to Classical Arabic. The Quran was chosen for annotation as a large body of work exists providing detailed syntactic analysis. Volunteer crowdsourcing is used for annotation in combination with expert supervision. A practical result of the annotation effort is the corpus website: http://corpus.quran.com, an educational resource with over two million users per year

Issues in Esahie Nominal Morphology: From Inflection to Word-formation

The present study is a documentation-oriented research which aims at exploring the nominal morphology of Esahie, an otherwise unexplored cross-border Kwa language. Essentially, it examines pertinent inflectional and word formation issues in the nominal domain of Esahie such as noun class system, agreement, syncretism, nominalization, and compounding. The overall goal of this thesis is to investigate and provide a comprehensive account of the attested types, structure, formation, and the lexical semantics of nouns and nominalizations in Esahie. This thesis also seeks to understand what the facts about the structure and formation of nouns and nominalizations in Esahie reveal about the nature of the interface between morphology, phonology, syntax, and semantics, and about the architecture of the grammar in general. In interpreting the Esahie data, we ultimately hope to contribute to current theoretical debates by presenting empirical arguments in support of an abstractive, rather than a constructive view of morphology, by arguing that adopting the formalism of Construction Morphology (CxM, see Booij 2010a-d), as an abstractive model, comes with many advantages. We show that the formalism espoused in CxM is able to deal adequately with all the inflectional and word formation issues discussed in this thesis, including the irregular (non-canonical) patterns which are characterized either by cumulative exponence or extra-compositionality. With regards to compounding, this study confirms the view (cf. Appah 2013; 2015; Akrofi-Ansah 2012b; Lawer 2017) that, in Kwa, notwithstanding the word class of the input elements, the output of a compounding operation is always a nominal. This characterization points to a fascinating (mutual) interplay between the word-formation phenomena of compounding and nominalization, since the former operation invariably feeds into the latter. Overall, this thesis shows that nominalization is a prominent word-formation operation in Kwa grammar. Data used in this thesis emanates from several fieldtrips carried out in some Esahie speaking communities in the Western-North region of Ghana, as well as other secondary sources

Deep Architectures for Visual Recognition and Description

In recent times, digital media contents are inherently of multimedia type, consisting of the form text, audio, image and video. Several of the outstanding computer Vision (CV) problems are being successfully solved with the help of modern Machine Learning (ML) techniques. Plenty of research work has already been carried out in the field of Automatic Image Annotation (AIA), Image Captioning and Video Tagging. Video Captioning, i.e., automatic description generation from digital video, however, is a different and complex problem altogether. This study compares various existing video captioning approaches available today and attempts their classification and analysis based on different parameters, viz., type of captioning methods (generation/retrieval), type of learning models employed, the desired output description length generated, etc. This dissertation also attempts to critically analyze the existing benchmark datasets used in various video captioning models and the evaluation metrics for assessing the final quality of the resultant video descriptions generated. A detailed study of important existing models, highlighting their comparative advantages as well as disadvantages are also included. In this study a novel approach for video captioning on the Microsoft Video Description (MSVD) dataset and Microsoft Video-to-Text (MSR-VTT) dataset is proposed using supervised learning techniques to train a deep combinational framework, for achieving better quality video captioning via predicting semantic tags. We develop simple shallow CNN (2D and 3D) as feature extractors, Deep Neural Networks (DNNs and Bidirectional LSTMs (BiLSTMs) as tag prediction models and Recurrent Neural Networks (RNNs) (LSTM) model as the language model. The aim of the work was to provide an alternative narrative to generating captions from videos via semantic tag predictions and deploy simpler shallower deep model architectures with lower memory requirements as solution so that it is not very memory extensive and the developed models prove to be stable and viable options when the scale of the data is increased. This study also successfully employed deep architectures like the Convolutional Neural Network (CNN) for speeding up automation process of hand gesture recognition and classification of the sign languages of the Indian classical dance form, ‘Bharatnatyam’. This hand gesture classification is primarily aimed at 1) building a novel dataset of 2D single hand gestures belonging to 27 classes that were collected from (i) Google search engine (Google images), (ii) YouTube videos (dynamic and with background considered) and (iii) professional artists under staged environment constraints (plain backgrounds). 2) exploring the effectiveness of CNNs for identifying and classifying the single hand gestures by optimizing the hyperparameters, and 3) evaluating the impacts of transfer learning and double transfer learning, which is a novel concept explored for achieving higher classification accuracy

Automatic Detection of Dementia and related Affective Disorders through Processing of Speech and Language

In 2019, dementia is has become a trillion dollar disorder. Alzheimer’s disease (AD) is a type of dementia in which the main observable symptom is a decline in cognitive functions, notably memory, as well as language and problem-solving. Experts agree that early detection is crucial to effectively develop and apply interventions and treatments, underlining the need for effective and pervasive assessment and screening tools. The goal of this thesis is to explores how computational techniques can be used to process speech and language samples produced by patients suffering from dementia or related affective disorders, to the end of automatically detecting them in large populations us- ing machine learning models. A strong focus is laid on the detection of early stage dementia (MCI), as most clinical trials today focus on intervention at this level. To this end, novel automatic and semi-automatic analysis schemes for a speech-based cogni- tive task, i.e., verbal fluency, are explored and evaluated to be an appropriate screening task. Due to a lack of available patient data in most languages, world-first multilingual approaches to detecting dementia are introduced in this thesis. Results are encouraging and clear benefits on a small French dataset become visible. Lastly, the task of detecting these people with dementia who also suffer from an affective disorder called apathy is explored. Since they are more likely to convert into later stage of dementia faster, it is crucial to identify them. These are the fist experiments that consider this task us- ing solely speech and language as inputs. Results are again encouraging, both using only speech or language data elicited using emotional questions. Overall, strong results encourage further research in establishing speech-based biomarkers for early detection and monitoring of these disorders to better patients’ lives.Im Jahr 2019 ist Demenz zu einer Billionen-Dollar-Krankheit geworden. Die Alzheimer- Krankheit (AD) ist eine Form der Demenz, bei der das Hauptsymptom eine Abnahme der kognitiven Funktionen ist, insbesondere des Gedächtnisses sowie der Sprache und des Problemlösungsvermögens. Experten sind sich einig, dass eine frühzeitige Erkennung entscheidend für die effektive Entwicklung und Anwendung von Interventionen und Behandlungen ist, was den Bedarf an effektiven und durchgängigen Bewertungsund Screening-Tools unterstreicht. Das Ziel dieser Arbeit ist es zu erforschen, wie computergest ützte Techniken eingesetzt werden können, um Sprach- und Sprechproben von Patienten, die an Demenz oder verwandten affektiven Störungen leiden, zu verarbeiten, mit dem Ziel, diese in großen Populationen mit Hilfe von maschinellen Lernmodellen automatisch zu erkennen. Ein starker Fokus liegt auf der Erkennung von Demenz im Frühstadium (MCI), da sich die meisten klinischen Studien heute auf eine Intervention auf dieser Ebene konzentrieren. Zu diesem Zweck werden neuartige automatische und halbautomatische Analyseschemata für eine sprachbasierte kognitive Aufgabe, d.h. die verbale Geläufigkeit, erforscht und als geeignete Screening-Aufgabe bewertet. Aufgrund des Mangels an verfügbaren Patientendaten in den meisten Sprachen werden in dieser Arbeit weltweit erstmalig mehrsprachige Ansätze zur Erkennung von Demenz vorgestellt. Die Ergebnisse sind ermutigend und es werden deutliche Vorteile an einem kleinen französischen Datensatz sichtbar. Schließlich wird die Aufgabe untersucht, jene Menschen mit Demenz zu erkennen, die auch an einer affektiven Störung namens Apathie leiden. Da sie mit größerer Wahrscheinlichkeit schneller in ein späteres Stadium der Demenz übergehen, ist es entscheidend, sie zu identifizieren. Dies sind die ersten Experimente, die diese Aufgabe unter ausschließlicher Verwendung von Sprache und Sprache als Input betrachten. Die Ergebnisse sind wieder ermutigend, sowohl bei der Verwendung von reiner Sprache als auch bei der Verwendung von Sprachdaten, die durch emotionale Fragen ausgelöst werden. Insgesamt sind die Ergebnisse sehr ermutigend und ermutigen zu weiterer Forschung, um sprachbasierte Biomarker für die Früherkennung und Überwachung dieser Erkrankungen zu etablieren und so das Leben der Patienten zu verbessern

Shell nouns : in a systemic functional linguistics perspective

Tese de doutoramento, Linguística (Análise do Discurso), Universidade de Lisboa, Faculdade de Letras, 2015Shell nouns in a Systemic Functional Linguistics perspective. The aim of this thesis is to develop an account of shell nouns (Schmid, 2000) in a Systemic Functional Linguistics (SFL) perspective. Using a parallel corpus comprising five article submissions by Portuguese academics in the field of economics and five published articles on comparable topics, the ideational, interpersonal and textual functions of shell nouns are tagged at the strata of the lexicogrammar and discourse semantics using Corpus Tool version 2.7.4 (O’Donnell, 2008). The systems networks used to tag the corpus are grounded in SFL theory. The analysis shows that shell nouns constitute an important systemic resource for the writers of research articles, who need to build an argument, positioning themselves and their study to convince the discourse community that their paper makes a contribution to knowledge in their disciplinary field. They enable a text to unfold by compacting information realised as a clause or more elsewhere in the text. Thus they can help scaffold a text through hyper-Themes, hyper-News and internal conjunction. At the stratum of the lexicogrammar, anaphorically referring nominal groups with a shell noun as Head often compose Theme, where they constitute a shared point of departure for the clause. In a decoding relational clause whose Process is realised by a verb such as reveal, confirm, or suggest, an anaphorically referring shell noun that construes Token helps to explicitly build the writer’s argument. Shell nouns that construe the field of research, such as results and findings are common in this function. Mental, linguistic and factual shell nouns contribute to construing dialogic position, and coupling between interpersonal systems and textual systems enables the writer to align the reader with certain positions and disalign with others. Although most shell nouns are not field specific, because they can project a figure that instantiates an entity, they contribute to construing field, for example instantiating entities as the object of study of the empirical research. The capacity of shell nouns to function as described above derives from their status as semiotic abstractions, which can refer to text as fact or report and are grammatical metaphors. They can be seen as lying at the intersection of modality and the logico-semantic relations of projection and expansion, brought into being by the semogenic process of nominalisation. The writers of the published articles and article submissions are found to use shell nouns in all of the functions above, but there are differences in the relative shares of the functions, which may affect reader reactions to the text