A Review on Text Detection Techniques

Text detection in image is an important field. Reading text is challenging because of the variations in images. Text detection is useful for many navigational purposes e.g. text on google API’s and traffic panels etc. This paper analyzes the work done on text detection by many researchers and critically evaluates the techniques designed for text detection and states the limitation of each approach. We have integrated the work of many researchers for getting a brief over view of multiple available techniques and their strengths and limitations are also discussed to give readers a clear picture. The major dataset discussed in all these papers are ICDAR 2003, 2005, 2011, 2013 and SVT(street view text).

EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020

Welcome to EVALITA 2020! EVALITA is the evaluation campaign of Natural Language Processing and Speech Tools for Italian. EVALITA is an initiative of the Italian Association for Computational Linguistics (AILC, http://www.ai-lc.it) and it is endorsed by the Italian Association for Artificial Intelligence (AIxIA, http://www.aixia.it) and the Italian Association for Speech Sciences (AISV, http://www.aisv.it)

Compositional language processing for multilingual sentiment analysis

Programa Oficial de Doutoramento en Computación. 5009V01[Abstract] This dissertation presents new approaches in the field of sentiment analysis and polarity classification, oriented towards obtaining the sentiment of a phrase, sentence or document from a natural language processing point of view. It makes a special emphasis on methods to handle semantic composionality, i. e. the ability to compound the sentiment of multiword phrases, where the global sentiment might be different or even opposite to the one coming from each of their their individual components; and the application of these methods to multilingual scenarios. On the one hand, we introduce knowledge-based approaches to calculate the semantic orientation at the sentence level, that can handle different phenomena for the purpose at hand (e. g. negation, intensification or adversative subordinate clauses). On the other hand, we describe how to build machine learning models to perform polarity classification from a different perspective, combining linguistic (lexical, syntactic and semantic) knowledge, with an emphasis in noisy and micro-texts. Experiments on standard corpora and international evaluation campaigns show the competitiveness of the methods here proposed, in monolingual, multilingual and code-switching scenarios. The contributions presented in the thesis have potential applications in the era of the Web 2.0 and social media, such as being able to determine what is the view of society about products, celebrities or events, identify their strengths and weaknesses or monitor how these opinions evolve over time. We also show how some of the proposed models can be useful for other data analysis tasks.[Resumen] Esta tesis presenta nuevas técnicas en el ámbito del análisis del sentimiento y la clasificación de polaridad, centradas en obtener el sentimiento de una frase, oración o documento siguiendo enfoques basados en procesamiento del lenguaje natural. En concreto, nos centramos en desarrollar métodos capaces de manejar la semántica composicional, es decir, con la capacidad de componer el sentimiento de oraciones donde la polaridad global puede ser distinta, o incluso opuesta, de la que se obtendría individualmente para cada uno de sus términos; y cómo dichos métodos pueden ser aplicados en entornos multilingües. En la primera parte de este trabajo, introducimos aproximaciones basadas en conocimiento para calcular la orientación semántica a nivel de oración, teniendo en cuenta construcciones lingüísticas relevantes en el ámbito que nos ocupa (por ejemplo, la negación, intensificación, o las oraciones subordinadas adversativas). En la segunda parte, describimos cómo construir clasificadores de polaridad basados en aprendizaje automático que combinan información léxica, sintáctica y semántica; centrándonos en su aplicación sobre textos cortos y de pobre calidad gramatical. Los experimentos realizados sobre colecciones estándar y competiciones de evaluación internacionales muestran la efectividad de los métodos aquí propuestos en entornos monolingües, multilingües y de code-switching. Las contribuciones presentadas en esta tesis tienen diversas aplicaciones en la era de la Web 2.0 y las redes sociales, como determinar la opinión que la sociedad tiene sobre un producto, celebridad o evento; identificar sus puntos fuertes y débiles o monitorizar cómo estas opiniones evolucionan a lo largo del tiempo. Por último, también mostramos cómo algunos de los modelos propuestos pueden ser útiles para otras tareas de análisis de datos.[Resumo] Esta tese presenta novas técnicas no ámbito da análise do sentimento e da clasificación da polaridade, orientadas a obter o sentimento dunha frase, oración ou documento seguindo aproximacións baseadas no procesamento da linguaxe natural. En particular, centrámosnos en métodos capaces de manexar a semántica composicional: métodos coa habilidade para compor o sentimento de oracións onde o sentimento global pode ser distinto, ou incluso oposto, do que se obtería individualmente para cada un dos seus términos; e como ditos métodos poden ser aplicados en entornos multilingües. Na primeira parte da tese, introducimos aproximacións baseadas en coñecemento; para calcular a orientación semántica a nivel de oración, tendo en conta construccións lingüísticas importantes no ámbito que nos ocupa (por exemplo, a negación, a intensificación ou as oracións subordinadas adversativas). Na segunda parte, describimos como podemos construir clasificadores de polaridade baseados en aprendizaxe automática e que combinan información léxica, sintáctica e semántica, centrándonos en textos curtos e de pobre calidade gramatical. Os experimentos levados a cabo sobre coleccións estándar e competicións de avaliación internacionais mostran a efectividade dos métodos aquí propostos, en entornos monolingües, multilingües e de code-switching. As contribucións presentadas nesta tese teñen diversas aplicacións na era da Web 2.0 e das redes sociais, como determinar a opinión que a sociedade ten sobre un produto, celebridade ou evento; identificar os seus puntos fortes e febles ou monitorizar como esas opinións evolucionan o largo do tempo. Como punto final, tamén amosamos como algúns dos modelos aquí propostos poden ser útiles para outras tarefas de análise de datos

Cross-Domain information extraction from scientific articles for research knowledge graphs

Today’s scholarly communication is a document-centred process and as such, rather inefficient. Fundamental contents of research papers are not accessible by computers since they are only present in unstructured PDF files. Therefore, current research infrastructures are not able to assist scientists appropriately in their core research tasks. This thesis addresses this issue and proposes methods to automatically extract relevant information from scientific articles for Research Knowledge Graphs (RKGs) that represent scholarly knowledge structured and interlinked. First, this thesis conducts a requirements analysis for an Open Research Knowledge Graph (ORKG). We present literature-related use cases of researchers that should be supported by an ORKG-based system and their specific requirements for the underlying ontology and instance data. Based on this analysis, the identified use cases are categorised into two groups: The first group of use cases needs manual or semi-automatic approaches for knowledge graph (KG) construction since they require high correctness of the instance data. The second group requires high completeness and can tolerate noisy instance data. Thus, this group needs automatic approaches for KG population. This thesis focuses on the second group of use cases and provides contributions for machine learning tasks that aim to support them. To assess the relevance of a research paper, scientists usually skim through titles, abstracts, introductions, and conclusions. An organised presentation of the articles' essential information would make this process more time-efficient. The task of sequential sentence classification addresses this issue by classifying sentences in an article in categories like research problem, used methods, or obtained results. To address this problem, we propose a novel unified cross-domain multi-task deep learning approach that makes use of datasets from different scientific domains (e.g. biomedicine and computer graphics) and varying structures (e.g. datasets covering either only abstracts or full papers). Our approach outperforms the state of the art on full paper datasets significantly while being competitive for datasets consisting of abstracts. Moreover, our approach enables the categorisation of sentences in a domain-independent manner. Furthermore, we present the novel task of domain-independent information extraction to extract scientific concepts from research papers in a domain-independent manner. This task aims to support the use cases find related work and get recommended articles. For this purpose, we introduce a set of generic scientific concepts that are relevant over ten domains in Science, Technology, and Medicine (STM) and release an annotated dataset of 110 abstracts from these domains. Since the annotation of scientific text is costly, we suggest an active learning strategy based on a state-of-the-art deep learning approach. The proposed method enables us to nearly halve the amount of required training data. Then, we extend this domain-independent information extraction approach with the task of \textit{coreference resolution}. Coreference resolution aims to identify mentions that refer to the same concept or entity. Baseline results on our corpus with current state-of-the-art approaches for coreference resolution showed that current approaches perform poorly on scientific text. Therefore, we propose a sequential transfer learning approach that exploits annotated datasets from non-academic domains. Our experimental results demonstrate that our approach noticeably outperforms the state-of-the-art baselines. Additionally, we investigate the impact of coreference resolution on KG population. We demonstrate that coreference resolution has a small impact on the number of resulting concepts in the KG, but improved its quality significantly. Consequently, using our domain-independent information extraction approach, we populate an RKG from 55,485 abstracts of the ten investigated STM domains. We show that every domain mainly uses its own terminology and that the populated RKG contains useful concepts. Moreover, we propose a novel approach for the task of \textit{citation recommendation}. This task can help researchers improve the quality of their work by finding or recommending relevant related work. Our approach exploits RKGs that interlink research papers based on mentioned scientific concepts. Using our automatically populated RKG, we demonstrate that the combination of information from RKGs with existing state-of-the-art approaches is beneficial. Finally, we conclude the thesis and sketch possible directions of future work.Die Kommunikation von Forschungsergebnissen erfolgt heutzutage in Form von Dokumenten und ist aus verschiedenen Gründen ineffizient. Wesentliche Inhalte von Forschungsarbeiten sind für Computer nicht zugänglich, da sie in unstrukturierten PDF-Dateien verborgen sind. Daher können derzeitige Forschungsinfrastrukturen Forschende bei ihren Kernaufgaben nicht angemessen unterstützen. Diese Arbeit befasst sich mit dieser Problemstellung und untersucht Methoden zur automatischen Extraktion von relevanten Informationen aus Forschungspapieren für Forschungswissensgraphen (Research Knowledge Graphs). Solche Graphen sollen wissenschaftliches Wissen maschinenlesbar strukturieren und verknüpfen. Zunächst wird eine Anforderungsanalyse für einen Open Research Knowledge Graph (ORKG) durchgeführt. Wir stellen literaturbezogene Anwendungsfälle von Forschenden vor, die durch ein ORKG-basiertes System unterstützt werden sollten, und deren spezifische Anforderungen an die zugrundeliegende Ontologie und die Instanzdaten. Darauf aufbauend werden die identifizierten Anwendungsfälle in zwei Gruppen eingeteilt: Die erste Gruppe von Anwendungsfällen benötigt manuelle oder halbautomatische Ansätze für die Konstruktion eines ORKG, da sie eine hohe Korrektheit der Instanzdaten erfordern. Die zweite Gruppe benötigt eine hohe Vollständigkeit der Instanzdaten und kann fehlerhafte Daten tolerieren. Daher erfordert diese Gruppe automatische Ansätze für die Konstruktion des ORKG. Diese Arbeit fokussiert sich auf die zweite Gruppe von Anwendungsfällen und schlägt Methoden für maschinelle Aufgabenstellungen vor, die diese Anwendungsfälle unterstützen können. Um die Relevanz eines Forschungsartikels effizient beurteilen zu können, schauen sich Forschende in der Regel die Titel, Zusammenfassungen, Einleitungen und Schlussfolgerungen an. Durch eine strukturierte Darstellung von wesentlichen Informationen des Artikels könnte dieser Prozess zeitsparender gestaltet werden. Die Aufgabenstellung der sequenziellen Satzklassifikation befasst sich mit diesem Problem, indem Sätze eines Artikels in Kategorien wie Forschungsproblem, verwendete Methoden oder erzielte Ergebnisse automatisch klassifiziert werden. In dieser Arbeit wird für diese Aufgabenstellung ein neuer vereinheitlichter Multi-Task Deep-Learning-Ansatz vorgeschlagen, der Datensätze aus verschiedenen wissenschaftlichen Bereichen (z. B. Biomedizin und Computergrafik) mit unterschiedlichen Strukturen (z. B. Datensätze bestehend aus Zusammenfassungen oder vollständigen Artikeln) nutzt. Unser Ansatz übertrifft State-of-the-Art-Verfahren der Literatur auf Benchmark-Datensätzen bestehend aus vollständigen Forschungsartikeln. Außerdem ermöglicht unser Ansatz die Klassifizierung von Sätzen auf eine domänenunabhängige Weise. Darüber hinaus stellen wir die neue Aufgabenstellung domänenübergreifende Informationsextraktion vor. Hierbei werden, unabhängig vom behandelten wissenschaftlichen Fachgebiet, inhaltliche Konzepte aus Forschungspapieren extrahiert. Damit sollen die Anwendungsfälle Finden von verwandten Arbeiten und Empfehlung von Artikeln unterstützt werden. Zu diesem Zweck führen wir eine Reihe von generischen wissenschaftlichen Konzepten ein, die in zehn Bereichen der Wissenschaft, Technologie und Medizin (STM) relevant sind, und veröffentlichen einen annotierten Datensatz von 110 Zusammenfassungen aus diesen Bereichen. Da die Annotation wissenschaftlicher Texte aufwändig ist, kombinieren wir ein Active-Learning-Verfahren mit einem aktuellen Deep-Learning-Ansatz, um die notwendigen Trainingsdaten zu reduzieren. Die vorgeschlagene Methode ermöglicht es uns, die Menge der erforderlichen Trainingsdaten nahezu zu halbieren. Anschließend erweitern wir unseren domänenunabhängigen Ansatz zur Informationsextraktion um die Aufgabe der Koreferenzauflösung. Die Auflösung von Koreferenzen zielt darauf ab, Erwähnungen zu identifizieren, die sich auf dasselbe Konzept oder dieselbe Entität beziehen. Experimentelle Ergebnisse auf unserem Korpus mit aktuellen Ansätzen zur Koreferenzauflösung haben gezeigt, dass diese bei wissenschaftlichen Texten unzureichend abschneiden. Daher schlagen wir eine Transfer-Learning-Methode vor, die annotierte Datensätze aus nicht-akademischen Bereichen nutzt. Die experimentellen Ergebnisse zeigen, dass unser Ansatz deutlich besser abschneidet als die bisherigen Ansätze. Darüber hinaus untersuchen wir den Einfluss der Koreferenzauflösung auf die Erstellung von Wissensgraphen. Wir zeigen, dass diese einen geringen Einfluss auf die Anzahl der resultierenden Konzepte in dem Wissensgraphen hat, aber die Qualität des Wissensgraphen deutlich verbessert. Mithilfe unseres domänenunabhängigen Ansatzes zur Informationsextraktion haben wir aus 55.485 Zusammenfassungen der zehn untersuchten STM-Domänen einen Forschungswissensgraphen erstellt. Unsere Analyse zeigt, dass jede Domäne hauptsächlich ihre eigene Terminologie verwendet und dass der erstellte Wissensgraph nützliche Konzepte enthält. Schließlich schlagen wir einen Ansatz für die Empfehlung von passenden Referenzen vor. Damit können Forschende einfacher relevante verwandte Arbeiten finden oder passende Empfehlungen erhalten. Unser Ansatz nutzt Forschungswissensgraphen, die Forschungsarbeiten mit in ihnen erwähnten wissenschaftlichen Konzepten verknüpfen. Wir zeigen, dass aktuelle Verfahren zur Empfehlung von Referenzen von zusätzlichen Informationen aus einem automatisch erstellten Wissensgraphen profitieren. Zum Schluss wird ein Fazit gezogen und ein Ausblick für mögliche zukünftige Arbeiten gegeben

Learning disentangled speech representations

A variety of informational factors are contained within the speech signal and a single short recording of speech reveals much more than the spoken words. The best method to extract and represent informational factors from the speech signal ultimately depends on which informational factors are desired and how they will be used. In addition, sometimes methods will capture more than one informational factor at the same time such as speaker identity, spoken content, and speaker prosody. The goal of this dissertation is to explore different ways to deconstruct the speech signal into abstract representations that can be learned and later reused in various speech technology tasks. This task of deconstructing, also known as disentanglement, is a form of distributed representation learning. As a general approach to disentanglement, there are some guiding principles that elaborate what a learned representation should contain as well as how it should function. In particular, learned representations should contain all of the requisite information in a more compact manner, be interpretable, remove nuisance factors of irrelevant information, be useful in downstream tasks, and independent of the task at hand. The learned representations should also be able to answer counter-factual questions. In some cases, learned speech representations can be re-assembled in different ways according to the requirements of downstream applications. For example, in a voice conversion task, the speech content is retained while the speaker identity is changed. And in a content-privacy task, some targeted content may be concealed without affecting how surrounding words sound. While there is no single-best method to disentangle all types of factors, some end-to-end approaches demonstrate a promising degree of generalization to diverse speech tasks. This thesis explores a variety of use-cases for disentangled representations including phone recognition, speaker diarization, linguistic code-switching, voice conversion, and content-based privacy masking. Speech representations can also be utilised for automatically assessing the quality and authenticity of speech, such as automatic MOS ratings or detecting deep fakes. The meaning of the term "disentanglement" is not well defined in previous work, and it has acquired several meanings depending on the domain (e.g. image vs. speech). Sometimes the term "disentanglement" is used interchangeably with the term "factorization". This thesis proposes that disentanglement of speech is distinct, and offers a viewpoint of disentanglement that can be considered both theoretically and practically

Automated Semantic Analysis, Legal Assessment, and Summarization of Standard Form Contracts

Consumers are confronted with standard form contracts on a daily basis, for example, when shopping online, registering for online platforms, or opening bank accounts. With expected revenue of more than 343 billion Euro in 2020, e-commerce is an ever more important branch of the European economy. Accepting standard form contracts often is a prerequisite to access products or services, and consumers frequently do so without reading, let alone understanding, them. Consumer protection organizations can advise and represent consumers in such situations of power imbalance. However, with increasing demand, limited budgets, and ever more complex regulations, they struggle to provide the necessary support. This thesis investigates techniques for the automated semantic analysis, legal assessment, and summarization of standard form contracts in German and English, which can be used to support consumers and those who protect them. We focus on Terms and Conditions from the fast growing market of European e-commerce, but also show that the developed techniques can in parts be applied to other types of standard form contracts. We elicited requirements from consumers and consumer advocates to understand their needs, identified the most relevant clause topics, and analyzed the processes in consumer protection organizations concerning the handling of standard form contracts. Based on these insights, a pipeline for the automated semantic analysis, legal assessment, and summarization of standard form contracts was developed. The components of this pipeline can automatically identify and extract standard form contracts from the internet and hierarchically structure them into their individual clauses. Clause topics can be automatically identified, and relevant information can be extracted. Clauses can then be legally assessed, either using a knowledge-base we constructed or through binary classification by a transformer model. This information is then used to create summaries that are tailored to the needs of the different user groups. For each step of the pipeline, different approaches were developed and compared, from classical rule-based systems to deep learning techniques. Each approach was evaluated on German and English corpora containing more than 10,000 clauses, which were annotated as part of this thesis. The developed pipeline was prototypically implemented as part of a web-based tool to support consumer advocates in analyzing and assessing standard form contracts. The implementation was evaluated with experts from two German consumer protection organizations with questionnaires and task-based evaluations. The results of the evaluation show that our system can identify over 50 different types of clauses, which cover more than 90% of the clauses typically occurring in Terms and Conditions from online shops, with an accuracy of 0.80 to 0.84. The system can also automatically extract 21 relevant data points from these clauses with a precision of 0.91 and a recall of 0.86. On a corpus of more than 200 German clauses, the system was also able to assess the legality of clauses with an accuracy of 0.90. The expert evaluation has shown that the system is indeed able to support consumer advocates in their daily work by reducing the time they need to analyze and assess clauses in standard form contracts

Linguistic- and Acoustic-based Automatic Dementia Detection using Deep Learning Methods

Dementia can affect a person's speech and language abilities, even in the early stages. Dementia is incurable, but early detection can enable treatment that can slow down and maintain mental function. Therefore, early diagnosis of dementia is of great importance. However, current dementia detection procedures in clinical practice are expensive, invasive, and sometimes inaccurate. In comparison, computational tools based on the automatic analysis of spoken language have the potential to be applied as a cheap, easy-to-use, and objective clinical assistance tool for dementia detection. In recent years, several studies have shown promise in this area. However, most studies focus heavily on the machine learning aspects and, as a consequence, often lack sufficient incorporation of clinical knowledge. Many studies also concentrate on clinically less relevant tasks such as the distinction between HC and people with AD which is relatively easy and therefore less interesting both in terms of the machine learning and the clinical application. The studies in this thesis concentrate on automatically identifying signs of neurodegenerative dementia in the early stages and distinguishing them from other clinical, diagnostic categories related to memory problems: (FMD, MCI, and HC). A key focus, when designing the proposed systems has been to better consider (and incorporate) currently used clinical knowledge and also to bear in mind how these machine-learning based systems could be translated for use in real clinical settings. Firstly, a state-of-the-art end-to-end system is constructed for extracting linguistic information from automatically transcribed spontaneous speech. The system's architecture is based on hierarchical principles thereby mimicking those used in clinical practice where information at both word-, sentence- and paragraph-level is used when extracting information to be used for diagnosis. Secondly, hand-crafted features are designed that are based on clinical knowledge of the importance of pausing and rhythm. These are successfully joined with features extracted from the end-to-end system. Thirdly, different classification tasks are explored, each set up so as to represent the types of diagnostic decision-making that is relevant in clinical practice. Finally, experiments are conducted to explore how to better deal with the known problem of confounding and overlapping symptoms on speech and language from age and cognitive decline. A multi-task system is constructed that takes age into account while predicting cognitive decline. The studies use the publicly available DementiaBank dataset as well as the IVA dataset, which has been collected by our collaborators at the Royal Hallamshire Hospital, UK. In conclusion, this thesis proposes multiple methods of using speech and language information for dementia detection with state-of-the-art deep learning technologies, confirming the automatic system's potential for dementia detection