Information extraction

In this paper we present a new approach to extract relevant information by knowledge graphs from natural language text. We give a multiple level model based on knowledge graphs for describing template information, and investigate the concept of partial structural parsing. Moreover, we point out that expansion of concepts plays an important role in thinking, so we study the expansion of knowledge graphs to use context information for reasoning and merging of templates

Deep learning methods for knowledge base population

Knowledge bases store structured information about entities or concepts of the world and can be used in various applications, such as information retrieval or question answering. A major drawback of existing knowledge bases is their incompleteness. In this thesis, we explore deep learning methods for automatically populating them from text, addressing the following tasks: slot filling, uncertainty detection and type-aware relation extraction. Slot filling aims at extracting information about entities from a large text corpus. The Text Analysis Conference yearly provides new evaluation data in the context of an international shared task. We develop a modular system to address this challenge. It was one of the top-ranked systems in the shared task evaluations in 2015. For its slot filler classification module, we propose contextCNN, a convolutional neural network based on context splitting. It improves the performance of the slot filling system by 5.0% micro and 2.9% macro F1. To train our binary and multiclass classification models, we create a dataset using distant supervision and reduce the number of noisy labels with a self-training strategy. For model optimization and evaluation, we automatically extract a labeled benchmark for slot filler classification from the manual shared task assessments from 2012-2014. We show that results on this benchmark are correlated with slot filling pipeline results with a Pearson's correlation coefficient of 0.89 (0.82) on data from 2013 (2014). The combination of patterns, support vector machines and contextCNN achieves the best results on the benchmark with a micro (macro) F1 of 51% (53%) on test. Finally, we analyze the results of the slot filling pipeline and the impact of its components. For knowledge base population, it is essential to assess the factuality of the statements extracted from text. From the sentence "Obama was rumored to be born in Kenya", a system should not conclude that Kenya is the place of birth of Obama. Therefore, we address uncertainty detection in the second part of this thesis. We investigate attention-based models and make a first attempt to systematize the attention design space. Moreover, we propose novel attention variants: External attention, which incorporates an external knowledge source, k-max average attention, which only considers the vectors with the k maximum attention weights, and sequence-preserving attention, which allows to maintain order information. Our convolutional neural network with external k-max average attention sets the new state of the art on a Wikipedia benchmark dataset with an F1 score of 68%. To the best of our knowledge, we are the first to integrate an uncertainty detection component into a slot filling pipeline. It improves precision by 1.4% and micro F1 by 0.4%. In the last part of the thesis, we investigate type-aware relation extraction with neural networks. We compare different models for joint entity and relation classification: pipeline models, jointly trained models and globally normalized models based on structured prediction. First, we show that using entity class prediction scores instead of binary decisions helps relation classification. Second, joint training clearly outperforms pipeline models on a large-scale distantly supervised dataset with fine-grained entity classes. It improves the area under the precision-recall curve from 0.53 to 0.66. Third, we propose a model with a structured prediction output layer, which globally normalizes the score of a triple consisting of the classes of two entities and the relation between them. It improves relation extraction results by 4.4% F1 on a manually labeled benchmark dataset. Our analysis shows that the model learns correct correlations between entity and relation classes. Finally, we are the first to use neural networks for joint entity and relation classification in a slot filling pipeline. The jointly trained model achieves the best micro F1 score with a score of 22% while the neural structured prediction model performs best in terms of macro F1 with a score of 25%

Zero-gravity movement studies

The use of computer graphics to simulate the movement of articulated animals and mechanisms has a number of uses ranging over many fields. Human motion simulation systems can be useful in education, medicine, anatomy, physiology, and dance. In biomechanics, computer displays help to understand and analyze performance. Simulations can be used to help understand the effect of external or internal forces. Similarly, zero-gravity simulation systems should provide a means of designing and exploring the capabilities of hypothetical zero-gravity situations before actually carrying out such actions. The advantage of using a simulation of the motion is that one can experiment with variations of a maneuver before attempting to teach it to an individual. The zero-gravity motion simulation problem can be divided into two broad areas: human movement and behavior in zero-gravity, and simulation of articulated mechanisms

Second CLIPS Conference Proceedings, volume 1

Topics covered at the 2nd CLIPS Conference held at the Johnson Space Center, September 23-25, 1991 are given. Topics include rule groupings, fault detection using expert systems, decision making using expert systems, knowledge representation, computer aided design and debugging expert systems

HUMAN ROBOT INTERACTION THROUGH SEMANTIC INTEGRATION OF MULTIPLE MODALITIES, DIALOG MANAGEMENT, AND CONTEXTS

The hypothesis for this research is that applying the Human Computer Interaction (HCI) concepts of using multiple modalities, dialog management, context, and semantics to Human Robot Interaction (HRI) will improve the performance of Instruction Based Learning (IBL) compared to only using speech. We tested the hypothesis by simulating a domestic robot that can be taught to clean a house using a multi-modal interface. We used a method of semantically integrating the inputs from multiple modalities and contexts that multiplies a confidence score for each input by a Fusion Weight, sums the products, and then uses the input with the highest product sum. We developed an algorithm for determining the Fusion Weights. We concluded that different modalities, contexts, and modes of dialog management impact human robot interaction; however, which combination is better depends on the importance of the accuracy of learning what is taught versus the succinctness of the dialog between the user and the robot

Financial information extraction using pre-defined and user-definable templates in the Lolita system

Financial operators have today access to an extremely large amount of data, both quantitative and qualitative, real-time or historical and can use this information to support their decision-making process. Quantitative data are largely processed by automatic computer programs, often based on artificial intelligence techniques, that produce quantitative analysis, such as historical price analysis or technical analysis of price behaviour. Differently, little progress has been made in the processing of qualitative data, which mainly consists of financial news articles from financial newspapers or on-line news providers. As a result the financial market players are overloaded with qualitative information which is potentially extremely useful but, due to the lack of time, is often ignored. The goal of this work is to reduce the qualitative data-overload of the financial operators. The research involves the identification of the information in the source financial articles which is relevant for the financial operators' investment decision making process and to implement the associated templates in the LOLITA system. The system should process a large number of source articles and extract specific templates according to the relevant information located in the source articles. The project also involves the design and implementation in LOLITA of a user- definable template interface for allowing the users to easily design new templates using sentences in natural language. This allows user-defined information extraction from source texts. This differs from most of existing information extraction systems which require the developers to code the templates directly in the system. The results of the research have shown that the system performed well in the extraction of financial templates from source articles which would allow the financial operator to reduce his qualitative data-overload. The results have also shown that the user-definable template interface is a viable approach to user-defined information extraction. A trade-off has been identified between the ease of use of the user-definable template interface and the loss of performance compared to hand- coded templates

Prototype of a Conversational Assistant for Satellite Mission Operations

The very first artificial satellite, Sputnik, was launched in 1957 marking a new era. Concurrently, satellite mission operations emerged. These start at launch and finish at the end of mission, when the spacecraft is decommissioned. Running a satellite mission requires the monitoring and control of telemetry data, to verify and maintain satellite health, reconfigure and command the spacecraft, detect, identify and resolve anomalies and perform launch and early orbit operations. The very first chatbot, ELIZA was created in 1966, and also marked a new era of Artificial Intelligence Systems. Said systems answer users’ questions in the most diverse domains, interpreting the human language input and responding in the same manner. Nowadays, these systems are everywhere, and the list of possible applications seems endless. The goal of the present master’s dissertation is to develop a prototype of a chatbot for mission operations. For this purpose implementing a Natural Language Processing (NLP) model for satellite missions allied to a dialogue flow model. The performance of the conversational assistant is evaluated with its implementation on a mission operated by the European Space Agency (ESA), implying the generation of the spacecraft’s Database Knowledge Graph (KG). Throughout the years, many tools have been developed and added to the systems used to monitor and control spacecrafts helping Flight Control Teams (FCT) either by maintaining a comprehensive overview of the spacecraft’s status and health, speeding up failure investigation, or allowing to easily correlate time series of telemetry data. However, despite all the advances made which facilitate the daily tasks, the teams still need to navigate through thousands of parameters and events spanning years of data, using purposely built user interfaces and relying on filters and time series plots. The solution presented in this dissertation and proposed by VisionSpace Technologies focuses on improving operational efficiency whilst dealing with the mission’s complex and extensive databases.O primeiro satélite artificial, Sputnik, foi lançado em 1957 e marcou o início de uma nova era. Simultaneamente, surgiram as operações de missão de satélites. Estas iniciam com o lançamento e terminam com desmantelamento do veículo espacial, que marca o fim da missão. A operação de satélites exige o acompanhamento e controlo de dados de telemetria, com o intuito de verificar e manter a saúde do satélite, reconfigurar e comandar o veículo, detetar, identificar e resolver anomalias e realizar o lançamento e as operações iniciais do satélite. Em 1966, o primeiro Chatbot foi criado, ELIZA, e também marcou uma nova era, de sistemas dotados de Inteligência Artificial. Tais sistemas respondem a perguntas nos mais diversos domínios, para tal interpretando linguagem humana e repondendo de forma similar. Hoje em dia, é muito comum encontrar estes sistemas e a lista de aplicações possíveis parece infindável. O objetivo da presente dissertação de mestrado consiste em desenvolver o protótipo de um Chatbot para operação de satélites. Para este proposito, criando um modelo de Processamento de Linguagem Natural (NLP) aplicado a missoões de satélites aliado a um modelo de fluxo de diálogo. O desempenho do assistente conversacional será avaliado com a sua implementação numa missão operada pela Agência Espacial Europeia (ESA), o que implica a elaboração do grafico de conhecimentos associado à base de dados da missão. Ao longo dos anos, várias ferramentas foram desenvolvidas e adicionadas aos sistemas que acompanham e controlam veículos espaciais, que colaboram com as equipas de controlo de missão, mantendo uma visão abrangente sobre a condição do satélite, acelerando a investigação de falhas, ou permitindo correlacionar séries temporais de dados de telemetria. No entanto, apesar de todos os progressos que facilitam as tarefas diárias, as equipas ainda necessitam de navegar por milhares de parametros e eventos que abrangem vários anos de recolha de dados, usando interfaces para esse fim e dependendo da utilização de filtros e gráficos de series temporais. A solução apresentada nesta dissertação e proposta pela VisionSpace Technologies tem como foco melhorar a eficiência operacional lidando simultaneamente com as suas complexas e extensas bases de dados

Deep learning methods for knowledge base population

Knowledge bases store structured information about entities or concepts of the world and can be used in various applications, such as information retrieval or question answering. A major drawback of existing knowledge bases is their incompleteness. In this thesis, we explore deep learning methods for automatically populating them from text, addressing the following tasks: slot filling, uncertainty detection and type-aware relation extraction. Slot filling aims at extracting information about entities from a large text corpus. The Text Analysis Conference yearly provides new evaluation data in the context of an international shared task. We develop a modular system to address this challenge. It was one of the top-ranked systems in the shared task evaluations in 2015. For its slot filler classification module, we propose contextCNN, a convolutional neural network based on context splitting. It improves the performance of the slot filling system by 5.0% micro and 2.9% macro F1. To train our binary and multiclass classification models, we create a dataset using distant supervision and reduce the number of noisy labels with a self-training strategy. For model optimization and evaluation, we automatically extract a labeled benchmark for slot filler classification from the manual shared task assessments from 2012-2014. We show that results on this benchmark are correlated with slot filling pipeline results with a Pearson's correlation coefficient of 0.89 (0.82) on data from 2013 (2014). The combination of patterns, support vector machines and contextCNN achieves the best results on the benchmark with a micro (macro) F1 of 51% (53%) on test. Finally, we analyze the results of the slot filling pipeline and the impact of its components. For knowledge base population, it is essential to assess the factuality of the statements extracted from text. From the sentence "Obama was rumored to be born in Kenya", a system should not conclude that Kenya is the place of birth of Obama. Therefore, we address uncertainty detection in the second part of this thesis. We investigate attention-based models and make a first attempt to systematize the attention design space. Moreover, we propose novel attention variants: External attention, which incorporates an external knowledge source, k-max average attention, which only considers the vectors with the k maximum attention weights, and sequence-preserving attention, which allows to maintain order information. Our convolutional neural network with external k-max average attention sets the new state of the art on a Wikipedia benchmark dataset with an F1 score of 68%. To the best of our knowledge, we are the first to integrate an uncertainty detection component into a slot filling pipeline. It improves precision by 1.4% and micro F1 by 0.4%. In the last part of the thesis, we investigate type-aware relation extraction with neural networks. We compare different models for joint entity and relation classification: pipeline models, jointly trained models and globally normalized models based on structured prediction. First, we show that using entity class prediction scores instead of binary decisions helps relation classification. Second, joint training clearly outperforms pipeline models on a large-scale distantly supervised dataset with fine-grained entity classes. It improves the area under the precision-recall curve from 0.53 to 0.66. Third, we propose a model with a structured prediction output layer, which globally normalizes the score of a triple consisting of the classes of two entities and the relation between them. It improves relation extraction results by 4.4% F1 on a manually labeled benchmark dataset. Our analysis shows that the model learns correct correlations between entity and relation classes. Finally, we are the first to use neural networks for joint entity and relation classification in a slot filling pipeline. The jointly trained model achieves the best micro F1 score with a score of 22% while the neural structured prediction model performs best in terms of macro F1 with a score of 25%