Este documento apresenta um estudo em nível de mestrado na área de Geração de Língua Natural (GLN), enfocando a questão da variação humana na tarefa de Geração de Expressões de Referência (GER). O trabalho apresenta um levantamento bibliográfico sobre o tema, a criação de dois algoritmos de GER e a construção de um novo córpus de expressões de referência. Modelos computacionais de GER baseados nos algoritmos criados foram implementados em versões que incorporam e não incorporam a variação humana e empregados em uma série de experimentos de GER em sete córpus de expressões de referência. Resultados comprovam a hipótese inicial de que algoritmos de GER que levam em conta a variação humana podem gerar expressões de referência mais próximas a descrições de seres humanos do que algoritmos que não levam esta questão em conta. Além disso, confirmou-se que algoritmos de GER baseados em técnicas de aprendizado de máquina mostram-se superiores a algoritmos de GER consagrados e amplamente utilizados na literatura, como o algoritmo Incremental. / This work concerns a MSc Project in the field of Natural Language Generation (NLG), focusing on the issue of human variation in the Referring Expression Generation task (REG). The study presents a literature review on the topic, the proposal of two REG algorithms and the construction of a new corpus of referring expressions. Based on these algorithms, two REG models are implemented: with and without taking human variation. These models are employed in a series of REG experiments using seven referring expression corpora. Results confirm the initial hypothesis that REG algorithms that take speaker variation into account outperform existing algorithms that generate speaker-independent descriptions. Moreover, the present study confirms that algorithms based on machine learning techniques overperform existing algorithms, as the Dale and Reiter\'s Incremental algorithm.

Este trabalho apresenta um estudo em nível de mestrado na área de Geração de Língua Natural (GLN), enfocando a experimentação na tarefa de Geração de Expressões de Referência (GER). O trabalho apresenta um levantamento bibliográfico sobre o tema, abordando principalmente o modo monólogo e diálogo de realização destes experimentos. Além disso, é apresentado um modelo computacional para a validação automática das descrições produzidas em experimentos de GER, e a sua incorporação em uma ferramenta WEB para realização de experimentos de custo mais baixo, do tipo monólogo, com os benefícios de experimentos do tipo diálogo. O Modelo é avaliado de maneira intrínseca com base em um conjunto de córpus de GER, e de maneira extrínseca em um experimento real com humanos. Resultados comprovam a hipótese inicial de que descrições coletadas em modo monólogo com validação automática das descrições são mais próximas das descrições obtidas em modo diálogo do que as obtidas em modo monólogo / This work concerns a MSc Project in the field of Natural Language Generation (NLG), focusing on the issue of experimentation in the Referring Expression Generation (REG). The study presents a literature review on the topic, distinguishing between monologue and dialogue experiments. Moreover, a computational model for the validation of referring expressions collected in these experiments is presented. The proposed model is embedded in a WEB tool for the design of low-cost monologue experiments with the advantages of dialogue settings. The Model is assessed intrinsically based on a set of GER corpus, and extrinsically in a real experiment with humans. Results confirm the initial hypothesis that descriptions collected in monologue settings with automatic validation of the descriptions are closer to the descriptions obtained in dialog ones than those obtained in monologue settings

Este documento apresenta um estudo em nível de mestrado na área de Geração de Língua Natural (GLN), enfocando a questão da variação humana na tarefa de Geração de Expressões de Referência (GER). O trabalho apresenta um levantamento bibliográfico sobre o tema, a criação de dois algoritmos de GER e a construção de um novo córpus de expressões de referência. Modelos computacionais de GER baseados nos algoritmos criados foram implementados em versões que incorporam e não incorporam a variação humana e empregados em uma série de experimentos de GER em sete córpus de expressões de referência. Resultados comprovam a hipótese inicial de que algoritmos de GER que levam em conta a variação humana podem gerar expressões de referência mais próximas a descrições de seres humanos do que algoritmos que não levam esta questão em conta. Além disso, confirmou-se que algoritmos de GER baseados em técnicas de aprendizado de máquina mostram-se superiores a algoritmos de GER consagrados e amplamente utilizados na literatura, como o algoritmo Incremental. / This work concerns a MSc Project in the field of Natural Language Generation (NLG), focusing on the issue of human variation in the Referring Expression Generation task (REG). The study presents a literature review on the topic, the proposal of two REG algorithms and the construction of a new corpus of referring expressions. Based on these algorithms, two REG models are implemented: with and without taking human variation. These models are employed in a series of REG experiments using seven referring expression corpora. Results confirm the initial hypothesis that REG algorithms that take speaker variation into account outperform existing algorithms that generate speaker-independent descriptions. Moreover, the present study confirms that algorithms based on machine learning techniques overperform existing algorithms, as the Dale and Reiter\'s Incremental algorithm.

Este trabalho apresenta um estudo em nível de mestrado na área de Geração de Língua Natural (GLN), enfocando a experimentação na tarefa de Geração de Expressões de Referência (GER). O trabalho apresenta um levantamento bibliográfico sobre o tema, abordando principalmente o modo monólogo e diálogo de realização destes experimentos. Além disso, é apresentado um modelo computacional para a validação automática das descrições produzidas em experimentos de GER, e a sua incorporação em uma ferramenta WEB para realização de experimentos de custo mais baixo, do tipo monólogo, com os benefícios de experimentos do tipo diálogo. O Modelo é avaliado de maneira intrínseca com base em um conjunto de córpus de GER, e de maneira extrínseca em um experimento real com humanos. Resultados comprovam a hipótese inicial de que descrições coletadas em modo monólogo com validação automática das descrições são mais próximas das descrições obtidas em modo diálogo do que as obtidas em modo monólogo / This work concerns a MSc Project in the field of Natural Language Generation (NLG), focusing on the issue of experimentation in the Referring Expression Generation (REG). The study presents a literature review on the topic, distinguishing between monologue and dialogue experiments. Moreover, a computational model for the validation of referring expressions collected in these experiments is presented. The proposed model is embedded in a WEB tool for the design of low-cost monologue experiments with the advantages of dialogue settings. The Model is assessed intrinsically based on a set of GER corpus, and extrinsically in a real experiment with humans. Results confirm the initial hypothesis that descriptions collected in monologue settings with automatic validation of the descriptions are closer to the descriptions obtained in dialog ones than those obtained in monologue settings

Este documento apresenta uma pesquisa a nvel de mestrado acadêmico, cujo o foco é a tarefa computacional de Geração de Expressões de Referência (GER), uma parte fundamental da comunicação que é estudada na Geração de Linguagem Natural (GLN). Mais especificamente, foram estudados os aspectos da linguagem que se manifestam em contextos de comunicação com restrição de tempo, e com base nisso foi proposto um modelo computacional de GER para produzir expressões de referência com o nvel de superespecificação parametrizável. Além disso, considerando-se as caractersticas de adaptabilidade do modelo proposto, também foi sugerida uma generalização deste para outros domnios, como os que compreendem contextos de comunicação além dos que possuem restrição de tempo / This document presents a MSc research that focused on the computational subtask of Referring Expression Generation (REG), an important component of Natural Language Generation (NLG) systems. More specifically, this work analyzes how time-restricted contexts of communication may affect language production and a computational model of GER was proposed to produce reference expressions with parameterizable superspecification. Furthermore, given the adaptability characteristics of the proposed model, it has also been suggested a generalization to other domains, which includes communication contexts besides those that have time constraints

Sentiment analysis is a field of computational linguistics involving identification, extraction, and classification of opinions, sentiments, and emotions expressed in natural language. Sentiment classification algorithms aim to identify whether the author of a text has a positive or a negative opinion about a topic. One of the main indicators which help to detect the opinion are the words used in the texts. Needless to say, the sentiments expressed in the texts also depend on the syntactic structure and the discourse context. Supervised machine learning approaches to sentiment classification were shown to achieve good results. Classifying texts by emotions requires finer-grained analysis than sentiment classification. In this thesis, we explore the task of emotion and mood classification for blog postings. We propose a novel approach that uses the hierarchy of possible moods to achieve better results than a standard flat classification approach. We also show that using sentiment orientation features improves the performance of classification. We used the LiveJournal blog corpus as a dataset to train and evaluate our method. Another contribution of this work is extracting paraphrases for emotion terms based on the six basics emotions proposed by Ekman (\textit{happiness, anger, sadness, disgust, surprise, fear}). Paraphrases are different ways to express the same information. Algorithms to extract and automatically identify paraphrases are of interest from both linguistic and practical points of view. Our paraphrase extraction method is based on a bootstrapping algorithms that starts with seed words. Unlike in previous work, our algorithm does not need a parallel corpus. In Natural Language Generation (NLG), paraphrasing is employed to create more varied and natural text. In our research, we extract paraphrases for emotions, with the goal of using them to automatically generate emotional texts (such as friendly or hostile texts) for conversations between intelligent agents and characters in educational games. Nowadays, online services are popular in many disciplines such as: e-learning, interactive games, educational games, stock market, chat rooms and so on. NLG methods can be used in order to generate more interesting and normal texts for such applications. Generating text with emotions is one of the contributions of our work. In the last part of this thesis, we give an overview of NLG from an applied system's points of view. We discuss when NLG techniques can be used; we explained the requirements analysis and specification of NLG systems. We also, describe the main NLG tasks of content determination, discourse planning, sentence aggregation, lexicalization, referring expression generation, and linguistic realisation. Moreover, we describe our Authoring Tool that we developed in order to allow writers without programming skills to automatically generate texts for educational games. We develop an NLG system that can generate text with different emotions. To do this, we introduce our pattern-based model for generation. We show our model starts with initial patterns, then constructs extended patterns from which we choose ``final'' patterns that are suitable for generating emotion sentences. A user can generate sentences to express the desired emotions by using our patterns. Alternatively, the user can use our Authoring Tool to generate sentences with emotions. Our acquired paraphrases will be employed by the tool in order to generate more varied outputs.

Sentiment analysis is a field of computational linguistics involving identification, extraction, and classification of opinions, sentiments, and emotions expressed in natural language. Sentiment classification algorithms aim to identify whether the author of a text has a positive or a negative opinion about a topic. One of the main indicators which help to detect the opinion are the words used in the texts. Needless to say, the sentiments expressed in the texts also depend on the syntactic structure and the discourse context. Supervised machine learning approaches to sentiment classification were shown to achieve good results. Classifying texts by emotions requires finer-grained analysis than sentiment classification. In this thesis, we explore the task of emotion and mood classification for blog postings. We propose a novel approach that uses the hierarchy of possible moods to achieve better results than a standard flat classification approach. We also show that using sentiment orientation features improves the performance of classification. We used the LiveJournal blog corpus as a dataset to train and evaluate our method. Another contribution of this work is extracting paraphrases for emotion terms based on the six basics emotions proposed by Ekman (\textit{happiness, anger, sadness, disgust, surprise, fear}). Paraphrases are different ways to express the same information. Algorithms to extract and automatically identify paraphrases are of interest from both linguistic and practical points of view. Our paraphrase extraction method is based on a bootstrapping algorithms that starts with seed words. Unlike in previous work, our algorithm does not need a parallel corpus. In Natural Language Generation (NLG), paraphrasing is employed to create more varied and natural text. In our research, we extract paraphrases for emotions, with the goal of using them to automatically generate emotional texts (such as friendly or hostile texts) for conversations between intelligent agents and characters in educational games. Nowadays, online services are popular in many disciplines such as: e-learning, interactive games, educational games, stock market, chat rooms and so on. NLG methods can be used in order to generate more interesting and normal texts for such applications. Generating text with emotions is one of the contributions of our work. In the last part of this thesis, we give an overview of NLG from an applied system's points of view. We discuss when NLG techniques can be used; we explained the requirements analysis and specification of NLG systems. We also, describe the main NLG tasks of content determination, discourse planning, sentence aggregation, lexicalization, referring expression generation, and linguistic realisation. Moreover, we describe our Authoring Tool that we developed in order to allow writers without programming skills to automatically generate texts for educational games. We develop an NLG system that can generate text with different emotions. To do this, we introduce our pattern-based model for generation. We show our model starts with initial patterns, then constructs extended patterns from which we choose ``final'' patterns that are suitable for generating emotion sentences. A user can generate sentences to express the desired emotions by using our patterns. Alternatively, the user can use our Authoring Tool to generate sentences with emotions. Our acquired paraphrases will be employed by the tool in order to generate more varied outputs.

As our modern-built structures are becoming increasingly complex, carrying out basic tasks such as identifying points or objects of interest in our surroundings can consume considerable time and cognitive resources. In this thesis, we present a computational approach to converting contextual information about a person's physical environment into natural language, with the aim of helping this person identify given task-related entities in their environment. Using efficient methods from automated planning - the field of artificial intelligence concerned with finding courses of action that can achieve a goal -, we generate discourse that interactively guides a hearer through completing their task. Our approach addresses the challenges of controlling, adapting to, and monitoring the situated context. To this end, we develop a natural language generation system that plans how to manipulate the non-linguistic context of a scene in order to make it more favorable for references to task-related objects. This strategy distributes a hearer's cognitive load of interpreting a reference over multiple utterances rather than one long referring expression. Further, to optimize the system's linguistic choices in a given context, we learn how to distinguish speaker behavior according to its helpfulness to hearers in a certain situation, and we model the behavior of human speakers that has been proven helpful. The resulting system combines symbolic with statistical reasoning, and tackles the problem of making non-trivial referential choices in rich context. Finally, we complement our approach with a mechanism for preventing potential misunderstandings after a reference has been generated. Employing remote eye-tracking technology, we monitor the hearer's gaze and find that it provides a reliable index of online referential understanding, even in dynamically changing scenes. We thus present a system that exploits hearer gaze to generate rapid feedback on a per-utterance basis, further enhancing its effectiveness. Though we evaluate our approach in virtual environments, the efficiency of our planning-based model suggests that this work could be a step towards effective conversational human-computer interaction situated in the real world. / Die zunehmende Komplexität moderner Gebäude und Infrastrukturen führt dazu, dass alltägliche Aktivitäten, wie z.B. die Identifizierung von gesuchten Objekten in unserer Umgebung und das Auffinden von Orten, beträchtliche Zeit und kognitive Ressourcen in Anspruch nehmen können. In dieser Dissertation werden computerbasierte Verfahren präsentiert, welche eine Person dabei unterstützen, Zielobjekte in Ihrem Umfeld zu identifizieren. Dabei werden Informationen über die Situation und das physische Umfeld der Person - der sog. situierte Kontext - in natürliche Sprache umgewandelt. So wird Diskurs generiert, der einen Hörer interaktiv zum Erreichen eines Zieles bzw. zum Abschließen einer Aufgabe führt. Hierbei kommen Methoden aus der Planung zum Einsatz, einem Gebiet der künstlichen Intelligenz, welches sich mit der Berechnung von zielgerichteten Handlungsabfolgen beschäftigt. Die in dieser Arbeit vorgestellten Verfahren widmen sich den Herausforderungen der Kontrolle des situierten Kontexts, der Anpassung an den situierten Kontext sowie der Überwachung des situierten Kontexts. Zu diesem Zweck wird zunächst ein Sprachgenerierungssystem entwickelt, das plant, wie der nicht-linguistische Kontext einer Szene manipuliert werden kann, damit die Referenz auf relevante Objekte erleichtert wird. Dadurch ist es möglich, die kognitive Beanspruchung eines Hörers bei der Interpretation einer Referenz über mehrere sprachliche Äußerungen zu verteilen. Damit die linguistischen Entscheidungen des Systems in einem vorgegebenen Kontext optimiert werden können, wird weiterhin gelernt, die Äußerungen von Sprechern danach zu differenzieren, wie hilfreich sie in bestimmten Situationen für die Hörer waren. Dabei wird das Verhalten von menschlichen Sprechern, welches sich als hilfreich erwiesen hat, modelliert. Das daraus entstehende System kombiniert symbolisches und statistisches Schließen und stellt somit einen Lösungsansatz für das Problem dar, wie nicht-triviale referentielle Entscheidungen in reichem Kontext getroffen werden können. Zum Schluss wird ein komplementärer Mechanismus vorgestellt, der potentielle Missverständnisse bzgl. generierter Referenzen verhindern kann. Zu diesem Zweck kommt Blickerfassungstechnologie zum Einsatz. Auf Basis der Überwachung und Auswertung des Blicks des Hörers können Rückschlüsse über die Interpretation gegebener Referenzen gemacht werden; dieser Mechanismus funktioniert auch in sich dynamisch verändernden Szenen zuverlässig. Somit wird ein System präsentiert, welches den Blick des Hörers nutzt, um rasch Feedback zu generieren. Dieses Vorgehen verbessert die Effektivität des Diskurses zusätzlich. Die vorgestellten Verfahren werden in virtuellen Umwelten evaluiert. Die Effizienz des planungsbasierten Modells ist allerdings ein Indiz dafür, dass die in dieser Arbeit gemachten Vorschläge dazu dienen können, effektive Mensch-Computer-Interaktion auf Basis von Sprache auch in der realen Welt umzusetzen.

Sentiment analysis is a field of computational linguistics involving identification, extraction, and classification of opinions, sentiments, and emotions expressed in natural language. Sentiment classification algorithms aim to identify whether the author of a text has a positive or a negative opinion about a topic. One of the main indicators which help to detect the opinion are the words used in the texts. Needless to say, the sentiments expressed in the texts also depend on the syntactic structure and the discourse context. Supervised machine learning approaches to sentiment classification were shown to achieve good results. Classifying texts by emotions requires finer-grained analysis than sentiment classification. In this thesis, we explore the task of emotion and mood classification for blog postings. We propose a novel approach that uses the hierarchy of possible moods to achieve better results than a standard flat classification approach. We also show that using sentiment orientation features improves the performance of classification. We used the LiveJournal blog corpus as a dataset to train and evaluate our method. Another contribution of this work is extracting paraphrases for emotion terms based on the six basics emotions proposed by Ekman (\textit{happiness, anger, sadness, disgust, surprise, fear}). Paraphrases are different ways to express the same information. Algorithms to extract and automatically identify paraphrases are of interest from both linguistic and practical points of view. Our paraphrase extraction method is based on a bootstrapping algorithms that starts with seed words. Unlike in previous work, our algorithm does not need a parallel corpus. In Natural Language Generation (NLG), paraphrasing is employed to create more varied and natural text. In our research, we extract paraphrases for emotions, with the goal of using them to automatically generate emotional texts (such as friendly or hostile texts) for conversations between intelligent agents and characters in educational games. Nowadays, online services are popular in many disciplines such as: e-learning, interactive games, educational games, stock market, chat rooms and so on. NLG methods can be used in order to generate more interesting and normal texts for such applications. Generating text with emotions is one of the contributions of our work. In the last part of this thesis, we give an overview of NLG from an applied system's points of view. We discuss when NLG techniques can be used; we explained the requirements analysis and specification of NLG systems. We also, describe the main NLG tasks of content determination, discourse planning, sentence aggregation, lexicalization, referring expression generation, and linguistic realisation. Moreover, we describe our Authoring Tool that we developed in order to allow writers without programming skills to automatically generate texts for educational games. We develop an NLG system that can generate text with different emotions. To do this, we introduce our pattern-based model for generation. We show our model starts with initial patterns, then constructs extended patterns from which we choose ``final'' patterns that are suitable for generating emotion sentences. A user can generate sentences to express the desired emotions by using our patterns. Alternatively, the user can use our Authoring Tool to generate sentences with emotions. Our acquired paraphrases will be employed by the tool in order to generate more varied outputs.

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.