Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118 pages, 8 figures, 1 tabl

Application of fuzzy sets in data-to-text system

This PhD dissertation addresses the convergence of two distinct paradigms: fuzzy sets and natural language generation. The object of study is the integration of fuzzy set-derived techniques that model imprecision and uncertainty in human language into systems that generate textual information from numeric data, commonly known as data-to-text systems. This dissertation covers an extensive state of the art review, potential convergence points, two real data-to-text applications that integrate fuzzy sets (in the meteorology and learning analytics domains), and a model that encompasses the most relevant elements in the linguistic description of data discipline and provides a framework for building and integrating fuzzy set-based approaches into natural language generation/data-to-ext systems

Automatic tagging and geotagging in video collections and communities

Automatically generated tags and geotags hold great promise to improve access to video collections and online communi- ties. We overview three tasks offered in the MediaEval 2010 benchmarking initiative, for each, describing its use scenario, definition and the data set released. For each task, a reference algorithm is presented that was used within MediaEval 2010 and comments are included on lessons learned. The Tagging Task, Professional involves automatically matching episodes in a collection of Dutch television with subject labels drawn from the keyword thesaurus used by the archive staff. The Tagging Task, Wild Wild Web involves automatically predicting the tags that are assigned by users to their online videos. Finally, the Placing Task requires automatically assigning geo-coordinates to videos. The specification of each task admits the use of the full range of available information including user-generated metadata, speech recognition transcripts, audio, and visual features

Sentiment classification with case-base approach

L'augmentation de la croissance des réseaux, des blogs et des utilisateurs des sites d'examen sociaux font d'Internet une énorme source de données, en particulier sur la façon dont les gens pensent, sentent et agissent envers différentes questions. Ces jours-ci, les opinions des gens jouent un rôle important dans la politique, l'industrie, l'éducation, etc. Alors, les gouvernements, les grandes et petites industries, les instituts universitaires, les entreprises et les individus cherchent à étudier des techniques automatiques fin d’extraire les informations dont ils ont besoin dans les larges volumes de données. L’analyse des sentiments est une véritable réponse à ce besoin. Elle est une application de traitement du langage naturel et linguistique informatique qui se compose de techniques de pointe telles que l'apprentissage machine et les modèles de langue pour capturer les évaluations positives, négatives ou neutre, avec ou sans leur force, dans des texte brut. Dans ce mémoire, nous étudions une approche basée sur les cas pour l'analyse des sentiments au niveau des documents. Notre approche basée sur les cas génère un classificateur binaire qui utilise un ensemble de documents classifies, et cinq lexiques de sentiments différents pour extraire la polarité sur les scores correspondants aux commentaires. Puisque l'analyse des sentiments est en soi une tâche dépendante du domaine qui rend le travail difficile et coûteux, nous appliquons une approche «cross domain» en basant notre classificateur sur les six différents domaines au lieu de le limiter à un seul domaine. Pour améliorer la précision de la classification, nous ajoutons la détection de la négation comme une partie de notre algorithme. En outre, pour améliorer la performance de notre approche, quelques modifications innovantes sont appliquées. Il est intéressant de mentionner que notre approche ouvre la voie à nouveaux développements en ajoutant plus de lexiques de sentiment et ensembles de données à l'avenir.Increasing growth of the social networks, blogs, and user review sites make Internet a huge source of data especially about how people think, feel, and act toward different issues. These days, people opinions play an important role in the politic, industry, education, etc. Thus governments, large and small industries, academic institutes, companies, and individuals are looking for investigating automatic techniques to extract their desire information from large amount of data. Sentiment analysis is one true answer to this need. Sentiment analysis is an application of natural language processing and computational linguistic that consists of advanced techniques such as machine learning and language model approaches to capture the evaluative factors such as positive, negative, or neutral, with or without their strength, from plain texts. In this thesis we study a case-based approach on cross-domain for sentiment analysis on the document level. Our case-based algorithm generates a binary classifier that uses a set of the processed cases, and five different sentiment lexicons to extract the polarity along the corresponding scores from the reviews. Since sentiment analysis inherently is a domain dependent task that makes it problematic and expensive work, we use a cross-domain approach by training our classifier on the six different domains instead of limiting it to one domain. To improve the accuracy of the classifier, we add negation detection as a part of our algorithm. Moreover, to improve the performance of our approach, some innovative modifications are applied. It is worth to mention that our approach allows for further developments by adding more sentiment lexicons and data sets in the future

Visual Question Answering: A Survey of Methods and Datasets

Visual Question Answering (VQA) is a challenging task that has received increasing attention from both the computer vision and the natural language processing communities. Given an image and a question in natural language, it requires reasoning over visual elements of the image and general knowledge to infer the correct answer. In the first part of this survey, we examine the state of the art by comparing modern approaches to the problem. We classify methods by their mechanism to connect the visual and textual modalities. In particular, we examine the common approach of combining convolutional and recurrent neural networks to map images and questions to a common feature space. We also discuss memory-augmented and modular architectures that interface with structured knowledge bases. In the second part of this survey, we review the datasets available for training and evaluating VQA systems. The various datatsets contain questions at different levels of complexity, which require different capabilities and types of reasoning. We examine in depth the question/answer pairs from the Visual Genome project, and evaluate the relevance of the structured annotations of images with scene graphs for VQA. Finally, we discuss promising future directions for the field, in particular the connection to structured knowledge bases and the use of natural language processing models.Comment: 25 page

Rakenduste kasutajaarvustustest informatsiooni kaevandamine tarkvara arendustegevuste soodustamiseks

Kasutajate vajaduste ja ootuste hindamine on arendajate jaoks oluline oma tarkvararakenduste kvaliteedi parandamiseks. Mobiilirakenduste platvormidele sisestatud arvustused on kasulikuks infoallikaks kasutajate pidevalt muutuvate vajaduste hindamiseks. Igapäevaselt rakenduste platvormidele esitatud arvustuste suur maht nõuab aga automaatseid meetodeid neist kasuliku info leidmiseks. Arvustuste automaatseks liigitamiseks, nt veateatis või uue funktsionaalsuse küsimine, saab kasutada teksti klassifitseerimismudeleid. Rakenduse funktsioonide automaatne kaevandamine arvustustest aitab teha kokkuvõtteid kasutajate meelsusest rakenduse olemasolevate funktsioonide osas. Kõigepealt eksperimenteerime erinevate tekstiklassifitseerimise mudelitega ning võrdleme lihtsaid, leksikaalseid tunnuseid kasutavaid mudeleid keerukamatega, mis kasutavad rikkalikke lingvistilisi tunnuseid või mis põhinevad tehisnärvivõrkudel. Erinevate faktorite mõju uurimiseks funktsioonide kaevandamise meetoditele me teeme kõigepealt kindlaks erinevate meetodite baastaseme täpsuse rakendades neid samades eksperimentaalsetes tingimustes. Seejärel võrdleme neid meetodeid erinevates tingimustes, varieerides treenimiseks kasutatud annoteeritud andmestikke ning hindamismeetodeid. Kuna juhendatud masinõppel baseeruvad kaevandamismeetodid on võrreldes reeglipõhistega tundlikumad (1) andmete märgendamisel kasutatud annoteerimisjuhistele ning (2) märgendatatud andmestiku suurusele, siis uurisime nende faktorite mõju juhendatud masinõppe kontekstis ja pakkusime välja uued annoteerimisjuhised, mis võivad aidata funktsioonide kaevandamise täpsust parandada. Käesoleva doktoritöö projekti tulemusel valmis ka kontseptuaalne tööriist, mis võimaldab konkureerivaid rakendusi omavahel võrrelda. Tööriist kombineerib arvustuse tekstide klassifitseerimise ja rakenduse funktsioonide kaevandamise meetodid. Tööriista hinnanud kümme tarkvaraarendajat leidsid, et sellest võib olla kasu rakenduse kvaliteedi parandamiselFor app developers, it is important to continuously evaluate the needs and expectations of their users to improve app quality. User reviews submitted to app marketplaces are regarded as a useful information source to re-access evolving user needs. The large volume of user reviews received every day requires automatic methods to find such information in user reviews. Text classification models can be used to categorize review information into types such as feature requests and bug reports, while automatic app feature extraction from user reviews can help in summarizing users’ sentiments at the level of app features. For classifying review information, we perform experiments to compare the performance of simple models using only lexical features to models with rich linguistic features and models built on deep learning architectures, i.e., Convolutional Neural Network (CNN). To investigate factors influencing the performance of automatic app feature extraction methods, i.e. rule-based and supervised machine learning, we first establish a baseline in a single experimental setting and then compare the performances in different experimental settings (i.e., varying annotated datasets and evaluation methods). Since the performance of supervised feature extraction methods is more sensitive than rule- based methods to (1) guidelines used to annotate app features in user reviews and (2) the size of the annotated data, we investigate their impact on the performance of supervised feature extraction models and suggest new annotation guidelines that have the potential to improve feature extraction performance. To make the research results of the thesis project also applicable for non-experts, we developed a proof-of-concept tool for comparing competing apps. The tool combines review classification and app feature extraction methods and has been evaluated by ten developers from industry who perceived it useful for improving the app quality.  https://www.ester.ee/record=b529379