Lexical Normalization of Spanish Tweets with Rule-Based Components and Language Models

This paper presents a system to normalize Spanish tweets, which uses preprocessing rules, a domain-appropriate edit-distance model, and language models to select correction candidates based on context. The system is an improvement on the tool we submitted to the Tweet-Norm 2013 shared task, and results on the task's test-corpus are above-average. Additionally, we provide a study of the impact for tweet normalization of the different components of the system: rule-based, edit-distance based and statistical

TweetNorm: a benchmark for lexical normalization of spanish tweets

The language used in social media is often characterized by the abundance of informal and non-standard writing. The normalization of this non-standard language can be crucial to facilitate the subsequent textual processing and to consequently help boost the performance of natural language processing tools applied to social media text. In this paper we present a benchmark for lexical normalization of social media posts, specifically for tweets in Spanish language. We describe the tweet normalization challenge we organized recently, analyze the performance achieved by the different systems submitted to the challenge, and delve into the characteristics of systems to identify the features that were useful. The organization of this challenge has led to the production of a benchmark for lexical normalization of social media, including an evaluation framework, as well as an annotated corpus of Spanish tweets-TweetNorm_es-, which we make publicly available. The creation of this benchmark and the evaluation has brought to light the types of words that submitted systems did best with, and posits the main shortcomings to be addressed in future work.Postprint (published version

Multilingual sentiment analysis in social media.

252 p.This thesis addresses the task of analysing sentiment in messages coming from social media. The ultimate goal was to develop a Sentiment Analysis system for Basque. However, because of the socio-linguistic reality of the Basque language a tool providing only analysis for Basque would not be enough for a real world application. Thus, we set out to develop a multilingual system, including Basque, English, French and Spanish.The thesis addresses the following challenges to build such a system:- Analysing methods for creating Sentiment lexicons, suitable for less resourced languages.- Analysis of social media (specifically Twitter): Tweets pose several challenges in order to understand and extract opinions from such messages. Language identification and microtext normalization are addressed.- Research the state of the art in polarity classification, and develop a supervised classifier that is tested against well known social media benchmarks.- Develop a social media monitor capable of analysing sentiment with respect to specific events, products or organizations

Multilingual sentiment analysis in social media.

252 p.This thesis addresses the task of analysing sentiment in messages coming from social media. The ultimate goal was to develop a Sentiment Analysis system for Basque. However, because of the socio-linguistic reality of the Basque language a tool providing only analysis for Basque would not be enough for a real world application. Thus, we set out to develop a multilingual system, including Basque, English, French and Spanish.The thesis addresses the following challenges to build such a system:- Analysing methods for creating Sentiment lexicons, suitable for less resourced languages.- Analysis of social media (specifically Twitter): Tweets pose several challenges in order to understand and extract opinions from such messages. Language identification and microtext normalization are addressed.- Research the state of the art in polarity classification, and develop a supervised classifier that is tested against well known social media benchmarks.- Develop a social media monitor capable of analysing sentiment with respect to specific events, products or organizations

GTI en TASS 2016 : Una aproximaci on supervisada para el an alisis de sentimiento basado en aspectos en Twitter

This paper describes the participation of the GTI research group of AtlantTIC, University of Vigo, in TASS 2016. This workshop is framed within the XXXII edition of the Annual Congress of the Spanish Society for Natural Language Processing event. In this work we propose a supervised approach based on classi ers, for the aspect based sentiment analysis task. Using this technique we managed to improve the performance of previous years, obtaining a solution reflecting the actual state-of-the-art.Este artículo describe la participación del grupo de investigación GTI, del centro AtlantTIC, perteneciente a la Universidad de Vigo, en el TASS 2016. Este taller es un evento enmarcado dentro de la XXXII edición del Congreso Anual de la Sociedad Española para el Procesamiento del Lenguaje Natural. En este trabajo se propone una aproximación supervisada, basada en clasificadores, para la tarea de análisis de sentimiento basado en aspectos. Mediante esta técnica hemos conseguido mejorar las prestaciones de ediciones anteriores, obteniendo una solución acorde con el estado del arte actual.Ministerio de Economía y Competitividad | Ref. TEC2013-47016-C2-1-RXunta de Galicia | Ref. GRC2014/04

DeustoTech Internet at TASS 2015: Sentiment analysis and polarity classification in spanish tweets

Resumen: Este artículo describe nuestro sistema presentado en el taller de análisis de sentimiento TASS 2015. Nuestro sistema aborda la tarea 1 del workshop, que consiste en realizar un análisis automático de sentimiento para determinar la polaridad global de un conjunto de tweets en español. Para ello, nuestro sistema se basa en un modelo supervisado con máquinas de soporte vectorial lineales en combinación con varios léxicos de polaridad. Se estudia la influencia de las diferentes característi-cas linguísticas y de diferentes tamaños de n-gramas en la mejora del algoritmo. Así mismo se presentan los resultados obtenidos, las diferentes pruebas que se han realizado, y una discusión sobre los resultados. Palabras clave: Análisis de sentimientos, clasificación de la polaridad, Twitter Abstract: This article describes our system presented at the workshop for sentiment analysis TASS 2015. Our system approaches the task 1 of the workshop, which consists on performing an automatic sentiment analysis to determine the global polarity of a set of tweets in Spanish. To do this, our system is based on a model supervised Linear Support Vector Machines combined with some polarity lexicons. The influence of the different linguistic features and the different sizes of n-grams in improving algorithm performance. Also the results obtained, the various tests that have been conducted, and a discussion of the results are presented

Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018)

Peer reviewe

Rapid Generation of Pronunciation Dictionaries for new Domains and Languages

This dissertation presents innovative strategies and methods for the rapid generation of pronunciation dictionaries for new domains and languages. Depending on various conditions, solutions are proposed and developed. Starting from the straightforward scenario in which the target language is present in written form on the Internet and the mapping between speech and written language is close up to the difficult scenario in which no written form for the target language exists

Artificial Neural Network methods applied to sentiment analysis

Sentiment Analysis (SA) is the study of opinions and emotions that are conveyed by text. This field of study has commercial applications for example in market research (e.g., “What do customers like and dislike about a product?”) and consumer behavior (e.g., “Which book will a customer buy next when he wrote a positive review about book X?”). A private person can benefit from SA by automatic movie or restaurant recommendations, or from applications on the computer or smart phone that adapt to the user’s current mood. In this thesis we will put forward research on artificial Neural Network (NN) methods applied to SA. Many challenges arise, such as sarcasm, domain dependency, and data scarcity, that need to be addressed by a successful system. In the first part of this thesis we perform linguistic analysis of a word (“hard”) under the light of SA. We show that sentiment-specific word sense disambiguation is necessary to distinguish fine nuances of polarity. Commonly available resources are not sufficient for this. The introduced Contextually Enhanced Sentiment Lexicon (CESL) is used to label occurrences of “hard” in a real dataset with its sense. That allows us to train a Support Vector Machine (SVM) with deep learning features that predicts the polarity of a single occurrence of the word, just given its context words. We show that the features we propose improve the result compared to existing standard features. Since the labeling effort is not negligible, we propose a clustering approach that reduces the manual effort to a minimum. The deep learning features that help predicting fine-grained, context-dependent polarity are computed by a Neural Network Language Model (NNLM), namely a variant of the Log-Bilinear Language model (LBL). By improving this model the performance of polarity classification might as well improve. Thus, we propose a non-linear version of the LBL and the vectorized Log-Bilinear Language model (vLBL), because non-linear models are generally considered more powerful. In a parameter study on a language modeling task, we show that the non-linear versions indeed perform better than their linear counterparts. However, the difference is small, except for settings where the model has only few parameters, which might be the case when little training data is available and the model therefore needs to be smaller in order to avoid overfitting. An alternative approach to fine-grained polarity classification as used above is to train classifiers that will do the distinction automatically. Due to the complexity of the task, the challenges of SA in general, and certain domain-specific issues (e.g., when using Twitter text) existing systems have much room to improve. Often statistical classifiers are used with simple Bag-of-Words (BOW) features or count features that stem from sentiment lexicons. We introduce a linguistically-informed Convolutional Neural Network (lingCNN) that builds upon the fact that there has been much research on language in general and sentiment lexicons in particular. lingCNN makes use of two types of linguistic features: word-based and sentence-based. Word-based features comprise features derived from sentiment lexicons, such as polarity or valence and general knowledge about language, such as a negation-based feature. Sentence-based features are also based on lexicon counts and valences. The combination of both types of features is superior to the original model without these features. Especially, when little training data is available (that can be the case for different languages that are underresourced), lingCNN proves to be significantly better (up to 12 macro-F1 points). Although, linguistic features in terms of sentiment lexicons are beneficial, their usage gives rise to a new set of problems. Most lexicons consist of infinitive forms of words only. Especially, lexicons for low-resource languages. However, the text that needs to be classified is unnormalized. Hence, we want to answer the question if morphological information is necessary for SA or if a system that neglects all this information and therefore can make better use of lexicons actually has an advantage. Our approach is to first stem or lemmatize a dataset and then perform polarity classification on it. On Czech and English datasets we show that better results can be achieved with normalization. As a positive side effect, we can compute better word embeddings by first normalizing the training corpus. This works especially well for languages that have rich morphology. We show on word similarity datasets for English, German, and Spanish that our embeddings improve performance. On a new WordNet-based evaluation we confirm these results on five different languages (Czech, English, German, Hungarian, and Spanish). The benefit of this new evaluation is further that it can be used for many other languages, as the only resource that is required is a WordNet. In the last part of the thesis, we use a recently introduced method to create an ultradense sentiment space out of generic word embeddings. This method allows us to compress 400 dimensional word embeddings down to 40 or even just 4 dimensions and still get similar results on a polarity classification task. While the training speed increases by a factor of 44, the difference in classification performance is not significant.Sentiment Analyse (SA) ist das Untersuchen von Meinungen und Emotionen die durch Text übermittelt werden. Dieses Forschungsgebiet findet kommerzielle Anwendungen in Marktforschung (z.B.: „Was mögen Kunden an einem Produkt (nicht)?“) und Konsumentenverhalten (z.B.: „Welches Buch wird ein Kunde als nächstes kaufen, nachdem er eine positive Rezension über Buch X geschrieben hat?“). Aber auch als Privatperson kann man von Forschung in SA profitieren. Beispiele hierfür sind automatisch erstellte Film- oder Restaurantempfehlungen oder Anwendungen auf Computer oder Smartphone die sich der aktuellen Stimmungslage des Benutzers anpassen. In dieser Arbeit werden wir Forschung auf dem Gebiet der Neuronen Netze (NN) angewendet auf SA vorantreiben. Dabei ergeben sich viele Herausforderungen, wie Sarkasmus, Domänenabhängigkeit und Datenarmut, die ein erfolgreiches System angehen muss. Im ersten Teil der Arbeit führen wir eine linguistische Analyse des englischen Wortes „hard“ in Hinblick auf SA durch. Wir zeigen, dass sentiment-spezifische Wortbedeutungsdisambiguierung notwendig ist, um feine Nuancen von Polarität (positive vs. negative Stimmung) unterscheiden zu können. Häufig verwendete, frei verfügbare Ressourcen sind dafür nicht ausreichend. Daher stellen wir CESL (Contextually Enhanced Sentiment Lexicon), ein sentiment-spezifisches Bedeutungslexicon vor, welches verwendet wird, um Vorkommen von „hard“ in einem realen Datensatz mit seinen Bedeutungen zu versehen. Das Lexikon erlaubt es eine Support Vector Machine (SVM) mit Features aus dem Deep Learning zu trainieren, die in der Lage ist, die Polarität eines Vorkommens nur anhand seiner Kontextwörter vorherzusagen. Wir zeigen, dass die vorgestellten Features die Ergebnisse der SVM verglichen mit Standard-Features verbessern. Da der Aufwand für das Erstellen von markierten Trainingsdaten nicht zu unterschätzen ist, stellen wir einen Clustering-Ansatz vor, der den manuellen Markierungsaufwand auf ein Minimum reduziert. Die Deep Learning Features, die die Vorhersage von feingranularer, kontextabhängiger Polarität verbessern, werden mittels eines neuronalen Sprachmodells, genauer eines Log-Bilinear Language model (LBL)s, berechnet. Wenn man dieses Modell verbessert, wird vermutlich auch das Ergebnis der Polaritätsklassifikation verbessert. Daher führen wir nichtlineare Versionen des LBL und vectorized Log-Bilinear Language model (vLBL) ein, weil nichtlineare Modelle generell als mächtiger angesehen werden. In einer Parameterstudie zur Sprachmodellierung zeigen wir, dass nichtlineare Modelle tatsächlich besser abschneiden, als ihre linearen Gegenstücke. Allerdings ist der Unterschied gering, es sei denn die Modelle können nur auf wenige Parameter zurückgreifen. So etwas kommt zum Beispiel vor, wenn nur wenige Trainingsdaten verfügbar sind und das Modell deshalb kleiner sein muss, um Überanpassung zu verhindern. Ein alternativer Ansatz zur feingranularen Polaritätsklassifikation wie oben verwendet, ist es, einen Klassifikator zu trainieren, der die Unterscheidung automatisch vornimmt. Durch die Komplexität der Aufgabe, der Herausforderungen von SA im Allgemeinen und speziellen domänenspezifischen Problemen (z.B.: wenn Twitter-Daten verwendet werden) haben existierende Systeme noch immer großes Optimierungspotential. Oftmals verwenden statistische Klassifikatoren einfache Bag-of-Words (BOW)-Features. Alternativ kommen Zähl-Features zum Einsatz, die auf Sentiment-Lexika aufsetzen. Wir stellen linguistically-informed Convolutional Neural Network (lingCNN) vor, dass auf dem Fakt beruht, dass bereits viel Forschung in Sprachen und Sentiment-Lexika geflossen ist. lingCNN macht von zwei linguistischen Feature-Typen Gebrauch: wortbasierte und satzbasierte. Wort-basierte Features umfassen Features die von Sentiment-Lexika, wie Polarität oder Valenz (die Stärke der Polarität) und generellem Wissen über Sprache, z.B.: Verneinung, herrühren. Satzbasierte Features basieren ebenfalls auf Zähl-Features von Lexika und auf Valenzen. Die Kombination beider Feature-Typen ist dem Originalmodell ohne linguistische Features überlegen. Besonders wenn wenige Trainingsdatensätze vorhanden sind (das kann der Fall für Sprachen sein, die weniger erforscht sind als englisch). lingCNN schneidet signifikant besser ab (bis zu 12 macro-F1 Punkte). Obwohl linguistische Features basierend auf Sentiment-Lexika vorteilhaft sind, führt deren Verwendung zu neuen Problemen. Der Großteil der Lexika enthält nur Infinitivformen der Wörter. Dies gilt insbesondere für Sprachen mit wenigen Ressourcen. Das ist eine Herausforderung, weil der Text der klassifiziert werden soll in der Regel nicht normalisiert ist. Daher wollen wir die Frage beantworten, ob morphologische Information für SA überhaupt notwendig ist oder ob ein System, dass jegliche morphologische Information ignoriert und dadurch bessere Verwendung der Lexika erzielt, einen Vorteil genießt. Unser Ansatz besteht aus Stemming und Lemmatisierung des Datensatzes, bevor dann die Polaritätsklassifikation durchgeführt wird. Auf englischen und tschechischen Daten zeigen wir, dass durch Normalisierung bessere Ergebnisse erzielt werden. Als positiven Nebeneffekt kann man bessere Wortrepresentationen (engl. word embeddings) berechnen, indem das Trainingskorpus zuerst normalisiert wird. Das funktioniert besonders gut für morphologisch reiche Sprachen. Wir zeigen auf Datensätzen zur Wortähnlichkeit für deutsch, englisch und spanisch, dass unsere Wortrepresentationen die Ergebnisse verbessern. In einer neuen WordNet-basierten Evaluation bestätigen wir diese Ergebnisse für fünf verschiedene Sprachen (deutsch, englisch, spanisch, tschechisch und ungarisch). Der Vorteil dieser Evaluation ist weiterhin, dass sie für viele Sprachen angewendet werden kann, weil sie lediglich ein WordNet als Ressource benötigt. Im letzten Teil der Arbeit verwenden wir eine kürzlich vorgestellte Methode zur Erstellen eines ultradichten Sentiment-Raumes aus generischen Wortrepresentationen. Diese Methode erlaubt es uns 400 dimensionale Wortrepresentationen auf 40 oder sogar nur 4 Dimensionen zu komprimieren und weiterhin die gleichen Resultate in Polaritätsklassifikation zu erhalten. Während die Trainingsgeschwindigkeit um einen Faktor von 44 verbessert wird, sind die Unterschiede in der Polaritätsklassifikation nicht signifikant

The Taming of the Shrew - non-standard text processing in the Digital Humanities

Natural language processing (NLP) has focused on the automatic processing of newspaper texts for many years. With the growing importance of text analysis in various areas such as spoken language understanding, social media processing and the interpretation of text material from the humanities, techniques and methodologies have to be reviewed and redefined since so called non-standard texts pose challenges on the lexical and syntactic level especially for machine-learning-based approaches. Automatic processing tools developed on the basis of newspaper texts show a decreased performance for texts with divergent characteristics. Digital Humanities (DH) as a field that has risen to prominence in the last decades, holds a variety of examples for this kind of texts. Thus, the computational analysis of the relationships of Shakespeare’s dramatic characters requires the adjustment of processing tools to English texts from the 16th-century in dramatic form. Likewise, the investigation of narrative perspective in Goethe’s ballads calls for methods that can handle German verse from the 18th century. In this dissertation, we put forward a methodology for NLP in a DH environment. We investigate how an interdisciplinary context in combination with specific goals within projects influences the general NLP approach. We suggest thoughtful collaboration and increased attention to the easy applicability of resulting tools as a solution for differences in the store of knowledge between project partners. Projects in DH are not only constituted by the automatic processing of texts but are usually framed by the investigation of a research question from the humanities. As a consequence, time limitations complicate the successful implementation of analysis techniques especially since the diversity of texts impairs the transferability and reusability of tools beyond a specific project. We answer to this with modular and thus easily adjustable project workflows and system architectures. Several instances serve as examples for our methodology on different levels. We discuss modular architectures that balance time-saving solutions and problem-specific implementations on the example of automatic postcorrection of the output text from an optical character recognition system. We address the problem of data diversity and low resource situations by investigating different approaches towards non-standard text processing. We examine two main techniques: text normalization and tool adjustment. Text normalization aims at the transformation of non-standard text in order to assimilate it to the standard whereas tool adjustment concentrates on the contrary direction of enabling tools to successfully handle a specific kind of text. We focus on the task of part-of-speech tagging to illustrate various approaches toward the processing of historical texts as an instance for non-standard texts. We discuss how the level of deviation from a standard form influences the performance of different methods. Our approaches shed light on the importance of data quality and quantity and emphasize the indispensability of annotations for effective machine learning. In addition, we highlight the advantages of problem-driven approaches where the purpose of a tool is clearly formulated through the research question. Another significant finding to emerge from this work is a summary of the experiences and increased knowledge through collaborative projects between computer scientists and humanists. We reflect on various aspects of the elaboration and formalization of research questions in the DH and assess the limitations and possibilities of the computational modeling of humanistic research questions. An emphasis is placed on the interplay of expert knowledge with respect to a subject of investigation and the implementation of tools for that purpose and the thereof resulting advantages such as the targeted improvement of digital methods through purposeful manual correction and error analysis. We show obstacles and chances and give prospects and directions for future development in this realm of interdisciplinary research