Automatic Genre Classification in Web Pages Applied to Web Comments

Automatic Web comment detection could significantly facilitate information retrieval systems, e.g., a focused Web crawler. In this paper, we propose a text genre classifier for Web text segments as intermediate step for Web comment detection in Web pages. Different feature types and classifiers are analyzed for this purpose. We compare the two-level approach to state-of-the-art techniques operating on the whole Web page text and show that accuracy can be improved significantly. Finally, we illustrate the applicability for information retrieval systems by evaluating our approach on Web pages achieved by a Web crawler

Coptic SCRIPTORIUM:A Corpus, Tools, and Methods for Corpus Linguistics and Computational Historical Research in Ancient Egypt

Coptic, having evolved from the language of the hieroglyphs of the pharaonic era, represents the last phase of the Egyptian language and is pivotal for a wide range of disciplines, such as linguistics, biblical studies, the history of Christianity, Egyptology, and ancient history. Coptic SCRIPTORIUM provides the first open-source technologies for computational and digital research across the disciplines as applied to Egyptian texts. The project is developing a digitized corpus of Coptic texts available in multiple formats and visualizations (including TEI XML), tools to analyze and process the language (e.g., the first Coptic part-of-speech tagger), a database with search and visualization capabilities, and a collaborative platform for scholars to contribute texts and annotations and to conduct research. The technologies and corpus will function as a collaborative environment for digital research by any scholars working in Coptic

Multi-Task sequence prediction for Tunisian Arabizi multi-level annotation

In this paper we propose a multi-task sequence prediction system, based on recurrent neural networks and used to annotate on multiple levels an Arabizi Tunisian corpus. The annotation performed are text classification, tokenization, PoS tagging and encoding of Tunisian Arabizi into CODA* Arabic orthography. The system is learned to predict all the annotation levels in cascade, starting from Arabizi input. We evaluate the system on the TIGER German corpus, suitably converting data to have a multi-task problem, in order to show the effectiveness of our neural architecture. We show also how we used the system in order to annotate a Tunisian Arabizi corpus, which has been afterwards manually corrected and used to further evaluate sequence models on Tunisian data. Our system is developed for the Fairseq framework, which allows for a fast and easy use for any other sequence prediction problem

Computational Sociolinguistics: A Survey

Language is a social phenomenon and variation is inherent to its social nature. Recently, there has been a surge of interest within the computational linguistics (CL) community in the social dimension of language. In this article we present a survey of the emerging field of "Computational Sociolinguistics" that reflects this increased interest. We aim to provide a comprehensive overview of CL research on sociolinguistic themes, featuring topics such as the relation between language and social identity, language use in social interaction and multilingual communication. Moreover, we demonstrate the potential for synergy between the research communities involved, by showing how the large-scale data-driven methods that are widely used in CL can complement existing sociolinguistic studies, and how sociolinguistics can inform and challenge the methods and assumptions employed in CL studies. We hope to convey the possible benefits of a closer collaboration between the two communities and conclude with a discussion of open challenges.Comment: To appear in Computational Linguistics. Accepted for publication: 18th February, 201

“You’re trolling because…” – A Corpus-based Study of Perceived Trolling and Motive Attribution in the Comment Threads of Three British Political Blogs

This paper investigates the linguistically marked motives that participants attribute to those they call trolls in 991 comment threads of three British political blogs. The study is concerned with how these motives affect the discursive construction of trolling and trolls. Another goal of the paper is to examine whether the mainly emotional motives ascribed to trolls in the academic literature correspond with those that the participants attribute to the alleged trolls in the analysed threads. The paper identifies five broad motives ascribed to trolls: emotional/mental health-related/social reasons, financial gain, political beliefs, being employed by a political body, and unspecified political affiliation. It also points out that depending on these motives, trolling and trolls are constructed in various ways. Finally, the study argues that participants attribute motives to trolls not only to explain their behaviour but also to insult them

Robust part-of-speech tagging of social media text

Part-of-Speech (PoS) tagging (Wortklassenerkennung) ist ein wichtiger Verarbeitungsschritt in vielen sprachverarbeitenden Anwendungen. Heute gibt es daher viele PoS Tagger, die diese wichtige Aufgabe automatisiert erledigen. Es hat sich gezeigt, dass PoS tagging auf informellen Texten oft nur mit unzureichender Genauigkeit möglich ist. Insbesondere Texte aus sozialen Medien sind eine große Herausforderung. Die erhöhte Fehlerrate, welche auf mangelnde Robustheit zurückgeführt werden kann, hat schwere Folgen für Anwendungen die auf PoS Informationen angewiesen sind. Diese Arbeit untersucht daher Tagger-Robustheit unter den drei Gesichtspunkten der (i) Domänenrobustheit, (ii) Sprachrobustheit und (iii) Robustheit gegenüber seltenen linguistischen Phänomene. Für (i) beginnen wir mit einer Analyse der Phänomene, die in informellen Texten häufig anzutreffen sind, aber in formalen Texten nur selten bis gar keine Verwendung finden. Damit schaffen wir einen Überblick über die Art der Phänomene die das Tagging von informellen Texten so schwierig machen. Wir evaluieren viele der üblicherweise benutzen Tagger für die englische und deutsche Sprache auf Texten aus verschiedenen Domänen, um einen umfassenden Überblick über die derzeitige Robustheit der verfügbaren Tagger zu bieten. Die Untersuchung ergab im Wesentlichen, dass alle Tagger auf informellen Texten große Schwächen zeigen. Methoden, um die Robustheit für domänenübergreifendes Tagging zu verbessern, sind prinzipiell hilfreich, lösen aber das grundlegende Robustheitsproblem nicht. Als neuen Lösungsansatz stellen wir Tagging in zwei Schritten vor, welches eine erhöhte Robustheit gegenüber domänenübergreifenden Tagging bietet. Im ersten Schritt wird nur grob-granular getaggt und im zweiten Schritt wird dieses Tagging dann auf das fein-granulare Level verfeinert. Für (ii) untersuchen wir Sprachrobustheit und ob jede Sprache einen zugeschnittenen Tagger benötigt, oder ob es möglich ist einen sprach-unabhängigen Tagger zu konstruieren, der für mehrere Sprachen funktioniert. Dazu vergleichen wir Tagger basierend auf verschiedenen Algorithmen auf 21 Sprachen und analysieren die notwendigen technischen Eigenschaften für einen Tagger, der auf mehreren Sprachen akkurate Modelle lernen kann. Die Untersuchung ergibt, dass Sprachrobustheit an für sich kein schwerwiegendes Problem ist und, dass die Tagsetgröße des Trainingskorpus ein wesentlich stärkerer Einflussfaktor für die Eignung eines Taggers ist als die Zugehörigkeit zu einer gewissen Sprache. Bezüglich (iii) untersuchen wir, wie man mit seltenen Phänomenen umgehen kann, für die nicht genug Trainingsdaten verfügbar sind. Dazu stellen wir eine neue kostengünstige Methode vor, die nur einen minimalen Aufwand an manueller Annotation erwartet, um zusätzliche Daten für solche seltenen Phänomene zu produzieren. Ein Feldversuch hat gezeigt, dass die produzierten Daten ausreichen um das Tagging von seltenen Phänomenen deutlich zu verbessern. Abschließend präsentieren wir zwei Software-Werkzeuge, FlexTag und DeepTC, die wir im Rahmen dieser Arbeit entwickelt haben. Diese Werkzeuge bieten die notwendige Flexibilität und Reproduzierbarkeit für die Experimente in dieser Arbeit.Part-of-speech (PoS) taggers are an important processing component in many Natural Language Processing (NLP) applications, which led to a variety of taggers for tackling this task. Recent work in this field showed that tagging accuracy on informal text domains is poor in comparison to formal text domains. In particular, social media text, which is inherently different from formal standard text, leads to a drastically increased error rate. These arising challenges originate in a lack of robustness of taggers towards domain transfers. This increased error rate has an impact on NLP applications that depend on PoS information. The main contribution of this thesis is the exploration of the concept of robustness under the following three aspects: (i) domain robustness, (ii) language robustness and (iii) long tail robustness. Regarding (i), we start with an analysis of the phenomena found in informal text that make tagging this kind of text challenging. Furthermore, we conduct a comprehensive robustness comparison of many commonly used taggers for English and German by evaluating them on the text of several text domains. We find that the tagging of informal text is poorly supported by available taggers. A review and analysis of currently used methods to adapt taggers to informal text showed that these methods improve tagging accuracy but offer no satisfactory solution. We propose an alternative tagging approach that reaches an increased multi-domain tagging robustness. This approach is based on tagging in two steps. The first step tags on a coarse-grained level and the second step refines the tags to the fine-grained tags. Regarding (ii), we investigate whether each language requires a language-tailored PoS tagger or if the construction of a competitive language independent tagger is feasible. We explore the technical details that contribute to a tagger's language robustness by comparing taggers based on different algorithms to learn models of 21 languages. We find that language robustness is a less severe issue and that the impact of the tagger choice depends more on the granularity of the tagset that shall be learned than on the language. Regarding (iii), we investigate methods to improve tagging of infrequent phenomena of which no sufficient amount of annotated training data is available, which is a common challenge in the social media domain. We propose a new method to overcome this lack of data that offers an inexpensive way of producing more training data. In a field study, we show that the quality of the produced data suffices to train tagger models that can recognize these under-represented phenomena. Furthermore, we present two software tools, FlexTag and DeepTC, which we developed in the course of this thesis. These tools provide the necessary flexibility for conducting all the experiments in this thesis and ensure their reproducibility