Exploring part-of-speech frequencies in a sociohistorical corpus of English

Peer reviewe

“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

Improved Coreference Resolution Using Cognitive Insights

Coreference resolution is the task of extracting referential expressions, or mentions, in text and clustering these by the entity or concept they refer to. The sustained research interest in the task reflects the richness of reference expression usage in natural language and the difficulty in encoding insights from linguistic and cognitive theories effectively. In this thesis, we design and implement LIMERIC, a state-of-the-art coreference resolution engine. LIMERIC naturally incorporates both non-local decoding and entity-level modelling to achieve the highly competitive benchmark performance of 64.22% and 59.99% on the CoNLL-2012 benchmark with a simple model and a baseline feature set. As well as strong performance, a key contribution of this work is a reconceptualisation of the coreference task. We draw an analogy between shift-reduce parsing and coreference resolution to develop an algorithm which naturally mimics cognitive models of human discourse processing. In our feature development work, we leverage insights from cognitive theories to improve our modelling. Each contribution achieves statistically significant improvements and sum to gains of 1.65% and 1.66% on the CoNLL-2012 benchmark, yielding performance values of 65.76% and 61.27%. For each novel feature we propose, we contribute an accompanying analysis so as to better understand how cognitive theories apply to real language data. LIMERIC is at once a platform for exploring cognitive insights into coreference and a viable alternative to current systems. We are excited by the promise of incorporating our and further cognitive insights into more complex frameworks since this has the potential to both improve the performance of computational models, as well as our understanding of the mechanisms underpinning human reference resolution

Proceedings

Proceedings of the Workshop on Annotation and Exploitation of Parallel Corpora AEPC 2010. Editors: Lars Ahrenberg, Jörg Tiedemann and Martin Volk. NEALT Proceedings Series, Vol. 10 (2010), 98 pages. © 2010 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/15893

LORE: a model for the detection of fine-grained locative references in tweets

[EN] Extracting geospatially rich knowledge from tweets is of utmost importance for location-based systems in emergency services to raise situational awareness about a given crisis-related incident, such as earthquakes, floods, car accidents, terrorist attacks, shooting attacks, etc. The problem is that the majority of tweets are not geotagged, so we need to resort to the messages in the search of geospatial evidence. In this context, we present LORE, a location-detection system for tweets that leverages the geographic database GeoNames together with linguistic knowledge through NLP techniques. One of the main contributions of this model is to capture fine-grained complex locative references, ranging from geopolitical entities and natural geographic references to points of interest and traffic ways. LORE outperforms state-of-the-art open-source location-extraction systems (i.e. Stanford NER, spaCy, NLTK and OpenNLP), achieving an unprecedented trade-off between precision and recall. Therefore, our model provides not only a quantitative advantage over other well-known systems in terms of performance but also a qualitative advantage in terms of the diversity and semantic granularity of the locative references extracted from the tweets.Financial support for this research has been provided by the Spanish Ministry of Science, Innovation and Universities [grant number RTC 2017-6389-5], and the European Union's Horizon 2020 research and innovation program [grant number 101017861: project SMARTLAGOON]. We also thank Universidad de Granada for their financial support to the first author through the Becas de Iniciacion para estudiantes de Master 2018 del Plan Propio de la UGR.Fernández-Martínez, NJ.; Periñán-Pascual, C. (2021). LORE: a model for the detection of fine-grained locative references in tweets. Onomázein. (52):195-225. https://doi.org/10.7764/onomazein.52.111952255

Linguistic-based Patterns for Figurative Language Processing: The Case of Humor Recognition and Irony Detection

El lenguaje figurado representa una de las tareas más difíciles del procesamiento del lenguaje natural. A diferencia del lenguaje literal, el lenguaje figurado hace uso de recursos lingüísticos tales como la ironía, el humor, el sarcasmo, la metáfora, la analogía, entre otros, para comunicar significados indirectos que la mayoría de las veces no son interpretables sólo en términos de información sintáctica o semántica. Por el contrario, el lenguaje figurado refleja patrones del pensamiento que adquieren significado pleno en contextos comunicativos y sociales, lo cual hace que tanto su representación lingüística, así como su procesamiento computacional, se vuelvan tareas por demás complejas. En este contexto, en esta tesis de doctorado se aborda una problemática relacionada con el procesamiento del lenguaje figurado a partir de patrones lingüísticos. En particular, nuestros esfuerzos se centran en la creación de un sistema capaz de detectar automáticamente instancias de humor e ironía en textos extraídos de medios sociales. Nuestra hipótesis principal se basa en la premisa de que el lenguaje refleja patrones de conceptualización; es decir, al estudiar el lenguaje, estudiamos tales patrones. Por tanto, al analizar estos dos dominios del lenguaje figurado, pretendemos dar argumentos respecto a cómo la gente los concibe, y sobre todo, a cómo esa concepción hace que tanto humor como ironía sean verbalizados de una forma particular en diversos medios sociales. En este contexto, uno de nuestros mayores intereses es demostrar cómo el conocimiento que proviene del análisis de diferentes niveles de estudio lingüístico puede representar un conjunto de patrones relevantes para identificar automáticamente usos figurados del lenguaje. Cabe destacar que contrario a la mayoría de aproximaciones que se han enfocado en el estudio del lenguaje figurado, en nuestra investigación no buscamos dar argumentos basados únicamente en ejemplos prototípicos, sino en textos cuyas característicasReyes Pérez, A. (2012). Linguistic-based Patterns for Figurative Language Processing: The Case of Humor Recognition and Irony Detection [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/16692Palanci

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