What is SemEval evaluating?: A Systematic Analysis of Evaluation Campaigns in NLP

SemEval is the primary venue in the NLP community for the proposal of new challenges and for the systematic empirical evaluation of NLP systems. This paper provides a systematic quantitative analysis of SemEval aiming to evidence the patterns of the contributions behind SemEval. By understanding the distribution of task types, metrics, architectures, participation and citations over time we aim to answer the question on what is being evaluated by SemEval.Comment: 12 pages, 6 figure

Recognizing Emotions in Short Texts

Tese de mestrado, Ciência Cognitiva, Universidade de Lisboa, Faculdade de Ciências, 2022O reconhecimento automático de emoções em texto é uma tarefa que mobiliza as áreas de processamento de linguagem natural e de computação afetiva, para as quais se pode contar com o especial contributo de disciplinas da Ciência Cognitiva como Inteligência Artificial e Ciência da Computação, Linguística e Psicologia. Visa, sobretudo, a deteção e interpretação de emoções humanas através da sua expressão na forma escrita por sistemas computacionais. A interação entre processos afetivos e cognitivos, o papel essencial que as emoções desempenham nas interações interpessoais e a crescente utilização de comunicação escrita online nos dias de hoje fazem com que o reconhecimento de emoções de forma automática seja cada vez mais importante, nomeadamente em áreas como saúde mental, interação pessoa-computador, ciência política ou marketing. A língua inglesa tem sido o maior alvo de estudo no que diz respeito ao reconhecimento de emoções em textos, sendo que ainda existe pouco trabalho desenvolvido para a língua portuguesa. Assim, existe uma necessidade em expandir o trabalho feito para a língua inglesa para o português. Esta dissertação tem como objetivo a comparação de dois métodos distintos de aprendizagem profunda resultantes dos avanços na área de Inteligência Artificial para detetar e classificar de forma automática estados emocionais discretos em textos escritos em língua portuguesa. Para tal, a abordagem de classificação de Polignano et al. (2019) baseada em redes de aprendizagem profunda como Long Short-Term Memory bidirecionais e redes convolucionais mediadas por um mecanismo de atenção será replicada para a língua inglesa e será reproduzida para a língua portuguesa. Para a língua inglesa, será utilizado o conjunto de dados da tarefa 1 do SemEval-2018 (Mohammad et al., 2018) tal como na experiência original, que considera quatro emoções discretas: raiva, medo, alegria e tristeza. Para a língua portuguesa, tendo em consideração a falta de conjuntos de dados disponíveis anotados relativamente a emoções, será efetuada uma recolha de dados a partir da rede social Twitter recorrendo a hashtags com conteúdo associado a uma emoção específica para determinar a emoção subjacente ao texto de entre as mesmas quatro emoções presentes no conjunto de dados da língua inglesa que será utilizado. De acordo com experiências realizadas por Mohammad & Kiritchenko (2015), este método de recolha de dados é consistente com a anotação de juízes humanos treinados. Tendo em conta a rápida e contínua evolução dos métodos de aprendizagem profunda para o processamento de linguagem natural e o estado da arte estabelecido por métodos recentes em tarefas desta área tal como o modelo pré-treinado BERT (Bidirectional Encoder Representations from Tranformers) (Devlin et al., 2019), será também aplicada esta abordagem para a tarefa de reconhecimento de emoções para as duas línguas em questão, utilizando os mesmos conjuntos de dados das experiências anteriores. Enquanto a abordagem de Polignano et al. teve um melhor desempenho nas experiências que realizámos com dados em inglês, com diferenças de F1-score de 0.02, o melhor resultado obtido nas experiências com dados na língua portuguesa foi com o modelo BERT, obtendo um resultado máximo de F1-score de 0.6124.Automatic emotion recognition from text is a task that mobilizes the areas of natural language processing and affective computing counting with the special contribution of Cognitive Science subjects such as Artificial Intelligence and Computer Science, Linguistics and Psychology. It aims at the detection and interpretation of human emotions expressed in the written form by computational systems. The interaction of affective and cognitive processes, the essential role that emotions play in interpersonal interactions and the currently increasing use of written communication online make automatic emotion recognition progressively important, namely in areas such as mental healthcare, human-computer interaction, political science, or marketing. The English language has been the main target of studies in emotion recognition in text and the work developed for the Portuguese language is still scarce. Thus, there is a need to expand the work developed for English to Portuguese. The goal of this dissertation is to present and compare two distinct deep learning methods resulting from the advances in Artificial Intelligence to automatically detect and classify discrete emotional states in texts written in Portuguese. For this, the classification approach of Polignano et al. (2019) based on deep learning networks such as bidirectional Long Short-Term Memory and convolutional networks mediated by a self-attention level will be replicated for English and it will be reproduced for Portuguese. For English, the SemEval-2018 task 1 dataset (Mohammad et al., 2018) will be used, as in the original experience, and it considers four discrete emotions: anger, fear, joy, and sadness. For Portuguese, considering the lack of available emotionally annotated datasets, data will be collected from the social network Twitter using hashtags associated to a specific emotional content to determine the underlying emotion of the text from the same four emotions present in the English dataset. According to experiments carried out by Mohammad & Kiritchenko (2015), this method of data collection is consistent with the annotation of trained human judges. Considering the fast and continuous evolution of deep learning methods for natural language processing and the state-of-the-art results achieved by recent methods in tasks in this area such as the pre-trained language model BERT (Bidirectional Encoder Representations from Transformers) (Devlin et al., 2019), this approach will also be applied to the task of emotion recognition for both languages using the same datasets from the previous experiments. It is expected to draw conclusions about the adequacy of these two presented approaches in emotion recognition and to contribute to the state of the art in this task for the Portuguese language. While the approach of Polignano et al. had a better performance in the experiments with English data with a difference in F1 scores of 0.02, for Portuguese we obtained the best result with BERT having a maximum F1 score of 0.6124

Sentiment Analysis in Digital Spaces: An Overview of Reviews

Sentiment analysis (SA) is commonly applied to digital textual data, revealing insight into opinions and feelings. Many systematic reviews have summarized existing work, but often overlook discussions of validity and scientific practices. Here, we present an overview of reviews, synthesizing 38 systematic reviews, containing 2,275 primary studies. We devise a bespoke quality assessment framework designed to assess the rigor and quality of systematic review methodologies and reporting standards. Our findings show diverse applications and methods, limited reporting rigor, and challenges over time. We discuss how future research and practitioners can address these issues and highlight their importance across numerous applications.Comment: 44 pages, 4 figures, 6 tables, 3 appendice

Biased Embeddings from Wild Data: Measuring, Understanding and Removing

Many modern Artificial Intelligence (AI) systems make use of data embeddings, particularly in the domain of Natural Language Processing (NLP). These embeddings are learnt from data that has been gathered "from the wild" and have been found to contain unwanted biases. In this paper we make three contributions towards measuring, understanding and removing this problem. We present a rigorous way to measure some of these biases, based on the use of word lists created for social psychology applications; we observe how gender bias in occupations reflects actual gender bias in the same occupations in the real world; and finally we demonstrate how a simple projection can significantly reduce the effects of embedding bias. All this is part of an ongoing effort to understand how trust can be built into AI systems.Comment: Author's original versio

Natural Language Processing using Deep Learning in Social Media

[ES] En los últimos años, los modelos de aprendizaje automático profundo (AP) han revolucionado los sistemas de procesamiento de lenguaje natural (PLN). Hemos sido testigos de un avance formidable en las capacidades de estos sistemas y actualmente podemos encontrar sistemas que integran modelos PLN de manera ubicua. Algunos ejemplos de estos modelos con los que interaccionamos a diario incluyen modelos que determinan la intención de la persona que escribió un texto, el sentimiento que pretende comunicar un tweet o nuestra ideología política a partir de lo que compartimos en redes sociales. En esta tesis se han propuestos distintos modelos de PNL que abordan tareas que estudian el texto que se comparte en redes sociales. En concreto, este trabajo se centra en dos tareas fundamentalmente: el análisis de sentimientos y el reconocimiento de la personalidad de la persona autora de un texto. La tarea de analizar el sentimiento expresado en un texto es uno de los problemas principales en el PNL y consiste en determinar la polaridad que un texto pretende comunicar. Se trata por lo tanto de una tarea estudiada en profundidad de la cual disponemos de una vasta cantidad de recursos y modelos. Por el contrario, el problema del reconocimiento de personalidad es una tarea revolucionaria que tiene como objetivo determinar la personalidad de los usuarios considerando su estilo de escritura. El estudio de esta tarea es más marginal por lo que disponemos de menos recursos para abordarla pero que no obstante presenta un gran potencial. A pesar de que el enfoque principal de este trabajo fue el desarrollo de modelos de aprendizaje profundo, también hemos propuesto modelos basados en recursos lingüísticos y modelos clásicos del aprendizaje automático. Estos últimos modelos nos han permitido explorar las sutilezas de distintos elementos lingüísticos como por ejemplo el impacto que tienen las emociones en la clasificación correcta del sentimiento expresado en un texto. Posteriormente, tras estos trabajos iniciales se desarrollaron modelos AP, en particular, Redes neuronales convolucionales (RNC) que fueron aplicadas a las tareas previamente citadas. En el caso del reconocimiento de la personalidad, se han comparado modelos clásicos del aprendizaje automático con modelos de aprendizaje profundo, pudiendo establecer una comparativa bajo las mismas premisas. Cabe destacar que el PNL ha evolucionado drásticamente en los últimos años gracias al desarrollo de campañas de evaluación pública, donde múltiples equipos de investigación comparan las capacidades de los modelos que proponen en las mismas condiciones. La mayoría de los modelos presentados en esta tesis fueron o bien evaluados mediante campañas de evaluación públicas, o bien emplearon la configuración de una campaña pública previamente celebrada. Siendo conscientes, por lo tanto, de la importancia de estas campañas para el avance del PNL, desarrollamos una campaña de evaluación pública cuyo objetivo era clasificar el tema tratado en un tweet, para lo cual recogimos y etiquetamos un nuevo conjunto de datos. A medida que avanzabamos en el desarrollo del trabajo de esta tesis, decidimos estudiar en profundidad como las RNC se aplicaban a las tareas de PNL. En este sentido, se exploraron dos líneas de trabajo. En primer lugar, propusimos un método de relleno semántico para RNC, que plantea una nueva manera de representar el texto para resolver tareas de PNL. Y en segundo lugar, se introdujo un marco teórico para abordar una de las críticas más frecuentes del aprendizaje profundo, el cual es la falta de interpretabilidad. Este marco busca visualizar qué patrones léxicos, si los hay, han sido aprendidos por la red para clasificar un texto.[CA] En els últims anys, els models d'aprenentatge automàtic profund (AP) han revolucionat els sistemes de processament de llenguatge natural (PLN). Hem estat testimonis d'un avanç formidable en les capacitats d'aquests sistemes i actualment podem trobar sistemes que integren models PLN de manera ubiqua. Alguns exemples d'aquests models amb els quals interaccionem diàriament inclouen models que determinen la intenció de la persona que va escriure un text, el sentiment que pretén comunicar un tweet o la nostra ideologia política a partir del que compartim en xarxes socials. En aquesta tesi s'han proposats diferents models de PNL que aborden tasques que estudien el text que es comparteix en xarxes socials. En concret, aquest treball se centra en dues tasques fonamentalment: l'anàlisi de sentiments i el reconeixement de la personalitat de la persona autora d'un text. La tasca d'analitzar el sentiment expressat en un text és un dels problemes principals en el PNL i consisteix a determinar la polaritat que un text pretén comunicar. Es tracta per tant d'una tasca estudiada en profunditat de la qual disposem d'una vasta quantitat de recursos i models. Per contra, el problema del reconeixement de la personalitat és una tasca revolucionària que té com a objectiu determinar la personalitat dels usuaris considerant el seu estil d'escriptura. L'estudi d'aquesta tasca és més marginal i en conseqüència disposem de menys recursos per abordar-la però no obstant i això presenta un gran potencial. Tot i que el fouc principal d'aquest treball va ser el desenvolupament de models d'aprenentatge profund, també hem proposat models basats en recursos lingüístics i models clàssics de l'aprenentatge automàtic. Aquests últims models ens han permès explorar les subtileses de diferents elements lingüístics com ara l'impacte que tenen les emocions en la classificació correcta del sentiment expressat en un text. Posteriorment, després d'aquests treballs inicials es van desenvolupar models AP, en particular, Xarxes neuronals convolucionals (XNC) que van ser aplicades a les tasques prèviament esmentades. En el cas de el reconeixement de la personalitat, s'han comparat models clàssics de l'aprenentatge automàtic amb models d'aprenentatge profund la qual cosa a permet establir una comparativa de les dos aproximacions sota les mateixes premisses. Cal remarcar que el PNL ha evolucionat dràsticament en els últims anys gràcies a el desenvolupament de campanyes d'avaluació pública on múltiples equips d'investigació comparen les capacitats dels models que proposen sota les mateixes condicions. La majoria dels models presentats en aquesta tesi van ser o bé avaluats mitjançant campanyes d'avaluació públiques, o bé s'ha emprat la configuració d'una campanya pública prèviament celebrada. Sent conscients, per tant, de la importància d'aquestes campanyes per a l'avanç del PNL, vam desenvolupar una campanya d'avaluació pública on l'objectiu era classificar el tema tractat en un tweet, per a la qual cosa vam recollir i etiquetar un nou conjunt de dades. A mesura que avançàvem en el desenvolupament del treball d'aquesta tesi, vam decidir estudiar en profunditat com les XNC s'apliquen a les tasques de PNL. En aquest sentit, es van explorar dues línies de treball.En primer lloc, vam proposar un mètode d'emplenament semàntic per RNC, que planteja una nova manera de representar el text per resoldre tasques de PNL. I en segon lloc, es va introduir un marc teòric per abordar una de les crítiques més freqüents de l'aprenentatge profund, el qual és la falta de interpretabilitat. Aquest marc cerca visualitzar quins patrons lèxics, si n'hi han, han estat apresos per la xarxa per classificar un text.[EN] In the last years, Deep Learning (DL) has revolutionised the potential of automatic systems that handle Natural Language Processing (NLP) tasks. We have witnessed a tremendous advance in the performance of these systems. Nowadays, we found embedded systems ubiquitously, determining the intent of the text we write, the sentiment of our tweets or our political views, for citing some examples. In this thesis, we proposed several NLP models for addressing tasks that deal with social media text. Concretely, this work is focused mainly on Sentiment Analysis and Personality Recognition tasks. Sentiment Analysis is one of the leading problems in NLP, consists of determining the polarity of a text, and it is a well-known task where the number of resources and models proposed is vast. In contrast, Personality Recognition is a breakthrough task that aims to determine the users' personality using their writing style, but it is more a niche task with fewer resources designed ad-hoc but with great potential. Despite the fact that the principal focus of this work was on the development of Deep Learning models, we have also proposed models based on linguistic resources and classical Machine Learning models. Moreover, in this more straightforward setup, we have explored the nuances of different language devices, such as the impact of emotions in the correct classification of the sentiment expressed in a text. Afterwards, DL models were developed, particularly Convolutional Neural Networks (CNNs), to address previously described tasks. In the case of Personality Recognition, we explored the two approaches, which allowed us to compare the models under the same circumstances. Noteworthy, NLP has evolved dramatically in the last years through the development of public evaluation campaigns, where multiple research teams compare the performance of their approaches under the same conditions. Most of the models here presented were either assessed in an evaluation task or either used their setup. Recognising the importance of this effort, we curated and developed an evaluation campaign for classifying political tweets. In addition, as we advanced in the development of this work, we decided to study in-depth CNNs applied to NLP tasks. Two lines of work were explored in this regard. Firstly, we proposed a semantic-based padding method for CNNs, which addresses how to represent text more appropriately for solving NLP tasks. Secondly, a theoretical framework was introduced for tackling one of the most frequent critics of Deep Learning: interpretability. This framework seeks to visualise what lexical patterns, if any, the CNN is learning in order to classify a sentence. In summary, the main achievements presented in this thesis are: - The organisation of an evaluation campaign for Topic Classification from texts gathered from social media. - The proposal of several Machine Learning models tackling the Sentiment Analysis task from social media. Besides, a study of the impact of linguistic devices such as figurative language in the task is presented. - The development of a model for inferring the personality of a developer provided the source code that they have written. - The study of Personality Recognition tasks from social media following two different approaches, models based on machine learning algorithms and handcrafted features, and models based on CNNs were proposed and compared both approaches. - The introduction of new semantic-based paddings for optimising how the text was represented in CNNs. - The definition of a theoretical framework to provide interpretable information to what CNNs were learning internally.Giménez Fayos, MT. (2021). Natural Language Processing using Deep Learning in Social Media [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/172164TESI

ConStance: Modeling Annotation Contexts to Improve Stance Classification

Manual annotations are a prerequisite for many applications of machine learning. However, weaknesses in the annotation process itself are easy to overlook. In particular, scholars often choose what information to give to annotators without examining these decisions empirically. For subjective tasks such as sentiment analysis, sarcasm, and stance detection, such choices can impact results. Here, for the task of political stance detection on Twitter, we show that providing too little context can result in noisy and uncertain annotations, whereas providing too strong a context may cause it to outweigh other signals. To characterize and reduce these biases, we develop ConStance, a general model for reasoning about annotations across information conditions. Given conflicting labels produced by multiple annotators seeing the same instances with different contexts, ConStance simultaneously estimates gold standard labels and also learns a classifier for new instances. We show that the classifier learned by ConStance outperforms a variety of baselines at predicting political stance, while the model's interpretable parameters shed light on the effects of each context.Comment: To appear at EMNLP 201

Your Stance is Exposed! Analysing Possible Factors for Stance Detection on Social Media

To what extent user's stance towards a given topic could be inferred? Most of the studies on stance detection have focused on analysing user's posts on a given topic to predict the stance. However, the stance in social media can be inferred from a mixture of signals that might reflect user's beliefs including posts and online interactions. This paper examines various online features of users to detect their stance towards different topics. We compare multiple set of features, including on-topic content, network interactions, user's preferences, and online network connections. Our objective is to understand the online signals that can reveal the users' stance. Experimentation is applied on tweets dataset from the SemEval stance detection task, which covers five topics. Results show that stance of a user can be detected with multiple signals of user's online activity, including their posts on the topic, the network they interact with or follow, the websites they visit, and the content they like. The performance of the stance modelling using different network features are comparable with the state-of-the-art reported model that used textual content only. In addition, combining network and content features leads to the highest reported performance to date on the SemEval dataset with F-measure of 72.49%. We further present an extensive analysis to show how these different set of features can reveal stance. Our findings have distinct privacy implications, where they highlight that stance is strongly embedded in user's online social network that, in principle, individuals can be profiled from their interactions and connections even when they do not post about the topic.Comment: Accepted as a full paper at CSCW 2019. Please cite the CSCW versio