Exploiting Class Labels to Boost Performance on Embedding-based Text Classification

Text classification is one of the most frequent tasks for processing textual data, facilitating among others research from large-scale datasets. Embeddings of different kinds have recently become the de facto standard as features used for text classification. These embeddings have the capacity to capture meanings of words inferred from occurrences in large external collections. While they are built out of external collections, they are unaware of the distributional characteristics of words in the classification dataset at hand, including most importantly the distribution of words across classes in training data. To make the most of these embeddings as features and to boost the performance of classifiers using them, we introduce a weighting scheme, Term Frequency-Category Ratio (TF-CR), which can weight high-frequency, category-exclusive words higher when computing word embeddings. Our experiments on eight datasets show the effectiveness of TF-CR, leading to improved performance scores over the well-known weighting schemes TF-IDF and KLD as well as over the absence of a weighting scheme in most cases.Comment: CIKM 202

Leveraging aspect phrase embeddings for cross-domain review rating prediction

Online review platforms are a popular way for users to post reviews by expressing their opinions towards a product or service, and they are valuable for other users and companies to find out the overall opinions of customers. These reviews tend to be accompanied by a rating, where the star rating has become the most common approach for users to give their feedback in a quantitative way, generally as a Likert scale of 1–5 stars. In other social media platforms like Facebook or Twitter, an automated review rating prediction system can be useful to determine the rating that a user would have given to the product or service. Existing work on review rating prediction focuses on specific domains, such as restaurants or hotels. This, however, ignores the fact that some review domains which are less frequently rated, such as dentists, lack sufficient data to build a reliable prediction model. In this paper, we experiment on 12 datasets pertaining to 12 different review domains of varying level of popularity to assess the performance of predictions across different domains. We introduce a model that leverages aspect phrase embeddings extracted from the reviews, which enables the development of both in-domain and cross-domain review rating prediction systems. Our experiments show that both of our review rating prediction systems outperform all other baselines. The cross-domain review rating prediction system is particularly significant for the least popular review domains, where leveraging training data from other domains leads to remarkable improvements in performance. The in-domain review rating prediction system is instead more suitable for popular review domains, provided that a model built from training data pertaining to the target domain is more suitable when this data is abundant

Tackling Sexist Hate Speech: Cross-Lingual Detection and Multilingual Insights from Social Media

With the widespread use of social media, the proliferation of online communication presents both opportunities and challenges for fostering a respectful and inclusive digital environment. Due to the anonymity and weak regulations of social media platforms, the rise of hate speech has become a significant concern, particularly against specific individuals or groups based on race, religion, ethnicity, or gender, posing a severe threat to human rights. Sexist hate speech is a prevalent form of online hate that often manifests itself through gender-based violence and discrimination, challenging societal norms and legal systems. Despite the advances in natural language processing techniques for detecting offensive and sexist content, most research still focuses on monolingual (primarily English) contexts, neglecting the multilingual nature of online platforms. This gap highlights the need for effective and scalable strategies to address the linguistic diversity and cultural variations in hate speech. Cross-language transfer learning and state-of-the-art multilingual pre-trained language models provide potential solutions to improve the detection efficiency of low-resource languages by leveraging data from high-resource languages. Additional knowledge is crucial to facilitate the models’ performance in detecting culturally varying expressions of sexist hate speech in different languages. In this thesis, we delve into the complex area of identifying sexist hate speech in social media across diverse languages pertaining to different language families, with a focus on sexism and a broad exploration of datasets, methodologies, and barriers inherent in mitigating online hate speech in cross-lingual and multilingual scenarios. We primarily apply cross-lingual transfer learning techniques to detect sexist hate speech, aiming to leverage knowledge acquired from related linguistic data in order to improve performance in a target language. We also investigate the integration of external knowledge to deepen the understanding of sexism in multilingual social media contexts, addressing both the challenges of linguistic diversity and the need for comprehensive, culturally sensitive hate speech detection models. Specifically, it embarks on a comprehensive survey of tackling cross-lingual hate speech online, summarising existing datasets and cross-lingual approaches, as well as highlighting challenges and frontiers in this field. It then presents a first contribution to the field, the creation of the Sina Weibo Sexism Review (Swsr) dataset in Chinese —a pioneering resource that not only fills a crucial gap in limited resources but also lays the foundation for relevant cross-lingual investigations. Additionally, it examines how cross-lingual techniques can be utilised to generate domain-aware word embeddings, and explores the application of these embeddings in a cross-lingual hate speech framework, thereby enhancing the capacity to capture the subtleties of sexist hate speech across diverse languages. Recognising the significance of linguistic nuances in multilingual and cross-lingual settings, another innovation consists in proposing and evaluating a series of multilingual and cross-lingual models tailored for detecting sexist hate speech. By leveraging the capacity of shared knowledge and features across languages, these models significantly advance the state-of-the-art in identifying online sexist hate speech. As societies continue to deal with the complexities of social media, the findings and methodologies presented in this thesis could effectively help foster more inclusive and respectful online content across languages