Hate speech against individuals and groups with certain demographics is a major issue in social media. Supervised models for hate speech detection mostly utilize labeled data collections to understand textual semantics. However, hate speech detection is a complex task that involves several aspects, including topic and writing style. The complexity of hate speech can be represented by an ensemble of models learned from different aspects of data. Moreover, ensemble members or base models can be modified to give attention to particular aspects of hate speech. In this study, we extract different aspects of hate speech to construct ensembles, thereby improving the performance of hate speech detection by ensemble learning. We conduct detailed experiments on five datasets in multiple languages to generalize our observations. The experimental results, supported by statistical significance tests, show that the performance of hate speech detection can be improved by capturing multiple aspects of hate speech. Our ensemble construction approach outperforms the baselines in terms of the F1 score of the Hate class in 80% of the cases, and the Offensive class in 75% of the cases. We also compare our approach with state-of-the-art ensemble methods from shared tasks and find that our highest-performing method can improve the performance of the Hate class in two out of three datasets. We further discuss our approach and experimental results in terms of ensemble parameters and writing style among ensemble members.

The exponential growth of social media data in the era of Web 2.0 has necessitated advanced techniques for sentiment analysis. While sentiment analysis in monolingual datasets has received significant attention that in code-mixed datasets still need to be studied more. Code-mixed data often contain a mixture of monolingual content (might be in transliterated form), single-script but multilingual content, and multi-script multilingual content. This paper explores the issue from three important angles. What will be the best strategy to deal with the data for sentiment detection? Whether to train the classifier with the whole of the dataset or only with the pure code-mixed subset from the dataset? How much important is the language identification (LID) for the task? If LID is to be done, how, and when will it be used to yield the best performance? We explore the questions in the light of three datasets of Tamil–English, Kannada–English, and Malayalam–English YouTube social media comments. Our solution incorporated mBERT and an optional LID module. We report our results using a set of metrics like precision, recall, $F_1$ score, and accuracy. The solutions provide considerable performance gain and some interesting insights for sentiment analysis from code-mixed data.

With recent advances in natural language generation, risks associated with the rapid proliferation and misuse of generative language models for malicious purposes steadily increase. Artificial text detection (ATD) has emerged to develop resources and computational methods to mitigate these risks, such as generating fake news and scientific article reviews. This paper introduces corpus of artificial texts (CoAT), a large-scale corpus of human-written and generated texts for the Russian language. CoAT spans six domains and comprises outputs from 13 text generation models (TGMs), which differ in the number of parameters, architectural choices, pre-training objectives, and downstream applications. We detail the data collection methodology, conduct a linguistic analysis of the corpus, and present a detailed analysis of the ATD experiments with widely used artificial text detectors. The results demonstrate that the detectors perform well on the seen TGMs, but fail to generalise to unseen TGMs and domains. We also find it challenging to identify the author of the given text, and human annotators significantly underperform the detectors. We release CoAT, the codebase, two ATD leaderboards, and other materials used in the paper.

The OffensEval shared tasks organized as part of SemEval-2019–2020 were very popular, attracting over 1300 participating teams. The two editions of the shared task helped advance the state of the art in offensive language identification by providing the community with benchmark datasets in Arabic, Danish, English, Greek, and Turkish. The datasets were annotated using the OLID hierarchical taxonomy, which since then has become the de facto standard in general offensive language identification research and was widely used beyond OffensEval. We present a survey of OffensEval and related competitions, and we discuss the main lessons learned. We further evaluate the performance of Large Language Models (LLMs), which have recently revolutionalized the field of Natural Language Processing. We use zero-shot prompting with six popular LLMs and zero-shot learning with two task-specific fine-tuned BERT models, and we compare the results against those of the top-performing teams at the OffensEval competitions. Our results show that while some LMMs such as Flan-T5 achieve competitive performance, in general LLMs lag behind the best OffensEval systems.

GPT-3 is a large-scale natural language model developed by OpenAI that can perform many different tasks, including topic classification. Although researchers claim that it requires only a small number of in-context examples to learn a task, in practice GPT-3 requires these training examples to be either of exceptional quality or a higher quantity than easily created by hand. To address this issue, this study teaches GPT-3 to classify whether a question is related to data science by augmenting a small training set with additional examples generated by GPT-3 itself. This study compares two augmented classifiers: the Classification Endpoint with an increased training set size and the Completion Endpoint with an augmented prompt optimized using a genetic algorithm. We find that data augmentation significantly increases the accuracy of both classifiers, and that the embedding-based Classification Endpoint achieves the best accuracy of about 76%, compared to human accuracy of 85%. In this way, giving large language models like GPT-3 the ability to propose their own training examples can improve short text classification performance.

Recent developments in text style transfer have led this field to be more highlighted than ever. There are many challenges associated with transferring the style of input text such as fluency and content preservation that need to be addressed. In this research, we present PGST, a novel Persian text style transfer approach in the gender domain, composed of different constituent elements. Established on the significance of parts of speech tags, our method is the first that successfully transfers the gendered linguistic style of Persian text. We have proceeded with a pre-trained word embedding for token replacement purposes, a character-based token classifier for gender exchange purposes, and a beam search algorithm for extracting the most fluent combination. Since different approaches are introduced in our research, we determine a trade-off value for evaluating different models’ success in faking our gender identification model with transferred text. Our research focuses primarily on Persian, but since there is no Persian baseline available, we applied our method to a highly studied gender-tagged English corpus and compared it to state-of-the-art English variants to demonstrate its applicability. Our final approach successfully defeated English and Persian gender identification models by 45.6% and 39.2%, respectively.

A competitive and cost-effective public procurement (PP) process is essential for the effective use of public resources. In this work, we explore whether descriptions of procurement calls can be used to predict their outcomes. In particular, we focus on predicting four well-known economic metrics: (i) the number of offers, (ii) whether only a single offer is received, (iii) whether a foreign firm is awarded the contract, and (iv) whether the contract price exceeds the expected price. We extract the European Union’s multilingual PP notices, covering 22 different languages. We investigate fine-tuning multilingual transformer models and propose two approaches: (1) multilayer perceptron (MLP) models with transformer embeddings for each business sector in which the training data are filtered based on the procurement category and (2) a k-nearest neighbor (KNN)-based approach fine-tuned using triplet networks. The fine-tuned MBERT model outperforms all other models in predicting calls with a single offer and foreign contract awards, whereas our MLP-based filtering approach yields state-of-the-art results in predicting contracts in which the contract price exceeds the expected price. Furthermore, our KNN-based approach outperforms all the baselines in all tasks and our other proposed models in predicting the number of offers. Moreover, we investigate cross-lingual and multilingual training for our tasks and observe that multilingual training improves prediction accuracy in all our tasks. Overall, our experiments suggest that notice descriptions play an important role in the outcomes of PP calls.

We introduce a generic, language-independent method to collect a large percentage of offensive and hate tweets regardless of their topics or genres. We harness the extralinguistic information embedded in the emojis to collect a large number of offensive tweets. We apply the proposed method on Arabic tweets and compare it with English tweets—analyzing key cultural differences. We observed a constant usage of these emojis to represent offensiveness throughout different timespans on Twitter. We manually annotate and publicly release the largest Arabic dataset for offensive, fine-grained hate speech, vulgar, and violence content. Furthermore, we benchmark the dataset for detecting offensiveness and hate speech using different transformer architectures and perform in-depth linguistic analysis. We evaluate our models on external datasets—a Twitter dataset collected using a completely different method, and a multi-platform dataset containing comments from Twitter, YouTube, and Facebook, for assessing generalization capability. Competitive results on these datasets suggest that the data collected using our method capture universal characteristics of offensive language. Our findings also highlight the common words used in offensive communications, common targets for hate speech, specific patterns in violence tweets, and pinpoint common classification errors that can be attributed to limitations of NLP models. We observe that even state-of-the-art transformer models may fail to take into account culture, background, and context or understand nuances present in real-world data such as sarcasm.

The recent progress of deep learning techniques has produced models capable of achieving high scores on traditional Natural Language Inference (NLI) datasets. To understand the generalization limits of these powerful models, an increasing number of adversarial evaluation schemes have appeared. These works use a similar evaluation method: they construct a new NLI test set based on sentences with known logic and semantic properties (the adversarial set), train a model on a benchmark NLI dataset, and evaluate it in the new set. Poor performance on the adversarial set is identified as a model limitation. The problem with this evaluation procedure is that it may only indicate a sampling problem. A machine learning model can perform poorly on a new test set because the text patterns presented in the adversarial set are not well represented in the training sample. To address this problem, we present a new evaluation method, the Invariance under Equivalence test (IE test). The IE test trains a model with sufficient adversarial examples and checks the model’s performance on two equivalent datasets. As a case study, we apply the IE test to the state-of-the-art NLI models using synonym substitution as the form of adversarial examples. The experiment shows that, despite their high predictive power, these models usually produce different inference outputs for equivalent inputs, and, more importantly, this deficiency cannot be solved by adding adversarial observations in the training data.

Latent semantic analysis (LSA) and correspondence analysis (CA) are two techniques that use a singular value decomposition for dimensionality reduction. LSA has been extensively used to obtain low-dimensional representations that capture relationships among documents and terms. In this article, we present a theoretical analysis and comparison of the two techniques in the context of document-term matrices. We show that CA has some attractive properties as compared to LSA, for instance that effects of margins, that is, sums of row elements and column elements, arising from differing document lengths and term frequencies are effectively eliminated so that the CA solution is optimally suited to focus on relationships among documents and terms. A unifying framework is proposed that includes both CA and LSA as special cases. We empirically compare CA to various LSA-based methods on text categorization in English and authorship attribution on historical Dutch texts and find that CA performs significantly better. We also apply CA to a long-standing question regarding the authorship of the Dutch national anthem Wilhelmus and provide further support that it can be attributed to the author Datheen, among several contenders.

This article introduces the Finnish Corpus of Online Registers (FinCORE) representing the full range of registers – situationally defined text varieties such as news and blogs – on the Finnish Internet. The extreme range of language use found online has challenged the study of registers. It has been unclear what registers the entire Internet includes, and if they can be sufficiently defined to allow for their analysis or classification, previous studies focusing on restricted sets of registers and English. FinCORE features 10,754 texts from the unrestricted web, manually annotated for their register using a scheme originally established for the Corpus of Online Registers of English (CORE). We present the FinCORE registers and compare them to CORE. Finally, we show that the FinCORE registers are sufficiently well-defined to allow for their automatic identification, thus opening novel possibilities for both linguistics and web-as-corpus research. FinCORE is published under an open license.

The availability of large web-based corpora has led to significant advances in a wide range of technologies, including massive retrieval systems or deep neural networks. However, leveraging this data is challenging, since web content is plagued by the so-called boilerplate: ads, incomplete or noisy text and rests of the navigation structure, such as menus or navigation bars. In this work, we present a novel and efficient approach to extract useful and well-formed content from web-scraped data. Our approach takes advantage of Language Models and their implicit knowledge about correctly formed text, and we demonstrate here that perplexity is a valuable artefact that can contribute in terms of effectiveness and efficiency. As a matter of fact, the removal of noisy parts leads to lighter AI or search solutions that are effective and entail important reductions in resources spent. We exemplify here the usefulness of our method with two downstream tasks, search and classification, and a cleaning task. We also provide a Python package with pre-trained models and a web demo demonstrating the capabilities of our approach.

Randomized prospective studies represent the gold standard for experimental design. In this paper, we present a randomized prospective study to validate the benefits of combining rule-based and data-driven natural language understanding methods in a virtual patient dialogue system. The system uses a rule-based pattern matching approach together with a machine learning (ML) approach in the form of a text-based convolutional neural network, combining the two methods with a simple logistic regression model to choose between their predictions for each dialogue turn. In an earlier, retrospective study, the hybrid system yielded a nearly 50% error reduction on our initial data, in part due to the differential performance between the two methods as a function of label frequency. Given these gains, and considering that our hybrid approach is unique among virtual patient systems, we compare the hybrid system to the rule-based system by itself in a randomized prospective study. We evaluate 110 unique medical student subjects interacting with the system over 5,296 conversation turns, to verify whether similar gains are observed in a deployed system. This prospective study broadly confirms the findings from the earlier one but also highlights important deficits in our training data. The hybrid approach still improves over either rule-based or ML approaches individually, even handling unseen classes with some success. However, we observe that live subjects ask more out-of-scope questions than expected. To better handle such questions, we investigate several modifications to the system combination component. These show significant overall accuracy improvements and modest F1 improvements on out-of-scope queries in an offline evaluation. We provide further analysis to characterize the difficulty of the out-of-scope problem that we have identified, as well as to suggest future improvements over the baseline we establish here.

Cyberbullying is the wilful and repeated infliction of harm on an individual using the Internet and digital technologies. Similar to face-to-face bullying, cyberbullying can be captured formally using the Routine Activities Model (RAM) whereby the potential victim and bully are brought into proximity of one another via the interaction on online social networking (OSN) platforms. Although the impact of the COVID-19 (SARS-CoV-2) restrictions on the online presence of minors has yet to be fully grasped, studies have reported that 44% of pre-adolescents have encountered more cyberbullying incidents during the COVID-19 lockdown. Transparency reports shared by OSN companies indicate an increased take-downs of cyberbullying-related comments, posts or content by artificially intelligen moderation tools. However, in order to efficiently and effectively detect or identify whether a social media post or comment qualifies as cyberbullying, there are a number factors based on the RAM, which must be taken into account, which includes the identification of cyberbullying roles and forms. This demands the acquisition of large amounts of fine-grained annotated data which is costly and ethically challenging to produce. In addition where fine-grained datasets do exist they may be unavailable in the target language. Manual translation is costly and expensive, however, state-of-the-art neural machine translation offers a workaround. This study presents a first of its kind experiment in leveraging machine translation to automatically translate a unique pre-adolescent cyberbullying gold standard dataset in Italian with fine-grained annotations into English for training and testing a native binary classifier for pre-adolescent cyberbullying. In addition to contributing high-quality English reference translation of the source gold standard, our experiments indicate that the performance of our target binary classifier when trained on machine-translated English output is on par with the source (Italian) classifier.

Nowadays, firms are constantly looking for methodological approaches that help them to decrease the time needed for the innovation process. Among these approaches, it is worth mentioning the TRIZ-based frameworks such as the Inventive Design Methodology (IDM), where the Problem Graph method is used to formulate a problem. However, the application of IDM is time-consuming due to the construction of a complete map to clarify a problem situation. Therefore, the Inverse Problem Graph (IPG) method has been introduced within the IDM framework to enhance its agility. Nevertheless, the manual gathering of essential information, including parameters and concepts, requires effort and time. This paper integrates the neural network doc2vec and machine learning algorithms as Artificial Intelligence methods into a graphical method inspired by the IPG process. This integration can facilitate and accelerate the development of inventive solutions by extracting parameters and concepts in the inventive design process. The method has been applied to develop a new lattice structure solution in the material field.

Hate speech is any kind of communication that attacks a person or a group based on their characteristics, such as gender, religion and race. Due to the availability of online platforms where people can express their (hateful) opinions, the amount of hate speech is steadily increasing that often leads to offline hate crimes. This paper focuses on understanding and detecting hate speech in underground hacking and extremist forums where cybercriminals and extremists, respectively, communicate with each other, and some of them are associated with criminal activity. Moreover, due to the lengthy posts, it would be beneficial to identify the specific span of text containing hateful content in order to assist site moderators with the removal of hate speech. This paper describes a hate speech dataset composed of posts extracted from HackForums, an online hacking forum, and Stormfront and Incels.co, two extremist forums. We combined our dataset with a Twitter hate speech dataset to train a multi-platform classifier. Our evaluation shows that a classifier trained on multiple sources of data does not always improve the performance compared to a mono-platform classifier. Finally, this is the first work on extracting hate speech spans from longer texts. The paper fine-tunes BERT (Bidirectional Encoder Representations from Transformers) and adopts two approaches – span prediction and sequence labelling. Both approaches successfully extract hateful spans and achieve an F1-score of at least 69%.

This study analyzes political polarization among the South Korean elite by examining 17 years’ worth of subcommittee meeting minutes from the South Korean National Assembly's standing committees. Its analysis applies various natural language processing techniques and the bidirectional encoder representations from the transformers model to measure and analyze polarization in the language used during these meetings. Its findings indicate that the degree of political polarization increased and decreased at various times over the study period but has risen sharply since the second half of 2016 and remained high throughout 2020. This result suggests that partisan political gaps between members of the South Korean National Assembly increase substantially.

Authorship attribution – the computational task of identifying the author of a given text document within a set of possible candidates – has been attracting interest in Natural Language Processing research for many years. At the same time, significant advances have also been observed in the related field of author profiling, that is, the computational task of learning author demographics from text such as gender, age and others. The close relation between the two topics – both of which focused on gaining knowledge about the individual who wrote a piece of text – suggests that research in these fields may benefit from each other. To illustrate this, this work addresses the issue of author identification with the aid of author profiling methods, adding demographics predictions to an authorship attribution architecture that may be particularly suitable to extensions of this kind, namely, a stack of classifiers devoted to different aspects of the input text (words, characters and text distortion patterns.) The enriched model is evaluated across a range of text domains, languages and author profiling estimators, and its results are shown to compare favourably to those obtained by a standard authorship attribution method that does not have access to author demographics predictions.