Miracle-dz/UniFAR-FTUs huggingface dataset:
MUCS@DravidianLangTech2023: Sentiment Analysis in Code-mixed Tamil and Tulu Texts using fastText

Sentiment Analysis (SA) is a field of computational study that focuses on analyzing and understanding people’s opinions, attitudes, and emotions towards an entity. An entity could be an individual, an event, a topic, a product etc., which is most likely to be covered by reviews and such reviews can be found in abundance on social media platforms. The increase in the number of social media users and the growing amount of user-generated code-mixed content such as reviews, comments, posts etc., on social media have resulted in a rising demand for efficient tools capable of effectively analyzing such content to detect the sentiments. However, SA of social media text is challenging due to the complex nature of the code-mixed text. To tackle this issue, in this paper, we team MUCS, describe learning models submitted to “Sentiment Analysis in Tamil and Tulu” -DravidianLangTech@Recent Advances In Natural Language Processing (RANLP) 2023. Using fastText embeddings to train the Machine Learning (ML) models to perform SA in code-mixed Tamil and Tulu texts, the proposed methodology exhibited F1 scores of 0.14 and 0.204 securing 13th and 15th rank for Tamil and Tulu texts respectively.
Revisiting Automatic Speech Recognition for Tamil and Hindi Connected Number Recognition

Automatic Speech Recognition and its applications are rising in popularity across applications with reasonable inference results. Recent state-of-the-art approaches, often employ significantly large-scale models to show high accuracy for ASR as a whole but often do not consider detailed analysis of performance across low-resource languages applications. In this preliminary work, we propose to revisit ASR in the context of Connected Number Recognition (CNR). More specifically, we (i) present a new dataset HCNR collected to understand various errors of ASR models for CNR, (ii) establish preliminary benchmark and baseline model for CNR, (iii) explore error mitigation strategies and their after-effects on CNR. In the due process, we also compare with end-to-end large scale ASR models for reference, to show its effectiveness.
Overview of Second Shared Task on Homophobia and Transphobia Detection in Social Media Comments

We present an overview of the second shared task on homophobia/transphobia Detection in social media comments. Given a comment, a system must predict whether or not it contains any form of homophobia/transphobia. The shared task included five languages: English, Spanish, Tamil, Hindi, and Malayalam. The data was given for two tasks. Task A was given three labels, and Task B fine-grained seven labels. In total, 75 teams enrolled for the shared task in Codalab. For task A, 12 teams submitted systems for English, eight teams for Tamil, eight teams for Spanish, and seven teams for Hindi. For task B, nine teams submitted for English, 7 teams for Tamil, 6 teams for Malayalam. We present and analyze all submissions in this paper.
Thermal Characteristics and Energy Balance of Various-Size Canadian Shield Lakes in the Mackenzie River Basin

This study addresses the thermal and energy budget characteristics of four different-size Canadian Shield lakes in the Mackenzie River basin during the ice-free season of 2000. The objectives are to characterize and compare the surface temperature and thermal structures, and to quantify the magnitudes and flux rates of the energy balance components of each lake. This study highlights the variability in thermal and energy balance characteristics arising from differences in mean lake depth and surface area. The lakes exhibit similar temporal patterns for air temperature, net radiation, wind speed, and wind direction. Net radiation and wind speed are highest over the largest lake, Great Slave Lake, due to colder surface temperatures and lengthy across-lake wind fetch, respectively. During the warming phase of the summer, surface temperature is inversely related to mean depth; however, during the cooling phase this relationship reverses. The shallowest of the four lakes remains isothermal during the entire ice-free period, while the three larger and deeper lakes are all dimictic. A lag in the onset of thermal stratification in the dimictic lakes is positively correlated with mean depth and surface area. Large evaporative water losses correspond to periods of low net radiation and cold dry air over Great Slave Lake. However, over the smaller, shallower lakes periods of high evaporation occur on days with high net radiation and warm, dry air. The capacity of larger lakes to store more heat results in longer ice-free periods and higher evaporation. Maximum heat content increases and occurs later for lakes of greater depth. Maximum evaporative rates occur later and cumulative evaporation is highest for lakes of greater depth and area. The ratios of total open water evaporation for the four lakes in order of size (smallest 5 1.0) are 1.0 : 1.2 : 1.3 : 1.4. Evaporation magnitudes are discussed in the context of other temperate and high-latitude lake studies.
RURAL ROAD FEATURE EXTRACTION FROM AERIAL IMAGES USING ANISOTROPIC DIFFUSION AND DYNAMIC SNAKES

iii ACKNOWLEDGMENTS I sincerely thank Dr. Bon A. Dewitt for his continuous support and encouragement throughout the course of this research. He provided much needed technical help and constructive criticism by taking time out of his busy schedule. Dr Michael C. Nechyba for getting me started with the right background to do research in the field of image processing. I would like to thank Dr Grenville Barnes and Dr Dave Gibson for their patience and support, and their invaluable contribution in supervising my research. Finally, I wish to express love and respect for my parents, family and friends. They are always with me.
Discourse Analysis of Argumentative Essays of English Learners Based on CEFR Level

In this paper, we investigate the relationship between the use of discourse relations and the CEFR-level of argumentative English learner essays. Using both the Rhetorical Structure Theory (RST) and the Penn Discourse TreeBank (PDTB) frameworks, we analyze essays from The International Corpus Network of Asian Learners (ICNALE), and the Corpus and Repository of Writing (CROW). Results show that the use of the RST relations of Explanation and Background, as well as the first-level PDTB sense of Contingency, are influenced by the English proficiency level of the writer.
Negative documents are positive: Improving event extraction performance using overlooked negative data

The scarcity of data poses a significant challenge in closed-domain event extraction, as is common in complex NLP tasks. This limitation primarily arises from the intricate nature of the annotation process. To address this issue, we present a multi-task model structure and training approach that leverages the additional data, which is found as not having any event information at document and sentence levels, generated during the event annotation process. By incorporating this supplementary data, our proposed framework demonstrates enhanced robustness and, in some scenarios, improved performance. A particularly noteworthy observation is that including only negative documents in addition to the original data contributes to performance enhancement. Our findings offer promising insights into leveraging extra data to mitigate data scarcity challenges in closed-domain event extraction.
Word Sense Disambiguation for Automatic Translation of Medical Dialogues into Pictographs

Word sense disambiguation is an NLP task embedded in different applications. We propose to evaluate its contribution to the automatic translation of French texts into pictographs, in the context of communication between doctors and patients with an intellectual disability. Different general and/or medical language models (Word2Vec, fastText, CamemBERT, FlauBERT, DrBERT, and CamemBERT-bio) are tested in order to choose semantically correct pictographs leveraging the synsets in the French WordNets (WOLF and WoNeF). The results of our automatic evaluations show that our method based on Word2Vec and fastText significantly improves the precision of medical translations into pictographs. We also present an evaluation corpus adapted to this task.
Automatically Generating Hindi Wikipedia Pages Using Wikidata as a Knowledge Graph: A Domain-Specific Template Sentences Approach

This paper presents a method for automatically generating Wikipedia articles in the Hindi language, using Wikidata as a knowledge base. Our method extracts structured information from Wikidata, such as the names of entities, their properties, and their relationships, and then uses this information to generate natural language text that conforms to a set of templates designed for the domain of interest. We evaluate our method by generating articles about scientists, and we compare the resulting articles to machine-translated articles. Our results show that more than 70% of the generated articles using our method are better in terms of coherence, structure, and readability. Our approach has the potential to significantly reduce the time and effort required to create Wikipedia articles in Hindi and could be extended to other languages and domains as well.
Designing the LECOR Learner Corpus for Romanian

This article presents a work-in-progress project, which aims to build and utilize a corpus of Romanian texts written or spoken by non-native students of different nationalities, who learn Romanian as a foreign language in the one-year, intensive academic program organized by the University of Bucharest. This corpus, called LECOR – Learner Corpus for Romanian – is made up of pairs of texts: a version of the student and a corrected one of the teacher. Each version is automatically annotated with lemma and POS-tag, and the two versions are then compared, and the differences are marked as errors at this stage. The corpus also contains metadata file sets about students and their samples. In this article, the conceptual framework for building and utilization of the corpus is presented, including the acquisition and organization phases of the primary material, the annotation process, and the first attempts to adapt the NoSketch Engine query interface to the project’s objectives. The article concludes by outlining the next steps in the development of the corpus aimed at quantitative accumulation and the development of the error correction process and the complex error annotation.
‘ChemXtract’ A System for Extraction of Chemical Events from Patent Documents

ChemXtraxt main goal is to extract the chemical events from patent documents. Event extraction requires that we first identify the names of chemical compounds involved in the events. Thus, in this work two extractions are done and they are (a) names of chemical compounds and (b) event that identify the specific involvement of the chemical compounds in a chemical reaction. Extraction of essential elements of a chemical reaction, generally known as Named Entity Recognition (NER), extracts the compounds, condition and yields, their specific role in reaction and assigns a label according to the role it plays within a chemical reaction. Whereas event extraction identifies the chemical event relations between the chemical compounds identified. Here in this work we have used Neural Conditional Random Fields (NCRF), which combines the power of artificial neural network (ANN) and CRFs. Different levels of features that include linguistic, orthographical and lexical clues are used. The results obtained are encouraging.
Tibetan Dependency Parsing with Graph Convolutional Neural Networks

Dependency parsing is a syntactic analysis method to analyze the dependency relationships between words in a sentence. The interconnection between words through dependency relationships is typical graph data. Traditional Tibetan dependency parsing methods typically model dependency analysis as a transition-based or sequence-labeling task, ignoring the graph information between words. To address this issue, this paper proposes a graph neural network (GNN)-based Tibetan dependency parsing method. This method treats Tibetan words as nodes and the dependency relationships between words as edges, thereby constructing the graph data of Tibetan sentences. Specifically, we use BiLSTM to learn the word representations of Tibetan, utilize GNN to model the relationships between words and employ MLP to predict the types of relationships between words. We conduct experiments on a Tibetan dependency database, and the results show that the proposed method can achieve high-quality Tibetan dependency parsing results.
Where “where” Matters : Event Location Disambiguation with a BERT Language Model

The method method presented in this paper uses a BERT model for classifying location mentions in event reporting news texts into two classes: a place of an event, called main location, or another location mention, called here secondary location. Our evaluation on articles, reporting protests, shows promising results and demonstrates the feasibility of our approach and the event geolocation task in general. We evaluate our method against a simple baseline and state of the art ML models and we achieve a significant improvement in all cases by using the BERT model. In contrast to other location classification approaches, we completelly avoid lingusitic pre processing and feature engineering, which is a pre-requisite for all multi-domain and multilingual applications.
MUCS@DravidianLangTech2023: Malayalam Fake News Detection Using Machine Learning Approach

Social media is widely used to spread fake news, which affects a larger population. So it is considered as a very important task to detect fake news spread on social media platforms. To address the challenges in the identification of fake news in the Malayalam language, in this paper, we - team MUCS, describe the Machine Learning (ML) models submitted to “Fake News Detection in Dravidian Languages” at DravidianLangTech@RANLP 2023 shared task. Three different models, namely, Multinomial Naive Bayes (MNB), Logistic Regression (LR), and Ensemble model (MNB, LR, and SVM) are trained using Term Frequency - Inverse Document Frequency (TF-IDF) of word unigrams. Among the three models ensemble model performed better with a macro F1-score of 0.83 and placed 3rd rank in the shared task.
Simplification by Lexical Deletion

Lexical simplification traditionally focuses on the replacement of tokens with simpler alternatives. However, in some cases the goal of this task (simplifying the form while preserving the meaning) may be better served by removing a word rather than replacing it. In fact, we show that existing datasets rely heavily on the deletion operation. We propose supervised and unsupervised solutions for lexical deletion based on classification, end-to-end simplification systems and custom language models. We contribute a new silver-standard corpus of lexical deletions (called SimpleDelete), which we mine from simple English Wikipedia edit histories and use to evaluate approaches to detecting superfluous words. The results show that even unsupervised approaches (TerseBERT) can achieve good performance in this new task. Deletion is one part of the wider lexical simplification puzzle, which we show can be isolated and investigated.
NoHateBrazil: A Brazilian Portuguese Text Offensiveness Analysis System

Hate speech is a surely relevant problem in Brazil. Nevertheless, its regulation is not effective due to the difficulty to identify, quantify and classify offensive comments. Here, we introduce a novel system for offensive comment analysis in Brazilian Portuguese. The system titled “NoHateBrazil” recognizes explicit and implicit offensiveness in context at a fine-grained level. Specifically, we propose a framework for data collection, human annotation and machine learning models that were used to build the system. In addition, we assess the potential of our system to reflect stereotypical beliefs against marginalized groups by contrasting them with counter-stereotypes. As a result, a friendly web application was implemented, which besides presenting relevant performance, showed promising results towards mitigation of the risk of reinforcing social stereotypes. Lastly, new measures were proposed to improve the explainability of offensiveness classification and reliability of the model’s predictions.
A Review of Research-Based Automatic Text Simplification Tools

In the age of knowledge, the democratisation of information facilitated through the Internet may not be as pervasive if written language poses challenges to particular sectors of the population. The objective of this paper is to present an overview of research-based automatic text simplification tools. Consequently, we describe aspects such as the language, language phenomena, language levels simplified, approaches, specific target populations these tools are created for (e.g. individuals with cognitive impairment, attention deficit, elderly people, children, language learners), and accessibility and availability considerations. The review of existing studies covering automatic text simplification tools is undergone by searching two databases: Web of Science and Scopus. The eligibility criteria involve text simplification tools with a scientific background in order to ascertain how they operate. This methodology yielded 27 text simplification tools that are further analysed. Some of the main conclusions reached with this review are the lack of resources accessible to the public, the need for customisation to foster the individual’s independence by allowing the user to select what s/he finds challenging to understand while not limiting the user’s capabilities and the need for more simplification tools in languages other than English, to mention a few.
Are You Not moved? Incorporating Sensorimotor Knowledge to Improve Metaphor Detection

Metaphors use words from one domain of knowledge to describe another, which can make the meaning less clear and require human interpretation to understand. This makes it difficult for automated models to detect metaphorical usage. The objective of the experiments in the paper is to enhance the ability of deep learning models to detect metaphors automatically. This is achieved by using two elements of semantic richness, sensory experience, and body-object interaction, as the main lexical features, combined with the contextual information present in the metaphorical sentences. The tests were conducted using classification and sequence labeling models for metaphor detection on the three metaphorical corpora VUAMC, MOH-X, and TroFi. The sensory experience led to significant improvements in the classification and sequence labelling models across all datasets. The highest gains were seen on the VUAMC dataset: recall increased by 20.9%, F1 by 7.5% for the classification model, and Recall increased by 11.66% and F1 by 3.69% for the sequence labelling model. Body-object interaction also showed positive impact on the three datasets.
Machine Translation of literary texts: genres, times and systems

Machine Translation (MT) has taken off dramatically in recent years due to the advent of Deep Learning methods and Neural Machine Translation (NMT) has enhanced the quality of automatic translation significantly. While most work has covered the automatic translation of technical, legal and medical texts, the application of MT to literary texts and the human role in this process have been underexplored. In an effort to bridge the gap of this under-researched area, this paper presents the results of a study which seeks to evaluate the performance of three MT systems applied to two different literary genres, two novels (1984 by George Orwell and Pride and Prejudice by Jane Austen) and two poems (I Felt a Funeral in my Brain by Emily Dickinson and Siren Song by Margaret Atwood) representing different literary periods and timelines. The evaluation was conducted by way of the automatic evaluation metric BLEU to objectively assess the performance that the MT system shows on each genre. The limitations of this study are also outlined.
On the Evaluation of Terminology Translation Errors in NMT and PB-SMT in the Legal Domain: a Study on the Translation of Arabic Legal Documents into English and French

In the translation process, terminological resources are used to solve translation problems, so information on terminological equivalence is crucial to make the most appropriate choices in terms of translation equivalence. In the context of Machine translation, indeed, neural models have improved the state-of-the-art in Machine Translation considerably in recent years. However, they still underperform in domain-specific fields and in under-resourced languages. This is particularly evident in translating legal terminology for Arabic, where current Machine Translation outputs do not adhere to the contextual, linguistic, cultural, and terminological constraints posed by translating legal terms in Arabic. In this paper, we conduct a comparative qualitative evaluation and comprehensive error analysis on legal terminology translation in Phrase-Based Statistical Machine Translation and Neural Machine Translation in two translation language pairs: Arabic-English and Arabic-French. We propose an error typology taking the legal terminology translation from Arabic into account. We demonstrate our findings, highlighting the strengths and weaknesses of both approaches in the area of legal terminology translation for Arabic. We also introduce a multilingual gold standard dataset that we developed using our Arabic legal corpus. This dataset serves as a reliable benchmark and/or reference during the evaluation process to decide the degree of adequacy and fluency of the Phrase-Based Statistical Machine Translation and Neural Machine Translation systems.
ChatGPT_Powered_Tourist_Aid_Applications__Proficient_in_Hindi__Yet_To_Master_Telugu_and_Kannada

This research investigates the effectiveness of Chat- GPT, an AI language model by OpenAI, in translating English into Hindi, Telugu, and Kannada languages, aimed at assisting tourists in India’s linguistically diverse environment. To measure the translation quality, a test set of 50 questions from diverse fields such as general knowledge, food, and travel was used. These were assessed by five volunteers for accuracy and fluency, and the scores were subsequently converted into a BLEU score. The BLEU score evaluates the closeness of a machine-generated translation to a human translation, with a higher score indicating better translation quality. The Hindi translations outperformed others, showcasing superior accuracy and fluency, whereas Telugu translations lagged behind. Human evaluators rated both the accuracy and fluency of translations, offering a comprehensive perspective on the language model’s performance.