Natural Language Processing Libraries Research Articles

Complexity trade-off in morphosyntactic module: suggestions from Japanese dialects

Abstract Using the GiNZA v4 natural language processing library, this study examines the case marking richness and word order flexibility in Japanese dialects across 47 regions to investigate the dynamic self-adaptation within the language subsystem, specifically morphology and syntax. Case marking richness is evaluated using the moving-average mean size of paradigm, while word order flexibility is assessed through entropy. The findings reveal a positive correlation between case marking richness and word order flexibility, providing empirical support for the “complexity trade-off hypothesis”. Leveraging the distinct morphosyntactic features inherent in dialects, this study proposes a hypothesis for categorizing Japanese dialects.

College Chatbot

Nowadays, many people are using smartphones with many new applications i.e., technology is growing day by day. Today Artificial Intelligence is playing a major role in a variety of fields ranging from industries in product manufacturing, to customer care in public relations. There are many online Artificial Intelligence (AI) systems or chatbots which are in existence that help people solve their problems. So, we are going to implement a virtual assistant based on AI that can solve any college-related query. This will work as a College Oriented Intelligence machine. This virtual machine will respond to the queries of students on college-related issues. A chatbot has information stored in its database to identify the sentences and make a decision itself as a response to answer a given question. The college enquiry chatbot will be built using an algorithm that analyses queries and understands user’s messages. College Enquiry Chatbot is a type of web application, the main motto of this project is to provide information regarding college. It gives information regarding college only. The information may be like how many branches are in college? what are the courses offered by the college? how many students per branch? By adding some more features and making some improvements we can embed this project into the working site of the college. The chatbot created here is a web-based application which uses Natural Language Processing Libraries and Artificial Intelligence Language to have conversations with humans. It is a simple bot which answers the queries regarding the college

CoreNLP dependency parsing and pattern identification for enhanced opinion mining in aspect-based sentiment analysis

Aspect-Based Sentiment Analysis (ABSA) aims to identify the sentiment expressed towards a specific feature or aspect of a given text. Although certain ABSA techniques employ syntactic information to capture the connection between the opinion target and the sentiment word, they often do not incorporate data processing techniques such as dependency parsing, which can be beneficial in accurately capturing the sentiment expressed towards the opinion target. In this paper a method for ABSA that employs both syntax and semantic information and incorporates dependency parsing(Semantic-Syntactic Dependency Parsing (SSDP) Method) with Core Natural Language Processing (CoreNLP) which is a natural language processing library for processing the input text and identifying patterns effectively (according to the CoreNLP relations and part of speech tagging(POS)) to extract the critical relations that accurately reflect the sentiment conveyed regarding the opinion target is proposed. The results show that the proposed pattern captured approximately 75% of the data, and the rest were classified via Long Short-Term Memory (LSTM) based on semantic information. We illustrated the efficacy of SSDP, through experiments on the SemEval14,Semval15 and Semval16 datasets, which include two datasets (laptops and restaurants) carefully categorized by a human annotator into categories of positive, negative, or neutral attitudes. The experimental results reveal that SSDP is superior to the other state-of-the-art ABSA approaches, that use syntax information but do not utilize data processing techniques. Additionally, we highlight the limitations of ABSA methods that do not incorporate syntax information and the potential improvements that can be made through the use of data processing.

Natural Language Processing in Knowledge-Based Support for Operator Assistance

Manufacturing industry faces increasing complexity in the performance of assembly tasks due to escalating demand for complex products with a greater number of variations. Operators require robust assistance systems to enhance productivity, efficiency, and safety. However, existing support services often fall short when operators encounter unstructured open questions and incomplete sentences due to primarily relying on procedural digital work instructions. This draws attention to the need for practical application of natural language processing (NLP) techniques. This study addresses these challenges by introducing a domain-specific dataset tailored to assembly tasks, capturing unique language patterns and linguistic characteristics. We explore strategies to process declarative and imperative sentences, including incomplete ones, effectively. Thorough evaluation of three pre-trained NLP libraries—NLTK, SPACY, and Stanford—is performed to assess their effectiveness in handling assembly-related concepts and ability to address the domain’s distinctive challenges. Our findings demonstrate the efficient performance of these open-source NLP libraries in accurately handling assembly-related concepts. By providing valuable insights, our research contributes to developing intelligent operator assistance systems, bridging the gap between NLP techniques and the assembly domain within manufacturing industry.

Empowering Financial Peace through Chatbot Guidance

Real-time conversations with humans can be held by intelligent chatbots, utilized by businesses to enhance customer service and streamline operations. A gap is perceived in existing chatbots' achievement of human-like conversational abilities, despite their widespread acceptance and implementation across various sectors. This research paper discusses the development of a unique chatbot providing not only financial guidance but also emotional support, thus sparking a blend of technological innovation and human connection. Emotional states of users are deciphered by the chatbot, using natural language processing (NLP) and sentiment analysis. Tailored responses are generated, offering practical financial advice as well as emotional support during times of stress. Aiming to empower users in managing their financial challenges with serenity, the chatbot integrates emotional support, financial education, goal setting and planning, stress management, and continuous support and monitoring. Cutting-edge technologies, including the Python programming language and NLP libraries like spaCy and NLTK, are incorporated in the project. It interfaces with external financial APIs to provide real-time data enabling informed decision-making. Our chatbot's focus lies in providing a secure and positive environment for users to share financial worries, with the unique ability to comprehend and respond to financial inquiries and emotional nuances in users' messages. By documenting the creation of this chatbot, a contribution is made to the expanding field of emotional intelligence in artificial intelligence applications. The expected outcomes of this research project include an enhanced sense of financial well-being for users, bridging the gap between technological solutions and human-centered support

Investigating Natural Language Techniques for Accurate Noun and Verb Extraction

Natural language processing (NLP) has witnessed significant advancements in recent decades. Automatically classifying parts of speech, like nouns and verbs, from textual input has transformed text analysis and language understanding. Using natural language processing techniques, we explore various methods for identifying noun and verb phrases automatically, with an emphasis on high accuracy. Our study explores rule-based, statistical, and Machine Learning (ML) approaches for determining the nouns and verbs from sentences. The effectiveness of these approaches is clearly evident, especially when NLP libraries such as SpaCy and the Natural Language Toolkit (NLTK) are used. As well as demonstrating their potential applications across diverse language processing tasks and industries, we conduct comparative research to showcase their advantages and disadvantages. The performance of these methods is also examined in terms of retrieving subject and action terms. SpaCy achieves an impressive accuracy of 95% in noun and verb extraction, while Part-Of-Speech (POS) technology tagging delivers an even higher accuracy of 96%. The results obtained with these methods illustrate how nouns, verbs, and names can be classified in text successfully.

Twitter discussions on breastfeeding during the COVID-19 pandemic

BackgroundBreastfeeding is a critical health intervention in infants. Recent literature reported that the COVID-19 pandemic resulted in significant mental health issues in pregnant and breastfeeding women due to social isolation and lack of direct professional support. These maternal mental health issues affected infant nutrition and decreased breastfeeding rates during COVID-19. Twitter, a popular social media platform, can provide insight into public perceptions and sentiment about various health-related topics. With evidence of significant mental health issues among women during the COVID-19 pandemic, the perception of infant nutrition, specifically breastfeeding, remains unknown.MethodsWe aimed to understand public perceptions and sentiment regarding breastfeeding during the COVID-19 pandemic through Twitter analysis using natural language processing techniques. We collected and analyzed tweets related to breastfeeding and COVID-19 during the pandemic from January 2020 to May 2022. We used Python software (v3.9.0) for all data processing and analyses. We performed sentiment and emotion analysis of the tweets using natural language processing libraries and topic modeling using an unsupervised machine-learning algorithm.ResultsWe analyzed 40,628 tweets related to breastfeeding and COVID-19 generated by 28,216 users. Emotion analysis revealed predominantly “Positive emotions” regarding breastfeeding, comprising 72% of tweets. The overall tweet sentiment was positive, with a mean weekly sentiment of 0.25 throughout, and was affected by external events. Topic modeling revealed six significant themes related to breastfeeding and COVID-19. Passive immunity through breastfeeding after maternal vaccination had the highest mean positive sentiment score of 0.32.ConclusionsOur study provides insight into public perceptions and sentiment regarding breastfeeding during the COVID-19 pandemic. Contrary to other topics we explored in the context of COVID (e.g., ivermectin, disinformation), we found that breastfeeding had an overall positive sentiment during the pandemic despite the documented rise in mental health challenges in pregnant and breastfeeding mothers. The wide range of topics on Twitter related to breastfeeding provides an opportunity for active engagement by the medical community and timely dissemination of advice, support, and guidance. Future studies should leverage social media analysis to gain real-time insight into public health topics of importance in child health and apply targeted interventions.

A Combined Usage of NLP Libraries Towards Analyzing Software Documents

Software documents are commonly processed by natural language processing (NLP) libraries to extract information. The libraries provide similar functional APIs to achieve NLP tasks, numerous toolkits result in a problem of selection. In this work, we propose a method to combine the strengths of different NLP libraries to avoid the subjective selection of a specific NLP library. The combined usage is conducted through two steps, i.e. document-level selection of primary NLP library and sentence-level overwriting. The primary NLP library is determined according to the overlap degree of the results. The highest overlap degree indicated the most effective NLP library on a specific NLP task. Through sentence-level overwriting, the possible fine-gained improvements from other libraries are extracted to overwrite the outputs of primary library. We evaluate the combined method with six widely used NLP libraries and 200 documents from three different sources. The results show that the combined method can generally outperform all the studied NLP libraries in terms of accuracy. The finding means that our combined method can be used instead of individual NLP library for more effective results.

Break Down Resumes into Sections to Extract Data and Perform Text Analysis using Python

The objective of AI-based resume screening is to automate the screening process, and text, keyword, and named entity recognition extraction are critical. This paper discusses segmenting resumes in order to extract data and perform text analysis. The raw CV file has been imported, and the resume data cleaned to remove extra spaces, punctuation and stop words. To extract names from resumes, regular expressions are used. We have also used the spaCy library which is considered the most accurate natural language processing library. It includes already-trained models for entity recognition, parsing, and tagging. The experimental method is used with resume data sourced from Kaggle, and external Source (MTIS).

SURVEY ON COMPREHENSIVE TECHNIQUES OF TEXT DATA AUGMENTATION

In the world of Data Science and machine learning everything starts with data and how well one can use it to get the desired output. But what can be done when enough data is not present. Obviously, overall result will get impacted because of scarcity of data. Hence, to resolve this problem, data augmentation comes into picture which helps to increase dataset size by augmenting it. Data augmentation is an assortment of techniques that facilitates to automatically generate high quality data with the help of existing data. In the field of Natural Language Processing (NLP) is difficult to augment the text due to huge complexity of language. The process of augmenting text data is more challenging and not as straightforward as one might expect. In this study, the way of doing NLP data augmentation and libraries available for it is explored. Data augmentation techniques, comparison and features of the python libraries which can be used for data augmentation is discussed. It will help researchers and data scientist to decide which library to use for their job.

An exploratory content and sentiment analysis of the guardian metaverse articles using leximancer and natural language processing

The metaverse has become one of the most popular concepts of recent times. Companies and entrepreneurs are fiercely competing to invest and take part in this virtual world. Millions of people globally are anticipated to spend much of their time in the metaverse, regardless of their age, gender, ethnicity, or culture. There are few comprehensive studies on the positive/negative sentiment and effect of the newly identified, but not well defined, metaverse concept that is already fast evolving the digital landscape. Thereby, this study aimed to better understand the metaverse concept, by, firstly, identifying the positive and negative sentiment characteristics and, secondly, by revealing the associations between the metaverse concept and other related concepts. To do so, this study used Natural Language Processing (NLP) methods, specifically Artificial Intelligence (AI) with computational qualitative analysis. The data comprised metaverse articles from 2021 to 2022 published on The Guardian website, a key global mainstream media outlet. To perform thematic content analysis of the qualitative data, this research used the Leximancer software, and the The Natural Language Toolkit (NLTK) from NLP libraries were used to identify sentiment. Further, an AI-based Monkeylearn API was used to make sectoral classifications of the main topics that emerged in the Leximancer analysis. The key themes which emerged in the Leximancer analysis, included "metaverse", "Facebook", "games" and "platforms". The sentiment analysis revealed that of all articles published in the period of 2021–2022 about the metaverse, 61% (n = 622) were positive, 30% (n = 311) were negative, and 9% (n = 90) were neutral. Positive discourses about the metaverse were found to concern key innovations that the virtual experiences brought to users and companies with the support of the technological infrastructure of blockchain, algorithms, NFTs, led by the gaming world. Negative discourse was found to evidence various problems (misinformation, harmful content, algorithms, data, and equipment) that occur during the use of Facebook and other social media platforms, and that individuals encountered harm in the metaverse or that the metaverse produces new problems. Monkeylearn findings revealed “marketing/advertising/PR” role, “Recreational” business, “Science & Technology” events as the key content topics. This study’s contribution is twofold: first, it showcases a novel way to triangulate qualitative data analysis of large unstructured textual data as a method in exploring the metaverse concept; and second, the study reveals the characteristics of the metaverse as a concept, as well as its association with other related concepts. Given that the topic of the metaverse is new, this is the first study, to our knowledge, to do both.

Создание лингвистического корпуса на основе инструментов обработки естественного языка: планирование программных решений

The paper is aimed at building a model of a linguistic corpus, which is generated according to the rules of the spaCy natural language processing library. Scientific novelty lies in the fact that within the framework of humanities research, the method of modelling is used, which is combined with a corpus approach and takes into account the technological (software) component at the very stage of goal setting. In the research, firstly, a general structural model of a linguistic corpus as a sequence of blocks was determined and standard queries to the database were formulated; secondly, a model of the corpus manager interface able to implement these standard queries was built; thirdly, an analysis of the proposed model with the help of mini-programs that allow assessing the degree of technical feasibility of the queries and their practical value was conducted. At this stage, text arrays of fictional works by German-speaking (F. Kafka, E. M. Remarque) and English-speaking (A. C. Doyle, G. Orwell) writers were involved as linguistic material. The obtained results showed that the constructed model has a number of advantages with a limited number of disadvantages, is flexible in terms of further development and can be programmatically implemented in the short term.

Effective Cyberbullying Detection with SparkNLP

Abstract: People of all ages are affected by cyberbullying, which has become a serious problem that can have negative effects like depression and even suicide. As a result, social media content regulation is becoming more and more necessary. Our research project uses SparkNLP, a potent and scalable natural language processing library, to address the problem of cyberbullying. A model for detecting cyberbullying was developed by us using a dataset of 48,000 tweets from Kaggle for training. We used the Universal Sentence Encoder (USE) to extract text data, and ClassifierDL, which employs deep neural networks and uses USE as an input for text classification, was used to build the classification model. Class imbalance is a problem, so we used text augmentation to address it. Age, ethnicity, gender, religion, other cyberbullying, and not cyberbullying are six categories into which our methodology accurately detects and categorizes cyberbullying. We evaluated the model on a held-out set of data to assess its accuracy and resilience, and it produced remarkable results of 93.34% on training data and 89% on test data. The goal of our project is to support and reinforce the ongoing initiatives that are working to stop cyberbullying and to advance a secure and positive online environment for everyone.

Language patterns in Japanese patients with Alzheimer disease: A machine learning approach.

The authors applied natural language processing and machine learning to explore the disease-related language patterns that warrant objective measures for assessing language ability in Japanese patients with Alzheimer disease (AD), while most previous studies have used large publicly available data sets in Euro-American languages. The authors obtained 276 speech samples from 42 patients with AD and 52 healthy controls, aged 50 years or older. A natural language processing library for Python was used, spaCy, with an add-on library, GiNZA, which is a Japanese parser based on Universal Dependencies designed to facilitate multilingual parser development. The authors used eXtreme Gradient Boosting for our classification algorithm. Each unit of part-of-speech and dependency was tagged and counted to create features such as tag-frequency and tag-to-tag transition-frequency. Each feature's importance was computed during the 100-fold repeated random subsampling validation and averaged. The model resulted in an accuracy of 0.84 (SD=0.06), and an area under the curve of 0.90 (SD=0.03). Among the features that were important for such predictions, seven of the top 10 features were related to part-of-speech, while the remaining three were related to dependency. A box plot analysis demonstrated that the appearance rates of content words-related features were lower among the patients, whereas those with stagnation-related features were higher. The current study demonstrated a promising level of accuracy for predicting AD and found the language patterns corresponding to the type of lexical-semantic decline known as 'empty speech', which is regarded as a characteristic of AD.

КОМПАРАТИВНИЙ АНАЛІЗ ВИРОБНИЦТВА ІНФОРМАЦІЙНОГО КОНТЕНТУ ПРОФЕСІЙНИМИ ЗМІ ТА ТЕЛЕГРАМ-КАНАЛАМИ (НА ПРИКЛАДІ НОВИН “ДЗЕРКАЛА ТИЖНЯ” ТА “УКРАИНА СЕЙЧАС”)

A comparative analysis of journalistic texts in the so-called new media (Telegram channel “Ukrayina Sejchas”) and professional media (online publication “Dzerkalo tyzhnya”) is conducted in the paper. The similarities and differences of the content are clarified (by topic, the most popular personalities who become the heroes of the news, compliance with the standards of accuracy and separation of facts from comments, or the neutrality of the news). The research method is computer analysis of texts using Python modules and Spacy natural language processing library. It was found that the most popular topic for both media was Russia’s full-scale war against Ukraine; Zelenskyi, the General Staff, the Armed Forces, and Putin were mentioned more often. However, at the same time, the Telegram channel bets on hot news from the regions closest to the front line, while the professional mass media takes care of the international agenda. Also, the content of the US is more about personalities, while the DT prefers institutions, some of which are other mass media. Therefore, the online mass media also accumulates exclusive messages from other professional mass media. However, the telegram channel’s attempt to report news as quickly as possible also leads to a violation of standards, in particular accuracy: the telegram channel can report an explosion or missile attack almost immediately as it happened, without having enough facts. The same applies to photos and videos, published as some evidence. Another difference between the Telegram channel and the professional media is that they do not always adhere to the standard of separating facts from comments. Therefore the channel not only report news, but also comment, ironize, etc. The performed analysis allows us to talk about a certain distribution of functions of new and professional media. Telegram channels better adhere to the rule of convergence of interests, and also produce content that is easier to perceive. However, without professional mass media, which will publish the so-called hard news – socially significant messages coming from various institutions and international organizations, as well as check the hot news reported by telegram channels, one cannot talk about an effective media system.

Mapping the plague through natural language processing

Pandemic diseases such as plague have produced a vast amount of literature providing information about the spatiotemporal extent, transmission, or countermeasures. However, the manual extraction of such information from running text is a tedious process, and much of this information remains locked into a narrative format. Natural Language processing (NLP) is a promising tool for the automated extraction of epidemiological data, and can facilitate the establishment of datasets. In this paper, we explore the utility of NLP to assist in the creation of a plague outbreak dataset. We produced a gold standard list of toponyms by manual annotation of a German plague treatise published by Sticker in 1908. We investigated the performance of five pre-trained NLP libraries (Google, Stanford CoreNLP, spaCy, germaNER and Geoparser) for the automated extraction of location data compared to the gold standard. Of all tested algorithms, spaCy performed best (sensitivity 0.92, F1 score 0.83), followed closely by Stanford CoreNLP (sensitivity 0.81, F1 score 0.87). Google NLP had a slightly lower performance (F1 score 0.72, sensitivity 0.78). Geoparser and germaNER had a poor sensitivity (0.41 and 0.61). We then evaluated how well automated geocoding services such as Google geocoding, Geonames and Geoparser located these outbreaks correctly. All geocoding services performed poorly – particularly for historical regions – and returned the correct GIS information only in 60.4%, 52.7% and 33.8% of all cases. Finally, we compared our newly digitized plague dataset to a re-digitized version of the plague treatise by Biraben and provide an update of the spatio-temporal extent of the second pandemic plague outbreaks. We conclude that NLP tools have their limitations, but they are potentially useful to accelerate the collection of data and the generation of a global plague outbreak database.

Narratives That Speak AI Lingua? AI Vocabulary in Listed Companies’ Annual Reports

Narratives about intelligent artefacts have influenced both the public’s imaginary and the actual development of the AI field since its foundation. Yet, in times where the field seems to be flourishing on the one hand, but rushing into an AI winter on the other, factual narratives about AI applications and advancements are more essential than ever. What is the gap between the actual capabilities of today’s AI and the vocabulary used to report about them? In particular, what is the AI lingua used in official, legal documents in business? To find out, we analysed leading share index companies’ annual reports from a representative fraction of the German economy (DAX 30), as a starting step in this direction. In this paper, we present a fact-based methodology for systematically assessing the true state of enterprise AI of those companies. Our initial empirical investigation covers only the annual reports of leading listed German enterprises in the DAX 30 as of May 2021 (i.e. before the DAX’s expansion to 40 members). For this concrete example, we collected their annual reports from 2010 to 2020 (N=312). We then built upon previous work by extending natural language processing (NLP) algorithms we developed for these purposes. The idea is to systematically process and automatically detect the use of AI-related terminology in those annual reports. Such a terminology is part of a classification schema we introduce for differentiating concrete types of AI-related terms. We also compare different NLP libraries regarding their suitability and speculate on the reasons behind the poor performance of some of them. Furthermore, we look at relevant AI keywords and phrases, thereby conducting a human-based semantic analysis of the context – tasks that machines still cannot do effectively. We also give guidance on how to proceed in similar studies, i.e. on how to extend our methodology and the key findings to other national economies. This way, we are contributing not only to an informed perception about the state of enterprise AI, but also to filling the gap between the narratives it uses and the actual state of AI development.

Deployment of a Free-Text Analytics Platform at a UK National Health Service Research Hospital: CogStack at University College London Hospitals.

BackgroundAs more health care organizations transition to using electronic health record (EHR) systems, it is important for these organizations to maximize the secondary use of their data to support service improvement and clinical research. These organizations will find it challenging to have systems capable of harnessing the unstructured data fields in the record (clinical notes, letters, etc) and more practically have such systems interact with all of the hospital data systems (legacy and current).ObjectiveWe describe the deployment of the EHR interfacing information extraction and retrieval platform CogStack at University College London Hospitals (UCLH).MethodsAt UCLH, we have deployed the CogStack platform, an information retrieval platform with natural language processing capabilities. The platform addresses the problem of data ingestion and harmonization from multiple data sources using the Apache NiFi module for managing complex data flows. The platform also facilitates the extraction of structured data from free-text records through use of the MedCAT natural language processing library. Finally, data science tools are made available to support data scientists and the development of downstream applications dependent upon data ingested and analyzed by CogStack.ResultsThe platform has been deployed at the hospital, and in particular, it has facilitated a number of research and service evaluation projects. To date, we have processed over 30 million records, and the insights produced from CogStack have informed a number of clinical research use cases at the hospital.ConclusionsThe CogStack platform can be configured to handle the data ingestion and harmonization challenges faced by a hospital. More importantly, the platform enables the hospital to unlock important clinical information from the unstructured portion of the record using natural language processing technology.

Arabic Language Modeling Based on Supervised Machine Learning

Misinformation and misleading actions have appeared as soon as COVID-19 vaccinations campaigns were launched, no matter what the country’s alphabetization level or growing index is. In such a situation, supervised machine learning techniques for classification appears as a suitable solution to model the value & veracity of data, especially in the Arabic language as a language used by millions of people around the world. To achieve this task, we had to collect data manually from SM platforms such as Facebook, Twitter and Arabic news websites. This paper aims to classify Arabic language news into fake news and real news, by creating a Machine Learning (ML) model that will detect Arabic fake news (DAFN) about COVID-19 vaccination. To achieve our goal, we will use Natural Language Processing (NLP) techniques, which is especially challenging since NLP libraries support for Arabic is not common. We will use NLTK package of python to preprocess the data, and then we will use a ML model for the classification.

HIV- Bidirectional Encoder Representations From Transformers: A Set of Pretrained Transformers for Accelerating HIV Deep Learning Tasks

The human immunodeficiency virus type 1 (HIV-1) is a global health threat that is characterized by extensive genetic diversity both within and between patients, rapid mutation to evade immune controls and antiretroviral therapies, and latent cellular and tissue reservoirs that stymie cure efforts. Viral genomic sequencing has proven effective at surveilling these phenotypes. However, rapid, accurate, and explainable prediction techniques lag our sequencing ability. Modern natural language processing libraries, like the Hugging Face transformers library, have both advanced the technical field and brought much-needed standardization of prediction tasks. Herein, the application of this toolset to an array of classification tasks useful to HIV-1 biology was explored: protease inhibitor resistance, coreceptor utilization, and body-site identification. HIV-Bidirectional Encoder Representations from Transformers (BERT), a protein-based transformer model fine-tuned on HIV-1 genomic sequences, was able to achieve accuracies of 88%, 92%, and 89% on the respective tasks, making it competitive with leading models capable of only one of these tasks. This model was also evaluated using a data augmentation strategy when mutations of known function were introduced. The HIV-BERT model produced results that agreed in directionality 10- to 1000-fold better than traditional machine learning models, indicating an improved ability to generalize biological knowledge to unseen sequences. The HIV-BERT model, trained task-specific models, and the datasets used to construct them have been released to the Hugging Face repository to accelerate research in this field.