Natural Language Processing Resources Research Articles

SrpELTeC: A Serbian Literary Corpus for Distant Reading

The article presents SrpELTeC, a corpus developed within the COST action Distant Reading for European Literary History (CA16204). All novels in SrpELTeC were selected, prepared, and annotated using the common principles established for all language collections in the European Literary Text Collection (ELTeC). The challenges and solutions in preparing SrpELTeC from scratch are outlined. All novels were manually encoded in TEI with rich metadata and structural annotation. The automatic annotation included POS-tagging, lemmatization, and named entities, relying on Natural Language Processing resources developed and maintained by the JeRTeh Language Resources and Technologies Society. The integration of SrpELTeC with Wikidata was supported with a set of SPARQL queries for the retrieval of metadata with different visualization options. Recent activities within the COST Action NexusLinguarum—European Network for Web-centred Linguistic Data Science (CA18209) are related to the linked data version of SrpELTeC using the NLP Interchange Format. All versions of SrpELTeC are freely available under the CC-BY license.

Automatic generation of nominal phrases for Portuguese and Galician

Abstract This paper presents XeraWord, an innovative tool for automatically generating nominal phrases. XeraWord can be used for different tasks, ranging from teaching languages to the creation of examples in lexicography, or even for the development of resources for natural language processing. In this area, Xera was the first experiment, allowing the automatic generation of nominal phrases in three languages: German, French and Spanish. This tool was extended to support other languages, namely, Portuguese and Galician. We start by presenting the theory behind the development of Xera and its new version, XeraWord, namely, the applied base methodology, and the natural language processing resources used to support it. Then, TraduWord, a tool specifically developed to construct resources for new languages, is presented. This tool allows the semi-automatic translation of the data required for the nominal phrase generation. For this, we discuss its advantages and disadvantages, analysing the quality of the translated resources, as well as the amount of manual work required to validate and correct these resources.

Building Text and Speech Benchmark Datasets and Models for Low‐Resourced East African Languages: Experiences and Lessons

ABSTRACTAfrica has over 2000 languages; however, those languages are not well represented in the existing natural language processing ecosystem. African languages lack essential digital resources to effectively engage in advancing language technologies. There is a need to generate high‐quality natural language processing resources for low‐resourced African languages. Obtaining high‐quality speech and text data is expensive and tedious because it can involve manual sourcing and verification of data sources. This paper discusses the process taken to curate and annotate text and speech datasets for five East African languages: Luganda, Runyankore‐Rukiga, Acholi, Lumasaba, and Swahili. We also present results obtained from baseline models for machine translation, topic modeling and classification, sentiment classification, and automatic speech recognition tasks. Finally, we discuss the experiences, challenges, and lessons learned in creating the text and speech datasets.

Sentiment Analysis of Finnish Twitter Discussions on COVID-19 During the Pandemic

With the outbreak of the COVID-19 pandemic, researchers have studied how people reacted on social media during the pandemic. Sentiment analysis has been leveraged to gain insight. However, much of the research conducted on both sentiment analysis and social media analysis of COVID-19 often focuses on widespread languages, such as English and Chinese. This is partly due to the scarcity of resources for natural language processing and sentiment analysis for morphologically complex and less prevalent languages such as Finnish. This paper aims to analyze sentiments on Twitter in the Finnish language during the COVID-19 pandemic. We manually annotate with sentiments a random sample of 1943 tweets about COVID-19 in Finnish. We use it to build binomial and multinomial logistic regression models with Lasso penalty by exploiting ngrams and two existing sentiment lexicons. We also build two similar models using an existing (pre-COVID-19) Twitter dataset for comparison. The best-performing model for the Finnish language is then used to determine the trends of positive, negative, and neutral opinions on a collection of tweets in Finnish extracted between April 21 and June 18, 2020. The best sentiment polarity prediction model for the Finnish language attain 0.785 AUC, 0.710 balanced accuracy, and 0.723 macro-averaged F1 for predicting positive and negative polarity (binomial classification), and 0.667 AUC, 0.607 balanced accuracy, and 0.475 F1 when adding neutral tweets (multinomial classification). On the other hand, the pre-COVID-19 model trained on the same number of tweets exhibits higher accuracy for the multinomial model (0.687 balanced accuracy, and 0.588 F1). We hypothesize that this loss of performance is due to the COVID-19 context that makes sentiment analysis of neutral tweets more difficult for the machine learning algorithm to predict. Running the model on all the extracted Finnish tweets, we observe a decrease in negativity and an increase in positivity over the observed time as the Finnish government lifts restrictions. Our results show that applying an existing general-purpose sentiment analyzer on tweets that are domain-specific, such as COVID-19, provides lower accuracy. More effort in the future needs to be invested in using and developing sentiment analysis tools tailored to their application domain when conducting large-scale social media analysis of specific medical issues, such as a global pandemic.

Automated Indonesian Text Augmentation with Web-Based Application Using Flask Framework

Text classification is one of the fundamental tasks in natural language processing (NLP). In the real world, data and resources available for text classification are limited. One of the issues with labeled data is imbalanced data. The problem of imbalanced data affects the performance and accuracy of the model because the model only focuses on data with majority labels. This impacts the model performance, which tends to classify correctly for the majority label only. Meanwhile, in some cases, it is more important for the minority label to be predicted correctly. Therefore, the measure of model accuracy cannot describe the true performance of the model. To overcome this, an oversampling approach is carried out. Text-based oversampling is known as text augmentation. However, NLP resources for the Indonesian language are still limited, especially in performing text augmentation. Therefore, this research conducts the development of a web application to augment Indonesian text automatically. The application was built using the prototype method. Users can perform augmentation automatically for the entire text in the dataset. Users can select preferred augmentation techniques and are required to upload datasets as input. The output of the application is the same dataset file as the input, with an additional column containing synthetic text augmented by the application.

A State of the Art Review on Natural Language Processing applied to the Malagasy Language

Based on the growing mass of information of all kinds to be processed and to facilitate human/machine dialogue, research teams and language industries are still developing applications integrating automatic natural language processing techniques. Natural Language Processing (NLP) is a discipline on the border of linguistics and computer science that concerns the application of computer programs and techniques to all aspects of human language. NLP is an area of research that is still open. It has extended its applications in various fields and applied them in several languages worldwide. This paper will review the NLP methods and resources that have been performed on the Malagasy language only. Recently, work on the automatic processing of the Malagasy language occurs even if the language is part of the group of under-endowed languages. This will allow researchers who are interested in the Malagasy language to update themselves in terms of the work already carried out, and the challenges that may be envisaged in future.

Multilingual NLP

The subject area of multilingual natural language processing (NLP) is concerned with the processing of natural language data in several languages. NLP systems that can translate between languages are becoming more and more necessary as the globe gets more interconnected in order to promote understanding and communication among speakers of various languages. To be effective, communication must overcome a number of obstacles presented by multilingual NLP. Lack of language standardization, which results in major variations in the grammatical constructions, vocabulary, and writing systems used in many languages, is one of the fundamental problems. The requirement for substantial amounts of annotated data for machine learning model training presents another difficulty. The creation of high-quality annotated datasets in numerous languages is time- and money-consuming, which restricts the supply of multilingual NLP resources. The problem of creating NLP systems that can handle several languages at once is the last one. This necessitates the deployment of sophisticated algorithms that can handle and evaluate data in numerous languages while producing precise findings. Researchers and developers are working on a variety of methods to address these issues. Creating standardized formats for multilingual data representation, like Universal Dependencies, which offers a unified framework for annotating linguistic data in several languages, is one strategy. Using transfer learning techniques to transfer knowledge from high-resource languages to low-resource languages is an alternative strategy. The amount of annotated data required for training NLP models in low-resource languages can bede creased with the use of this method. Last but not least, researchers are working to create multilingual NLP models that can manage numerous languages at once. To deliver precise results across numerous languages, these models employ cutting-edge methodologies like neural machine translation and multilingual word embedding’s. Despite the fact that multilingual NLP presents a number of difficult issues, with continuing study and development, it is possible to create NLP systems that are capable of processing natural language data from several languages.

TPTS: Text pre-processing Techniques for Sindhi Language

The Internet is a significant source of textual data, with users generating vast amounts of information through social media and news agencies daily. The extraction of meaningful information from large datasets is a challenging and costly process. Text pre-processing is a crucial initial step in any Natural Language Processing (NLP) task, as it can impact the overall performance of the study. The main objective of text pre-processing is to transform unstructured text into a linguistically meaningful (standard form) format, making extracting information for any text-processing task easier. This paper introduces TPTS, a model for text pre-processing in the Sindhi language. TPTS performs essential NLP tasks such as text tokenization, normalization, stop-word removal, stemming, and POS tagging for the Sindhi language. The Sindhi Text Corpus (STC), consisting of 1.5k Sindhi text documents collected from various online news websites, is used for experimentation. The TF-IDF approach is employed to identify high-frequency stop-words in the Sindhi language. Furthermore, a rule-based system tags words with their part of speech in Sindhi input text. The ROUGE evaluation metric is used to assess the effectiveness of the proposed TPTS technique, achieving 89% accuracy on the STC corpus. The Sindhi language is spoken by over 30 million people globally, and the lack of adequate NLP tools and resources limits the development of technology and natural language applications that can benefit Sindhi speakers. The proposed TPTS model can aid in developing such applications, making it beneficial not only for text pre-processing tasks but also for other Sindhi language text-processing tasks such as text summarization, sentiment analysis, speech-processing applications, text mining, and information retrieval systems.

A study on methods for revising dependency treebanks: in search of gold

Reliably annotated corpora with reliable annotation are a valuable resource for Natural Language Processing, which justifies the search for methods capable of assisting linguistic revision. In this context, we present a study on methods for revising dependency treebanks, investigating the contribution of three different strategies to the corpus review: (i) linguistic rules; (ii) an adaptation of the n-grams method proposed by Boyd et al. (2008) applied to Portuguese; and (iii) Inter-Annotator Disagreement, a linguistically motivated approach that draws inspiration from the human annotation process. The results are promising, and taken together the three methods can lead to the revision of up to 58% of the errors in a specific corpus at the cost of revising only 20% of the corpus. We also present a tool that integrates treebank editing, evaluation and search capabilities with the review methods, as well as a gold-standard Portuguese corpus from the oil and gas domain.

Practical Considerations for Developing Clinical Natural Language Processing Systems for Population Health Management and Measurement.

Experts have noted a concerning gap between clinical natural language processing (NLP) research and real-world applications, such as clinical decision support. To help address this gap, in this viewpoint, we enumerate a set of practical considerations for developing an NLP system to support real-world clinical needs and improve health outcomes. They include determining (1) the readiness of the data and compute resources for NLP, (2) the organizational incentives to use and maintain the NLP systems, and (3) the feasibility of implementation and continued monitoring. These considerations are intended to benefit the design of future clinical NLP projects and can be applied across a variety of settings, including large health systems or smaller clinical practices that have adopted electronic medical records in the United States and globally.

ATIVIDADE DE AGÊNCIAS BRASILEIRAS DE TURISMO NAS REDES SOCIAIS: UMA ANÁLISE UTILIZANDO PROCESSAMENTO DE LINGUAGEM NATURAL

ABSTRACT This paper aims to analyze the activity of two Brazilian tourism agencies in social media and the online behavior of their consumers. The research used Natural language processing resources supported by sentiment and content analysis techniques. The main results show a prevalence of positive comments on the companies' pages, and companies are more responsive to users on pages with a higher number of positive comments. There is also a tendency towards more significant company interaction with user comments that express positive emotions.

Division and the Digital Language Divide: A Critical Perspective on Natural Language Processing Resources for the South and North Korean Languages

Abstract: The digital world is marked by large asymmetries in the volume of content available between different languages. As a direct corollary, this inequality also exists, amplified, in the number of resources (labeled and unlabeled datasets, pretrained models, academic research) available for the computational analysis of these languages or what is generally called natural language processing (NLP). NLP literature divides languages between high- and low-resource languages. Thanks to early private and public investment in the field, the Korean language is generally considered to be a high-resource language. Yet, the good fortunes of Korean in the age of machine learning obscure the divided state of the language, as recensions of available resources and research solely focus on the standard language of South Korea, thus making it the sole representant of an otherwise diverse linguistic family that includes the Northern standard language as well as regional and diasporic dialects. This paper shows that the resources developed for the South Korean language do not necessarily transfer to the North Korean language. However, it also argues that this does not make North Korean a low-resource language. On one hand, South Korean resources can be augmented with North Korean data to achieve better performance. On the other, North Korean has more resources than commonly assumed. Retracing the long history of NLP research in North Korea, the paper shows that a large number of datasets and research exists for the North Korean language even if they are not easily available. The paper concludes by exploring the possibility of "unified" language models and underscoring the need for active NLP research collaboration across the Korean peninsula.

A Survey on NLP Resources, Tools, and Techniques for Marathi Language Processing

Natural Language Processing (NLP) has been in practice for the past couple of decades, and extensive work has been done for the Western languages, particularly the English language. The Eastern counterpart, especially the languages of the Indian subcontinent, needs attention as not much language processing work has been done on these languages. Western languages are rich in dictionaries, WordNet, and associated tools, while Indian languages are lagging behind in this segment. Marathi is the third most spoken language in India and the 15th most spoken language worldwide. Lack of resources, complex linguistic facts, and the inclusion of prevalent dialects of neighbors have resulted in limited work for Marathi. The aim of this study is to provide an insight into the various linguistic resources, tools, and state-of-the-art techniques applied to the processing of the Marathi language. Initially, morphological descriptions of the Marathi language are provided, followed by a discussion on the characteristics of the Marathi language. Thereafter, for Marathi language, the availability of corpus, tools, and techniques to be used to develop NLP tasks is reviewed. Finally, gap analysis is discussed in current research and future directions for this new and dynamic area of research are listed that will benefit the Marathi Language Processing research community.

Sentiment analysis on Bangla conversation using machine learning approach

<span>Nowadays, online communication is more convenient and popular than face-to-face conversation. Therefore, people prefer online communication over face-to-face meetings. Enormous people use online chatting systems to speak with their loved ones at any given time throughout the world. People create massive quantities of conversation every second because of their online engagement. People's feelings during the conversation period can be gleaned as useful information from these conversations. Text analysis and conclusion of any material as summarization can be done using sentiment analysis by natural language processing. The use of communication for customer service portals in various e-commerce platforms and crime investigations based on digital evidence is increasing the need for sentiment analysis of a conversation. Other languages, such as English, have well-developed libraries and resources for natural language processing, yet there are few studies conducted on Bangla. It is more challenging to extract sentiments from Bangla conversational data due to the language's grammatical complexity. As a result, it opens vast study opportunities. So, support vector machine, multinomial naïve Bayes, k-nearest neighbors, logistic regression, decision tree, and random forest was used. From the dataset, extracted information was labeled as positive and negative.</span>

Training and intrinsic evaluation of lightweight word embeddings for the clinical domain in Spanish.

Resources for Natural Language Processing (NLP) are less numerous for languages different from English. In the clinical domain, where these resources are vital for obtaining new knowledge about human health and diseases, creating new resources for the Spanish language is imperative. One of the most common approaches in NLP is word embeddings, which are dense vector representations of a word, considering the word's context. This vector representation is usually the first step in various NLP tasks, such as text classification or information extraction. Therefore, in order to enrich Spanish language NLP tools, we built a Spanish clinical corpus from waiting list diagnostic suspicions, a biomedical corpus from medical journals, and term sequences sampled from the Unified Medical Language System (UMLS). These three corpora can be used to compute word embeddings models from scratch using Word2vec and fastText algorithms. Furthermore, to validate the quality of the calculated embeddings, we adapted several evaluation datasets in English, including some tests that have not been used in Spanish to the best of our knowledge. These translations were validated by two bilingual clinicians following an ad hoc validation standard for the translation. Even though contextualized word embeddings nowadays receive enormous attention, their calculation and deployment require specialized hardware and giant training corpora. Our static embeddings can be used in clinical applications with limited computational resources. The validation of the intrinsic test we present here can help groups working on static and contextualized word embeddings. We are releasing the training corpus and the embeddings within this publication1.

Analyzing Spanish-Language Public Sentiment in the Context of a Pandemic and Social Unrest: The Panama Case.

Over the past decade, an increase in global connectivity and social media users has changed the way in which opinions and sentiments are shared. Platforms such as Twitter can act as public forums for expressing opinions on non-personal matters, but often also as an outlet for individuals to share their feelings and personal thoughts. This becomes especially evident during times of crisis, such as a massive civil disorder or a pandemic. This study proposes the estimation and analysis of sentiments expressed by Twitter users of the Republic of Panama during the years 2019 and 2020. The proposed workflow is comprised of the extraction, quantification, processing and analysis of Spanish-language Twitter data based on Sentiment Analysis. This case of study highlights the importance of developing natural language processing resources explicitly devised for supporting opinion mining applications in Latin American countries, where language regionalisms can drastically change the lexicon on each country. A comparative analysis performed between popular machine learning algorithms demonstrated that a version of a distributed gradient boosting algorithm could infer sentiment polarity contained in Spanish text in an accurate and time-effective manner. This algorithm is the tool used to analyze over 20 million tweets produced between the years of 2019 and 2020 by residents of the Republic of Panama, accurately displaying strong sentiment responses to events occurred in the country over the two years that the analysis performed spanned. The obtained results highlight the potential that methodologies such as the one proposed in this study could have for transparent government monitoring of responses to public policies on a population scale.

RastrOS Project: Natural Language Processing contributions to the development of an eye-tracking corpus with predictability norms for Brazilian Portuguese.

This article presents RastrOS, a new eye-tracking corpus of eye movement data from university students during silent reading of paragraphs of texts in Brazilian Portuguese (BP). The article shows the potential of the corpus for natural language processing (NLP) using it to evaluate the sentence complexity prediction task in BP and it also focuses on the description of NLP resources and methods developed to create the corpus. Specifically, we present: (i) the method used to select the corpus paragraphs from large corpora, using linguistic metrics and clustering algorithms; (ii) the platform for collecting the Cloze test, which is also responsible for creating the project datasets, and (iii) the hybrid semantic similarity method, based on word embedding models and contextualised word representations, used to generate semantic predictability norms. RastrOS can be downloaded from the open science framework repository with the computational infrastructure mentioned above. Datasets with predictability norms of 393 participants and eye-tracking data of 37 participants are available in the OSF repository for this work (https://osf.io/9jxg3/).

Construction Grammar Conceptual Network: Coordination-based graph method for semantic association analysis

AbstractIn this article, we present the Construction Grammar Conceptual Network method, developed for identifying lexical similarity and word sense discrimination in a syntactically tagged corpus, based on the cognitive linguistic assumption that coordination construction instantiates conceptual relatedness. This graph analysis method projects a semantic value onto a given coordinated syntactic dependency and constructs a second-order lexical network of lexical collocates with a high co-occurrence measure. The subsequent process of clustering and pruning the graph reveals lexical communities with high conceptual similarity, which are interpreted as associated senses of the source lexeme. We demonstrate the theory and its application to the task of identifying the conceptual structure and different meanings of nouns, adjectives and verbs using examples from different corpora, and explain the modulating effects of linguistic and graph parameters. This graph approach is based on syntactic dependency processing and can be used as a complementary method to other contemporary natural language processing resources to enrich semantic tasks such as word disambiguation, domain relatedness, sense structure, identification of synonymy, metonymy, and metaphoricity, as well as to automate comprehensive meta-reasoning about languages and identify cross/intra-cultural discourse variations of prototypical conceptualization patterns and knowledge representations. As a contribution, we provide a web-based app at http://emocnet.uniri.hr/.

Realizing the Power of Text Mining and Natural Language Processing for Analyzing Patient Safety Event Narratives: The Challenges and Path Forward.

Patient safety event (PSE) reports are a useful lens to understand hazards and patient safety risks in healthcare systems. However, patient safety officers and analysts in healthcare systems and safety organizations are challenged to make sense of the ever-increasing volume of PSE reports, including the free-text narratives. As a result, there is a growing emphasis on applying text mining and natural language processing (NLP) approaches to assist in the processing and understanding of these narratives. Although text mining and NLP in healthcare have advanced significantly over the past decades, the utility of the resulting models, ontologies, and algorithms to analyze PSE narratives are limited given the unique difference and challenges in content and language between PSE narratives and clinical documentation. To promote the application of text mining and NLP for PSE narratives, these unique challenges must be addressed. Improving data access, developing NLP resources to practically use contributing factor taxonomies, and developing and adopting shared specifications for interoperability will help create an infrastructure and environment that unlocks the collaborative potential between patient safety, research, and machine learning communities, in the development of reproducible and generalizable methods and models to better understand and improve patient safety and patient care.

Building Dictionaries for Low Resource Languages: Challenges of Unsupervised Learning

The development of natural language processing resources for Albanian has grown steadily in recent years. This paper presents research conducted on unsupervised learning-the challenges associated with building a dictionary for the Albanian language and creating part-of-speech tagging models. The majority of languages have their own dictionary, but languages with low resources suffer from a lack of resources. It facilitates the sharing of information and services for users and whole communities through natural language processing. The experimentation corpora for the Albanian language includes 250K sentences from different disciplines, with a proposal for a part-of-speech tagging tag set that can adequately represent the underlying linguistic phenomena. Contributing to the development of Albanian is the purpose of this paper. The results of experiments with the Albanian language corpus revealed that its use of articles and pronouns resembles that of more high-resource languages. According to this study, the total expected frequency as a means for correctly tagging words has been proven effective for populating the Albanian language dictionary.