Introducing various Semantic Models for Amharic: Experimentation and Evaluation with multiple Tasks and Datasets

The availability of different pre-trained semantic models enabled the quick development of machine learning components for downstream applications. Despite the availability of abundant text data for low resource languages, only a few semantic models are publicly available. Publicly available pre-trained models are usually built as a multilingual version of semantic models that can not fit well for each language due to context variations. In this work, we introduce different semantic models for Amharic. After we experiment with the existing pre-trained semantic models, we trained and fine-tuned nine new different models using a monolingual text corpus. The models are build using word2Vec embeddings, distributional thesaurus (DT), contextual embeddings, and DT embeddings obtained via network embedding algorithms. Moreover, we employ these models for different NLP tasks and investigate their impact. We find that newly trained models perform better than pre-trained multilingual models. Furthermore, models based on contextual embeddings from RoBERTA perform better than the word2Vec models

AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages

Africa is home to over 2000 languages from over six language families and has the highest linguistic diversity among all continents. This includes 75 languages with at least one million speakers each. Yet, there is little NLP research conducted on African languages. Crucial in enabling such research is the availability of high-quality annotated datasets. In this paper, we introduce AfriSenti, which consists of 14 sentiment datasets of 110,000+ tweets in 14 African languages (Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili, Tigrinya, Twi, Xitsonga, and Yor\`ub\'a) from four language families annotated by native speakers. The data is used in SemEval 2023 Task 12, the first Afro-centric SemEval shared task. We describe the data collection methodology, annotation process, and related challenges when curating each of the datasets. We conduct experiments with different sentiment classification baselines and discuss their usefulness. We hope AfriSenti enables new work on under-represented languages. The dataset is available at https://github.com/afrisenti-semeval/afrisent-semeval-2023 and can also be loaded as a huggingface datasets (https://huggingface.co/datasets/shmuhammad/AfriSenti).Comment: 15 pages, 6 Figures, 9 Table

Cross-Lingual and Low-Resource Sentiment Analysis

Identifying sentiment in a low-resource language is essential for understanding opinions internationally and for responding to the urgent needs of locals affected by disaster incidents in different world regions. While tools and resources for recognizing sentiment in high-resource languages are plentiful, determining the most effective methods for achieving this task in a low-resource language which lacks annotated data is still an open research question. Most existing approaches for cross-lingual sentiment analysis to date have relied on high-resource machine translation systems, large amounts of parallel data, or resources only available for Indo-European languages. This work presents methods, resources, and strategies for identifying sentiment cross-lingually in a low-resource language. We introduce a cross-lingual sentiment model which can be trained on a high-resource language and applied directly to a low-resource language. The model offers the feature of lexicalizing the training data using a bilingual dictionary, but can perform well without any translation into the target language. Through an extensive experimental analysis, evaluated on 17 target languages, we show that the model performs well with bilingual word vectors pre-trained on an appropriate translation corpus. We compare in-genre and in-domain parallel corpora, out-of-domain parallel corpora, in-domain comparable corpora, and monolingual corpora, and show that a relatively small, in-domain parallel corpus works best as a transfer medium if it is available. We describe the conditions under which other resources and embedding generation methods are successful, and these include our strategies for leveraging in-domain comparable corpora for cross-lingual sentiment analysis. To enhance the ability of the cross-lingual model to identify sentiment in the target language, we present new feature representations for sentiment analysis that are incorporated in the cross-lingual model: bilingual sentiment embeddings that are used to create bilingual sentiment scores, and a method for updating the sentiment embeddings during training by lexicalization of the target language. This feature configuration works best for the largest number of target languages in both untargeted and targeted cross-lingual sentiment experiments. The cross-lingual model is studied further by evaluating the role of the source language, which has traditionally been assumed to be English. We build cross-lingual models using 15 source languages, including two non-European and non-Indo-European source languages: Arabic and Chinese. We show that language families play an important role in the performance of the model, as does the morphological complexity of the source language. In the last part of the work, we focus on sentiment analysis towards targets. We study Arabic as a representative morphologically complex language and develop models and morphological representation features for identifying entity targets and sentiment expressed towards them in Arabic open-domain text. Finally, we adapt our cross-lingual sentiment models for the detection of sentiment towards targets. Through cross-lingual experiments on Arabic and English, we demonstrate that our findings regarding resources, features, and language also hold true for the transfer of targeted sentiment

Dread Talk: The Rastafarians\u27 Linguistic Response to Societal Oppression

Opposed to the repressive socio-economic political climate that resulted in the impoverishment of masses of Jamaicans, the Jamaican Rastafarians developed a language to resist societal oppression. This study examines that language--Dread Talk--as resistive language. Having determined that the other variations spoken in their community--Standard Jamaican English and Jamaican Creole--were inadequate to express their dispossessed circumstances, the Rastafarians forged an identity through their language that represents a resistant philosophy, music and religion. This resistance not only articulates their socio-political state, but also commands global attention. This study scrutinizes the lexical, phonological, and syntactical structures of the poetic music discourse of Dread Talk, the conscious deliberate fashioning of a language that purposefully expresses resistance to the political and social ideology of their native land, Jamaica

Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing

Linguistic typology aims to capture structural and semantic variation across the world's languages. A large-scale typology could provide excellent guidance for multilingual Natural Language Processing (NLP), particularly for languages that suffer from the lack of human labeled resources. We present an extensive literature survey on the use of typological information in the development of NLP techniques. Our survey demonstrates that to date, the use of information in existing typological databases has resulted in consistent but modest improvements in system performance. We show that this is due to both intrinsic limitations of databases (in terms of coverage and feature granularity) and under-employment of the typological features included in them. We advocate for a new approach that adapts the broad and discrete nature of typological categories to the contextual and continuous nature of machine learning algorithms used in contemporary NLP. In particular, we suggest that such approach could be facilitated by recent developments in data-driven induction of typological knowledge

Creation and importance of language corps in Uzbekistan

The article discusses the transformation of language into the language of the Internet, computer technology, mathematical linguistics, its continuation and the formation and development of computer linguistics, in particular the question of modeling natural languages for artificial intelligence. The Uzbek National Corps plays an important role in enhancing the international status of the Uzbek language. The work carried out in the field of computer linguistics plays an important role in resolving existing problems in the Uzbek language. The question of the linguistic and extralinguistic separation of special tags for marking texts and their components is studied in particular.The coding requirements for important text information are defined. The state analyzes the linguistic module and the algorithm and its types from independent components of the linguistic program code. The need for algorithms for phonological, morphological and spelling rules for the formation of the lexical and grammatical code is scientifically substantiated. The importance of such linguistic modules as phonology, morphology and spelling in the formation of the linguistic base of the national corpus of the Uzbek language is emphasized. The article examines the corpus’s primary purpose as a complex linguistic source, as well as the fact that it primarily contains two sorts of information and its types. The key effective capabilities of the corpus, according to the paper, are reducing time spent on the text analysis process and being able to explain the properties of language units in speech with thousands of instances. The national corpus, the educational corpus, and the parallel corpus are all discussed in the subject of computer linguistics. It was stressed that linguistic and extralinguistic tagging of them, the development of corpus formation algorithms, and the establishment of corpus linguistic support are all societal need. It recognizes the urgency of developing the basis for the creation of the Uzbek language corpus, conducting research in the field of computer linguistics as a scientific and theoretical source