scholarly journals Developing Core Technologies for Resource-Scarce Nguni Languages

Information ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 520
Author(s):  
Jakobus S. du Toit ◽  
Martin J. Puttkammer
Keyword(s):  
South African ◽  
Language Processing ◽  
Rule Based ◽  
Local Resource ◽  
African Languages ◽  
Linguistic Resources ◽  
Part Of Speech ◽  
Parallel Data ◽  
Further Development

The creation of linguistic resources is crucial to the continued growth of research and development efforts in the field of natural language processing, especially for resource-scarce languages. In this paper, we describe the curation and annotation of corpora and the development of multiple linguistic technologies for four official South African languages, namely isiNdebele, Siswati, isiXhosa, and isiZulu. Development efforts included sourcing parallel data for these languages and annotating each on token, orthographic, morphological, and morphosyntactic levels. These sets were in turn used to create and evaluate three core technologies, viz. a lemmatizer, part-of-speech tagger, morphological analyzer for each of the languages. We report on the quality of these technologies which improve on previously developed rule-based technologies as part of a similar initiative in 2013. These resources are made publicly accessible through a local resource agency with the intention of fostering further development of both resources and technologies that may benefit the NLP industry in South Africa.

scholarly journals A brief study of the Autshumato Machine Translation Web Service for South African languages

Literator ◽  
2021 ◽  
Vol 42 (1) ◽  
Author(s):  
Nomsa J. Skosana ◽  
Respect Mlambo
Keyword(s):  
Web Service ◽  
South African ◽  
Machine Translation ◽  
Language Processing ◽  
High Speed ◽  
Training Data ◽  
Parallel Corpora ◽  
African Languages ◽  
Official Languages

The scarcity of adequate resources for South African languages poses a huge challenge for their functional development in specialised fields such as science and technology. The study examines the Autshumato Machine Translation (MT) Web Service, created by the Centre for Text Technology at the North-West University. This software supports both formal and informal translations as a machine-aided human translation tool. We investigate the system in terms of its advantages and limitations and suggest possible solutions for South African languages. The results show that the system is essential as it offers high-speed translation and operates as an open-source platform. It also provides multiple translations from sentences, documents and web pages. Some South African languages were included whilst others were excluded and we find this to be a limitation of the system. We also find that the system was trained with a limited amount of data, and this has an adverse effect on the quality of the output. The study suggests that adding specialised parallel corpora from various contemporary fields for all official languages and involving language experts in the pre-editing of training data can be a major step towards improving the quality of the system’s output. The study also outlines that developers should consider integrating the system with other natural language processing applications. Finally, the initiatives discussed in this study will help to improve this MT system to be a more effective translation tool for all the official languages of South Africa.

An Automatic Question Generation System using Rule-Based Approach in Bloom’s Taxonomy

2019 ◽  
Vol 13 ◽  
Author(s):  
G Deena ◽  
K Raja ◽  
K Kannan
Keyword(s):  
Language Processing ◽  
Learning Process ◽  
Question Generation ◽  
Test Question ◽  
Rule Based ◽  
Part Of Speech ◽  
Core Idea ◽  
Rule Based Approach ◽  
Teaching Learning ◽  
Automatic Question Generation

: In this competing world, education has become part of everyday life. The process of imparting the knowledge to the learner through education is the core idea in the Teaching-Learning Process (TLP). An assessment is one way to identify the learner’s weak spot of the area under discussion. An assessment question has higher preferences in judging the learner's skill. In manual preparation, the questions are not assured in excellence and fairness to assess the learner’s cognitive skill. Question generation is the most important part of the teaching-learning process. It is clearly understood that generating the test question is the toughest part. Methods: Proposed an Automatic Question Generation (AQG) system which automatically generates the assessment questions dynamically from the input file. Objective: The Proposed system is to generate the test questions that are mapped with blooms taxonomy to determine the learner’s cognitive level. The cloze type questions are generated using the tag part-of-speech and random function. Rule-based approaches and Natural Language Processing (NLP) techniques are implemented to generate the procedural question of the lowest blooms cognitive levels. Analysis: The outputs are dynamic in nature to create a different set of questions at each execution. Here, input paragraph is selected from computer science domain and their output efficiency are measured using the precision and recall.

scholarly journals Multilingual Open Information Extraction: Challenges and Opportunities

Information ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 228 ◽  
Author(s):  
Daniela Barreiro Claro ◽  
Marlo Souza ◽  
Clarissa Castellã Xavier ◽  
Leandro Oliveira
Keyword(s):  
Information Extraction ◽  
Language Processing ◽  
State Of The Art ◽  
Transfer Of Knowledge ◽  
Linguistic Resources ◽  
Open Information Extraction ◽  
General Rules ◽  
Challenges And Opportunities ◽  
Multilingual Approach

The number of documents published on the Web in languages other than English grows every year. As a consequence, the need to extract useful information from different languages increases, highlighting the importance of research into Open Information Extraction (OIE) techniques. Different OIE methods have dealt with features from a unique language; however, few approaches tackle multilingual aspects. In those approaches, multilingualism is restricted to processing text in different languages, rather than exploring cross-linguistic resources, which results in low precision due to the use of general rules. Multilingual methods have been applied to numerous problems in Natural Language Processing, achieving satisfactory results and demonstrating that knowledge acquisition for a language can be transferred to other languages to improve the quality of the facts extracted. We argue that a multilingual approach can enhance OIE methods as it is ideal to evaluate and compare OIE systems, and therefore can be applied to the collected facts. In this work, we discuss how the transfer knowledge between languages can increase acquisition from multilingual approaches. We provide a roadmap of the Multilingual Open IE area concerning state of the art studies. Additionally, we evaluate the transfer of knowledge to improve the quality of the facts extracted in each language. Moreover, we discuss the importance of a parallel corpus to evaluate and compare multilingual systems.

scholarly journals PENENTUAN KELAS KATA PADA PART OF SPEECH TAGGING KATA AMBIGU BAHASA INDONESIA

2018 ◽  
Vol 2 (3) ◽  
pp. 157
Author(s):  
Ahmad Subhan Yazid ◽  
Agung Fatwanto
Keyword(s):  
Language Processing ◽  
Word Class ◽  
Rule Based ◽  
Part Of Speech Tagging ◽  
Pos Tagging ◽  
Part Of Speech ◽  
Ambiguous Words ◽  
Computer Science Faculty ◽  
Speech Tagging ◽  
Bahasa Indonesia

Indonesian hold a fundamental role in the communication. There is ambiguous problem in its machine learning implementation. In the Natural Language Processing study, Part of Speech (POS) tagging has a role in the decreasing this problem. This study use the Rule Based method to determine the best word class for ambiguous words in Indonesian. This research follows some stages: knowledge inventory, making algorithms, implementation, Testing, Analysis, and Conclusions. The first data used is Indonesian corpus that was developed by Language department of Computer science Faculty, Indonesia University. Then, data is processed and shown descriptively by following certain rules and specification. The result is a POS tagging algorithm included 71 rules in flowchart and descriptive sentence notation. Refer to testing result, the algorithm successfully provides 92 labeling of 100 tested words (92%). The results of the implementation are influenced by the availability of rules, word class tagsets and corpus data.

scholarly journals RussianLanguage Thesauri: Automated Construction and Application For Natural Language Processing Tasks

2018 ◽  
Vol 25 (4) ◽  
pp. 435-458
Author(s):  
Nadezhda S. Lagutina ◽  
Ksenia V. Lagutina ◽  
Aleksey S. Adrianov ◽  
Ilya V. Paramonov
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Russian Language ◽  
Labor Costs ◽  
Linguistic Resources ◽  
Advantages And Disadvantages ◽  
Text Corpora ◽  
Linguistic Methods

The paper reviews the existing Russian-language thesauri in digital form and methods of their automatic construction and application. The authors analyzed the main characteristics of open access thesauri for scientific research, evaluated trends of their development, and their effectiveness in solving natural language processing tasks. The statistical and linguistic methods of thesaurus construction that allow to automate the development and reduce labor costs of expert linguists were studied. In particular, the authors considered algorithms for extracting keywords and semantic thesaurus relationships of all types, as well as the quality of thesauri generated with the use of these tools. To illustrate features of various methods for constructing thesaurus relationships, the authors developed a combined method that generates a specialized thesaurus fully automatically taking into account a text corpus in a particular domain and several existing linguistic resources. With the proposed method, experiments were conducted with two Russian-language text corpora from two subject areas: articles about migrants and tweets. The resulting thesauri were assessed by using an integrated assessment developed in the previous authors’ study that allows to analyze various aspects of the thesaurus and the quality of the generation methods. The analysis revealed the main advantages and disadvantages of various approaches to the construction of thesauri and the extraction of semantic relationships of different types, as well as made it possible to determine directions for future study.

scholarly journals Question Generator System of Sentence Completion in TOEFL Using NLP and K-Nearest Neighbor

2019 ◽  
Vol 4 (2) ◽  
pp. 294-311
Author(s):  
Lala Septem Riza ◽  
Anita Dyah Pertiwi ◽  
Eka Fitrajaya Rahman ◽  
Munir Munir ◽  
Cep Ubad Abdullah
Keyword(s):  
Language Processing ◽  
Nearest Neighbor ◽  
Sentence Completion ◽  
K Nearest Neighbor ◽  
Generator System ◽  
English Grammar ◽  
Pos Tagging ◽  
Part Of Speech ◽  
Foreign Media

Test of English as a Foreign Language (TOEFL) is one of learning evaluation forms that requires excellent quality of questions. Preparing TOEFL questions using a conventional way certainly spends a lot of time. Computer technology can be used to solve the problem. Therefore, this research was conducted in order to solve the problem of making TOEFL questions with sentence completion type. The built system consists of several stages: (1) input data collection from foreign media news sites with excellent English grammar quality; (2) preprocessing with Natural Language Processing (NLP); (3) Part of Speech (POS) tagging; (4) question feature extraction; (5) separation and selection of news sentences; (6) determination and value collection of seven features; (7) conversion of categorical data value; (8) target classification of blank position word with K-Nearest Neighbor (KNN); (9) heuristic determination of rules from human experts; and (10) options selection or distraction based on heuristic rules. After conducting the experiment on 10 news, it is obtained that 20 questions based on the results of the evaluation showed that the generated questions had a very good quality with percentage of 81.93% (after the assessment by the human expert), and 70% was the same blank position from the historical data of TOEFL questions. So, it can be concluded that the generated question has the following characteristics: the quality of the result follows the data training from the historical TOEFL questions, and the quality of the distraction is very good because it is derived from the heuristics of human experts.

Punjabi Pos Tagger: Rule Based and HMM

2017 ◽  
Vol 7 (7) ◽  
pp. 193
Author(s):  
Umrinderpal Singh ◽  
Vishal Goyal
Keyword(s):  
Information Retrieval ◽  
Language Processing ◽  
State Of The Art ◽  
Input Word ◽  
Rule Based ◽  
Part Of Speech Tagging ◽  
Part Of Speech ◽  
Unseen Data ◽  
Pos Tagger ◽  
Speech Tagging

The Part of Speech tagger system is used to assign a tag to every input word in a given sentence. The tags may include different part of speech tag for a particular language like noun, pronoun, verb, adjective, conjunction etc. and may have subcategories of all these tags. Part of Speech tagging is a basic and a preprocessing task of most of the Natural Language Processing (NLP) applications such as Information Retrieval, Machine Translation, and Grammar Checking etc. The task belongs to a larger set of problems, namely, sequence labeling problems. Part of Speech tagging for Punjabi is not widely explored territory. We have discussed Rule Based and HMM based Part of Speech tagger for Punjabi along with the comparison of their accuracies of both approaches. The System is developed using 35 different standard part of speech tag. We evaluate our system on unseen data with state-of-the-art accuracy 93.3%.

scholarly journals Viability of Neural Networks for Core Technologies for Resource-Scarce Languages

Information ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 41
Author(s):  
Melinda Loubser ◽  
Martin J. Puttkammer
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
South African ◽  
Language Processing ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Neural Network Models ◽  
African Languages ◽  
Pos Tagging

In this paper, the viability of neural network implementations of core technologies (the focus of this paper is on text technologies) for 10 resource-scarce South African languages is evaluated. Neural networks are increasingly being used in place of other machine learning methods for many natural language processing tasks with good results. However, in the South African context, where most languages are resource-scarce, very little research has been done on neural network implementations of core language technologies. In this paper, we address this gap by evaluating neural network implementations of four core technologies for ten South African languages. The technologies we address are part of speech tagging, named entity recognition, compound analysis and lemmatization. Neural architectures that performed well on similar tasks in other settings were implemented for each task and the performance was assessed in comparison with currently used machine learning implementations of each technology. The neural network models evaluated perform better than the baselines for compound analysis, are viable and comparable to the baseline on most languages for POS tagging and NER, and are viable, but not on par with the baseline, for Afrikaans lemmatization.

Interpreting Design Structure in Patents Using an Ontology Library

2013 ◽  
Author(s):  
Zhen Li ◽  
Derrick Tate
Keyword(s):  
Language Processing ◽  
Computational Approach ◽  
Data Sources ◽  
Text Data ◽  
Ontological Engineering ◽  
Rule Based ◽  
Part Of Speech ◽  
Design Structure ◽  
Tree Generation ◽  
Patent Documents

Patents contain valuable information for engineering design. However, the increasing number of annual patent publications makes it difficult for any individual designer to assimilate all up-to-date knowledge hidden in patent documents. In this paper, we proposed a computational approach to interpret design structure embedded in patent claims using pre-developed ontology libraries. The study combined natural language processing (NLP) techniques, text data-mining, ontological engineering, and our rule-based tree generation method. Data sources and adopted tools included online patent documents, knowledge gathered from engineering textbooks, WordNet, a part-of-speech tagger developed by the Stanford NLP group, and Graphviz. We showed that the framework proposed in the paper not only could help minimize manual work required for obtaining design structures but also enable automatic dissimilarity comparison between patents.

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