scholarly journals Automatic Multilingual Stopwords Identification from Very Small Corpora

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2169
Author(s):  
Stefano Ferilli
Keyword(s):  
Natural Language Processing ◽  
Language Processing ◽  
State Of The Art ◽  
Linguistic Knowledge ◽  
Linguistic Resources ◽  
Novel Approach ◽  
Document Frequency ◽  
Critical Problems ◽  
Fully Automatic ◽  
Local Languages

Tools for Natural Language Processing work using linguistic resources, that are language-specific. The complexity of building such resources causes many languages to lack them. So, learning them automatically from sample texts would be a desirable solution. This usually requires huge training corpora, which are not available for many local languages and jargons, lacking a wide literature. This paper focuses on stopwords, i.e., terms in a text which do not contribute in conveying its topic or content. It provides two main, inter-related and complementary, methodological contributions: (i) it proposes a novel approach based on term and document frequency to rank candidate stopwords, that works also on very small corpora (even single documents); and (ii) it proposes an automatic cutoff strategy to select the best candidates in the ranking, thus addressing one of the most critical problems in the stopword identification practice. Nice features of these approaches are that (i) they are generic and applicable to different languages, (ii) they are fully automatic, and (iii) they do not require any previous linguistic knowledge. Extensive experiments show that both are extremely effective and reliable. The former outperforms all comparable approaches in the state-of-the-art, both in terms of performance (Precision stays at 100% or nearly so for a large portion of the top-ranked candidate stopwords, while Recall is quite close to the maximum reachable in theory.) and in smooth behavior (Precision is monotonically decreasing, and Recall is monotonically increasing, allowing the experimenter to choose the preferred balance.). The latter is more flexible than existing solutions in the literature, requiring just one parameter intuitively related to the balance between Precision and Recall one wishes to obtain.

Feature-based natural language processing for GSL synthesis

2007 ◽  
Vol 10 (1) ◽  
pp. 3-23 ◽  
Author(s):  
Eleni Efthimiou ◽  
Stavroula-Evita Fotinea ◽  
Galini Sapountzaki
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Linguistic Knowledge ◽  
Specific Information ◽  
Grammar Teaching ◽  
Linguistic Resources ◽  
Sign Synthesis ◽  
Input Strings ◽  
Structure Patterns

The work reported in this study is based on research that has been carried out while developing a sign synthesis system for Greek Sign Language (GSL): theoretical linguistic analysis as well as lexicon and grammar resources derived from this analysis. We focus on the organisation of linguistic knowledge that initiates the multi-functional processing required to achieve sign generation performed by a virtual signer. In this context, structure rules and lexical coding support sign synthesis of GSL utterances, by exploitation of avatar technologies for the representation of the linguistic message. Sign generation involves two subsystems: a Greek-to-GSL conversion subsystem and a sign performance subsystem. The conversion subsystem matches input strings of written Greek-to-GSL structure patterns, exploiting Natural Language Processing (NLP) mechanisms. The sign performance subsystem uses parsed output of GSL structure patterns, enriched with sign-specific information, to activate a virtual signer for the performance of properly coded linguistic messages. Both the conversion and the synthesis procedure are based on adequately constructed electronic linguistic resources. Applicability of sign synthesis is demonstrated with the example of a Web-based prototype environment for GSL grammar teaching.

A Machine Learning Application for Raising WASH Awareness in the Times of COVID-19 Pandemic (Preprint)

2020 ◽  
Author(s):  
Rohan Pandey ◽  
Vaibhav Gautam ◽  
Ridam Pal ◽  
Harsh Bandhey ◽  
Lovedeep Singh Dhingra ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
User Feedback ◽  
Who Guidelines ◽  
The Times ◽  
The Right ◽  
Local Languages

BACKGROUND The COVID-19 pandemic has uncovered the potential of digital misinformation in shaping the health of nations. The deluge of unverified information that spreads faster than the epidemic itself is an unprecedented phenomenon that has put millions of lives in danger. Mitigating this ‘Infodemic’ requires strong health messaging systems that are engaging, vernacular, scalable, effective and continuously learn the new patterns of misinformation. OBJECTIVE We created WashKaro, a multi-pronged intervention for mitigating misinformation through conversational AI, machine translation and natural language processing. WashKaro provides the right information matched against WHO guidelines through AI, and delivers it in the right format in local languages. METHODS We theorize (i) an NLP based AI engine that could continuously incorporate user feedback to improve relevance of information, (ii) bite sized audio in the local language to improve penetrance in a country with skewed gender literacy ratios, and (iii) conversational but interactive AI engagement with users towards an increased health awareness in the community. RESULTS A total of 5026 people who downloaded the app during the study window, among those 1545 were active users. Our study shows that 3.4 times more females engaged with the App in Hindi as compared to males, the relevance of AI-filtered news content doubled within 45 days of continuous machine learning, and the prudence of integrated AI chatbot “Satya” increased thus proving the usefulness of an mHealth platform to mitigate health misinformation. CONCLUSIONS We conclude that a multi-pronged machine learning application delivering vernacular bite-sized audios and conversational AI is an effective approach to mitigate health misinformation. CLINICALTRIAL Not Applicable

Report on the 4th Joint Workshop on Bibliometric-Enhanced Information Retrieval and Natural Language Processing for Digital Libraries at SIGIR 2019

2019 ◽  
Vol 53 (2) ◽  
pp. 3-10
Author(s):  
Muthu Kumar Chandrasekaran ◽  
Philipp Mayr
Keyword(s):  
Information Retrieval ◽  
Natural Language Processing ◽  
Natural Language ◽  
Research And Development ◽  
Language Processing ◽  
Digital Libraries ◽  
State Of The Art ◽  
Shared Task ◽  
Processing Information ◽  
Joint Workshop

The 4 th joint BIRNDL workshop was held at the 42nd ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2019) in Paris, France. BIRNDL 2019 intended to stimulate IR researchers and digital library professionals to elaborate on new approaches in natural language processing, information retrieval, scientometrics, and recommendation techniques that can advance the state-of-the-art in scholarly document understanding, analysis, and retrieval at scale. The workshop incorporated different paper sessions and the 5 th edition of the CL-SciSumm Shared Task.

scholarly journals Building natural language processing tools for Runyakitara

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Fridah Katushemererwe ◽  
Andrew Caines ◽  
Paula Buttery
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Learning ◽  
Language Processing ◽  
Primary Data ◽  
Computer Assisted ◽  
Endangered Languages ◽  
Test Case ◽  
Short Supply ◽  
Linguistic Resources

AbstractThis paper describes an endeavour to build natural language processing (NLP) tools for Runyakitara, a group of four closely related Bantu languages spoken in western Uganda. In contrast with major world languages such as English, for which corpora are comparatively abundant and NLP tools are well developed, computational linguistic resources for Runyakitara are in short supply. First therefore, we need to collect corpora for these languages, before we can proceed to the design of a spell-checker, grammar-checker and applications for computer-assisted language learning (CALL). We explain how we are collecting primary data for a new Runya Corpus of speech and writing, we outline the design of a morphological analyser, and discuss how we can use these new resources to build NLP tools. We are initially working with Runyankore–Rukiga, a closely-related pair of Runyakitara languages, and we frame our project in the context of NLP for low-resource languages, as well as CALL for the preservation of endangered languages. We put our project forward as a test case for the revitalization of endangered languages through education and technology.

Textual entailment graphs

2015 ◽  
Vol 21 (5) ◽  
pp. 699-724 ◽  
Author(s):  
LILI KOTLERMAN ◽  
IDO DAGAN ◽  
BERNARDO MAGNINI ◽  
LUISA BENTIVOGLI
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Gold Standard ◽  
State Of The Art ◽  
Text Analytics ◽  
Joint Work ◽  
Gold Standard Dataset ◽  
Textual Entailment ◽  
Interesting Task

AbstractIn this work, we present a novel type of graphs for natural language processing (NLP), namely textual entailment graphs (TEGs). We describe the complete methodology we developed for the construction of such graphs and provide some baselines for this task by evaluating relevant state-of-the-art technology. We situate our research in the context of text exploration, since it was motivated by joint work with industrial partners in the text analytics area. Accordingly, we present our motivating scenario and the first gold-standard dataset of TEGs. However, while our own motivation and the dataset focus on the text exploration setting, we suggest that TEGs can have different usages and suggest that automatic creation of such graphs is an interesting task for the community.

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 Using Natural Language Processing and Artificial Intelligence to Explore the Nutrition and Sustainability of Recipes and Food

2021 ◽  
Vol 3 ◽  
Author(s):  
Marieke van Erp ◽  
Christian Reynolds ◽  
Diana Maynard ◽  
Alain Starke ◽  
Rebeca Ibáñez Martín ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Domain Knowledge ◽  
State Of The Art ◽  
Interdisciplinary Approach ◽  
Comprehensive Analysis ◽  
The State ◽  
Use Cases

In this paper, we discuss the use of natural language processing and artificial intelligence to analyze nutritional and sustainability aspects of recipes and food. We present the state-of-the-art and some use cases, followed by a discussion of challenges. Our perspective on addressing these is that while they typically have a technical nature, they nevertheless require an interdisciplinary approach combining natural language processing and artificial intelligence with expert domain knowledge to create practical tools and comprehensive analysis for the food domain.

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