scholarly journals Development for performance of Porter stemmer algorithm

2021 ◽  
Vol 1 (2 (109)) ◽  
pp. 6-13
Author(s):  
Manhal Elias Polus ◽  
Thekra Abbas
Keyword(s):  
Language Processing ◽  
Text Classification ◽  
Main Part ◽  
Text Processing ◽  
Information Access ◽  
Relevant Information ◽  
Accurate Information ◽  
Specific Knowledge ◽  
Processing Stage ◽  
Linguistic Process

The Porter stemmer algorithm is a broadly used, however, an essential tool for natural language processing in the area of information access. Stemming is used to remove words that add the final morphological and diacritical endings of words in English words to their root form to extract the word root, i.e. called stem/root in the primary text processing stage. In other words, it is a linguistic process that simply extracts the main part that may be close to the relative and related root. Text classification is a major task in extracting relevant information from a large volume of data. In this paper, we suggest ways to improve a version of the Porter algorithm with the aim of processing and overcome its limitations and to save time and memory by reducing the size of the words. The system uses the improved Porter derivation technique for word pruning. Whereas performs cognitive-inspired computing to discover morphologically related words from the corpus without any human intervention or language-specific knowledge. The improved Porter algorithm is compared to the original stemmer. The improved Porter algorithm has better performance and enables more accurate information retrieval (IR).

scholarly journals A Web Service for Biomedical Term Look-Up

2005 ◽  
Vol 6 (1-2) ◽  
pp. 86-93 ◽  
Author(s):  
Henk Harkema ◽  
Ian Roberts ◽  
Rob Gaizauskas ◽  
Mark Hepple
Keyword(s):  
Natural Language ◽  
Web Service ◽  
Language Processing ◽  
Large Scale ◽  
Information Sources ◽  
Text Processing ◽  
Processing System ◽  
Relevant Information ◽  
Automatic Identification ◽  
Efficient Manner

Recent years have seen a huge increase in the amount of biomedical information that is available in electronic format. Consequently, for biomedical researchers wishing to relate their experimental results to relevant data lurking somewhere within this expanding universe of on-line information, the ability to access and navigate biomedical information sources in an efficient manner has become increasingly important. Natural language and text processing techniques can facilitate this task by making the information contained in textual resources such as MEDLINE more readily accessible and amenable to computational processing. Names of biological entities such as genes and proteins provide critical links between different biomedical information sources and researchers' experimental data. Therefore, automatic identification and classification of these terms in text is an essential capability of any natural language processing system aimed at managing the wealth of biomedical information that is available electronically. To support term recognition in the biomedical domain, we have developed Termino, a large-scale terminological resource for text processing applications, which has two main components: first, a database into which very large numbers of terms can be loaded from resources such as UMLS, and stored together with various kinds of relevant information; second, a finite state recognizer, for fast and efficient identification and mark-up of terms within text. Since many biomedical applications require this functionality, we have made Termino available to the community as a web service, which allows for its integration into larger applications as a remotely located component, accessed through a standardized interface over the web.

scholarly journals Vietnamese Text Classification Algorithm using Long Short Term Memory and Word2Vec

2020 ◽  
Vol 19 (6) ◽  
pp. 1255-1279
Author(s):  
Huu Nguyen Phat ◽  
Nguyen Thi Minh Anh
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Text Classification ◽  
Industrial Revolution ◽  
Short Term Memory ◽  
Text Processing ◽  
Research Effort ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

In the context of the ongoing forth industrial revolution and fast computer science development the amount of textual information becomes huge. So, prior to applying the seemingly appropriate methodologies and techniques to the above data processing their nature and characteristics should be thoroughly analyzed and understood. At that, automatic text processing incorporated in the existing systems may facilitate many procedures. So far, text classification is one of the basic applications to natural language processing accounting for such factors as emotions’ analysis, subject labeling etc. In particular, the existing advancements in deep learning networks demonstrate that the proposed methods may fit the documents’ classifying, since they possess certain extra efficiency; for instance, they appeared to be effective for classifying texts in English. The thorough study revealed that practically no research effort was put into an expertise of the documents in Vietnamese language. In the scope of our study, there is not much research for documents in Vietnamese. The development of deep learning models for document classification has demonstrated certain improvements for texts in Vietnamese. Therefore, the use of long short term memory network with Word2vec is proposed to classify text that improves both performance and accuracy. The here developed approach when compared with other traditional methods demonstrated somewhat better results at classifying texts in Vietnamese language. The evaluation made over datasets in Vietnamese shows an accuracy of over 90%; also the proposed approach looks quite promising for real applications.

Multilingual Information Access

2013 ◽  
pp. 203-228
Author(s):  
Víctor Peinado ◽  
Álvaro Rodrigo ◽  
Fernando López-Ostenero
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Information Access ◽  
Relevant Information ◽  
Heterogeneous Information ◽  
Linguistic Resources ◽  
Translation Tools ◽  
Heterogeneous Information Sources ◽  
Cross Language ◽  
Multiple Languages

This chapter focuses on Multilingual Information Access (MLIA), a multidisciplinary area that aims to solve accessing, querying, and retrieving information from heterogeneous information sources expressed in different languages. Current Information Retrieval technology, combined with Natural Language Processing tools allows building systems able to efficiently retrieve relevant information and, to some extent, to provide concrete answers to questions expressed in natural language. Besides, when linguistic resources and translation tools are available, cross-language information systems can assist to find information in multiple languages. Nevertheless, little is still known about how to properly assist people to find and use information expressed in unknown languages. Approaches proved as useful for automatic systems seem not to match with real user’s needs.

scholarly journals Sentiment Analysis Techniques Applied to Raw-Text Data from a Csq-8 Questionnaire about Mindfulness in Times of COVID-19 to Improve Strategy Generation

2021 ◽  
Vol 18 (12) ◽  
pp. 6408
Author(s):  
Mario Jojoa Acosta ◽  
Gema Castillo-Sánchez ◽  
Begonya Garcia-Zapirain ◽  
Isabel de la Torre Díez ◽  
Manuel Franco-Martín
Keyword(s):  
Health Care ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Language Processing ◽  
Health Care Professionals ◽  
Ground Truth ◽  
Relevant Information ◽  
Free Text

The use of artificial intelligence in health care has grown quickly. In this sense, we present our work related to the application of Natural Language Processing techniques, as a tool to analyze the sentiment perception of users who answered two questions from the CSQ-8 questionnaires with raw Spanish free-text. Their responses are related to mindfulness, which is a novel technique used to control stress and anxiety caused by different factors in daily life. As such, we proposed an online course where this method was applied in order to improve the quality of life of health care professionals in COVID 19 pandemic times. We also carried out an evaluation of the satisfaction level of the participants involved, with a view to establishing strategies to improve future experiences. To automatically perform this task, we used Natural Language Processing (NLP) models such as swivel embedding, neural networks, and transfer learning, so as to classify the inputs into the following three categories: negative, neutral, and positive. Due to the limited amount of data available—86 registers for the first and 68 for the second—transfer learning techniques were required. The length of the text had no limit from the user’s standpoint, and our approach attained a maximum accuracy of 93.02% and 90.53%, respectively, based on ground truth labeled by three experts. Finally, we proposed a complementary analysis, using computer graphic text representation based on word frequency, to help researchers identify relevant information about the opinions with an objective approach to sentiment. The main conclusion drawn from this work is that the application of NLP techniques in small amounts of data using transfer learning is able to obtain enough accuracy in sentiment analysis and text classification stages.

AI Applications to the Law Domain in Japan

2020 ◽  
Vol 7 (3) ◽  
pp. 471-494
Author(s):  
Katsumi NITTA ◽  
Ken SATOH
Keyword(s):  
Expert Systems ◽  
Information And Communication Technologies ◽  
Language Processing ◽  
Text Processing ◽  
Research Area ◽  
Legal Information ◽  
Research Activities ◽  
Legal Expert ◽  
Legal Domain ◽  
Legal Expert Systems

AbstractArtificial intelligence (AI) and law is an AI research area that has a history spanning more than 50 years. In the early stages, several legal-expert systems were developed. Legal-expert systems are tools designed to realize fair judgments in court. In addition to this research, as information and communication technologies and AI technologies have progressed, AI and law has broadened its view from legal-expert systems to legal analytics and, recently, a lot of machine-learning and text-processing techniques have been employed to analyze legal information. The research trends are the same in Japan as well and not only people involved with legal-expert systems, but also those involved with natural language processing as well as lawyers have become interested in AI and law. This report introduces the history of and the research activities on applying AI to the legal domain in Japan.

scholarly journals A Survey of Arabic Named Entity Recognition and Classification

2014 ◽  
Vol 40 (2) ◽  
pp. 469-510 ◽  
Author(s):  
Khaled Shaalan
Keyword(s):  
Language Processing ◽  
Named Entity Recognition ◽  
Relevant Information ◽  
Arabic Language ◽  
Entity Recognition ◽  
Named Entities ◽  
Linguistic Resources ◽  
Named Entity ◽  
To Receive ◽  
Made In

As more and more Arabic textual information becomes available through the Web in homes and businesses, via Internet and Intranet services, there is an urgent need for technologies and tools to process the relevant information. Named Entity Recognition (NER) is an Information Extraction task that has become an integral part of many other Natural Language Processing (NLP) tasks, such as Machine Translation and Information Retrieval. Arabic NER has begun to receive attention in recent years. The characteristics and peculiarities of Arabic, a member of the Semitic languages family, make dealing with NER a challenge. The performance of an Arabic NER component affects the overall performance of the NLP system in a positive manner. This article attempts to describe and detail the recent increase in interest and progress made in Arabic NER research. The importance of the NER task is demonstrated, the main characteristics of the Arabic language are highlighted, and the aspects of standardization in annotating named entities are illustrated. Moreover, the different Arabic linguistic resources are presented and the approaches used in Arabic NER field are explained. The features of common tools used in Arabic NER are described, and standard evaluation metrics are illustrated. In addition, a review of the state of the art of Arabic NER research is discussed. Finally, we present our conclusions. Throughout the presentation, illustrative examples are used for clarification.

scholarly journals Interactive use of online health resources: a comparison of consumer and professional questions

2016 ◽  
Vol 23 (4) ◽  
pp. 802-811 ◽  
Author(s):  
Kirk Roberts ◽  
Dina Demner-Fushman
Keyword(s):  
Language Processing ◽  
Information Access ◽  
Health Resources ◽  
Background Information ◽  
Special Focus ◽  
Health And Wellness ◽  
Medical Problems ◽  
Online Resource ◽  
The Individual ◽  
Processing Techniques

Abstract Objective To understand how consumer questions on online resources differ from questions asked by professionals, and how such consumer questions differ across resources. Materials and Methods Ten online question corpora, 5 consumer and 5 professional, with a combined total of over 40 000 questions, were analyzed using a variety of natural language processing techniques. These techniques analyze questions at the lexical, syntactic, and semantic levels, exposing differences in both form and content. Results Consumer questions tend to be longer than professional questions, more closely resemble open-domain language, and focus far more on medical problems. Consumers ask more sub-questions, provide far more background information, and ask different types of questions than professionals. Furthermore, there is substantial variance of these factors between the different consumer corpora. Discussion The form of consumer questions is highly dependent upon the individual online resource, especially in the amount of background information provided. Professionals, on the other hand, provide very little background information and often ask much shorter questions. The content of consumer questions is also highly dependent upon the resource. While professional questions commonly discuss treatments and tests, consumer questions focus disproportionately on symptoms and diseases. Further, consumers place far more emphasis on certain types of health problems (eg, sexual health). Conclusion Websites for consumers to submit health questions are a popular online resource filling important gaps in consumer health information. By analyzing how consumers write questions on these resources, we can better understand these gaps and create solutions for improving information access. This article is part of the Special Focus on Person-Generated Health and Wellness Data, which published in the May 2016 issue, Volume 23, Issue 3.

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