An Information-Extraction System for Urdu---A Resource-Poor Language

Mukund, Smruthi; Srihari, Rohini K.; Peterson, Erik

doi:10.1145/1838751.1838754

Cited by 50 publications

(46 citation statements)

References 43 publications

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“…Figure 1 illustrates the numbers of percentage for each language beside English used in evaluated studies. For instance, studies of [5] [17], Arabic [11], Vietnamese [33], and Akkadian [35]. However, other studies [13] [29][22] applied sentence segmentation to analyse Japanese, Greek and Malay languages accordingly.…”

Section: Review Methodsmentioning

confidence: 99%

Text Segmentation for Analysing Different Languages

Pak¹,

Teh²

2017

Proceedings of the First EAI International Conference on Computer Science and Engineering

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Abstract. Over the past several years, researchers have applied different methods of text segmentation. Text segmentation is defined as a method of splitting a document into smaller segments, assuming with its own relevant meaning. Those segments can be classified into the tag, word, sentence, topic, phrase and any information unit. Firstly, this study reviews the different types of text segmentation methods used in different types of documentation, and later discusses the various reasons for utilizing it in opinion mining. The main contribution of this study includes a summarisation of research papers from the past 10 years that applied text segmentation as their main approach in text analysing. Results show that word segmentation was successfully and widely used for processing different languages.

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Section: Review Methodsmentioning

confidence: 99%

Text Segmentation for Analysing Different Languages

Pak¹,

Teh²

2017

Proceedings of the First EAI International Conference on Computer Science and Engineering

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“…However, POS tagger is a basic tool for various applications in NLP field such as information retrieval (IR), information extraction (IE), etc. Moreover, POS tagger is necessary as a tool to build up any language corpus [19,20].…”

Section: Part Of Speech (Pos)mentioning

confidence: 99%

Template based Medical Reports Summarization

El-Qumsan¹,

El-Halees²

2018

IJCA

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The torrential information in the medical records is considered a great problem because it difficult to distinguish the needed and necessary information from the huge quantity of data. As a result, the importance of summarize medical reports is growing day after day. Medical information extraction is one of the important topics that aim to identify medical information and detect hidden relations. This topic is considered one of the most important topics in the field of text mining where is used to process unstructured texts and extract meaningful information which is hidden in the unstructured texts.The information extracted from medical reports is very useful to medical staff to detect hidden relations between medical information, and making decisions that will improve the medical service for patients, in addition to saving time and effort.In our paper, an approach that use template based medical reports summarization has been developed to transfer medical reports from semi structured and unstructured form to structured form. It classifies the identified entities then extracts important information such as diseases, medical procedures, and drugs. After that, it can discovery hidden relationship between medical information by using association rules. The dataset used in this paper was collected from the Palestinian Ministry of Health.To evaluate the performance and effectiveness of our model, human expert has been used as a reference to measure the degree of acceptance of the extracted association rules which have been extracted from the dataset. So, Likert's scale has been used for evaluation. After the data analysis obtained from the questionnaire. It shows us that the proportion of accuracy association rules, which have been extracted is about 80%.

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“…Besides the use of English language in the research studies of subjectivity classification, there are several research works in the Arabic language [23] and the Urdu language [24]. [23] used support vector machine (SVM) as supervised machine learning for the subjectivity and sentiment analysis.…”

Section: ) Subjectivity Classificationmentioning

confidence: 99%

“…[23] used support vector machine (SVM) as supervised machine learning for the subjectivity and sentiment analysis. As well, [24] used techniques such as bootstrap learning and resource sharing from a syntactically similar language.…”

Section: ) Subjectivity Classificationmentioning

confidence: 99%

Sentiment Analysis or Opinion Mining: A Review

Saad¹,

Saberi²

2017

International Journal on Advanced Science, Engineering and Information Technology

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Abstract-Opinion Mining (OM) or Sentiment Analysis (SA) can be defined as the task of detecting, extracting and classifying opinions on something. It is a type of the processing of the natural language (NLP) to track the public mood to a certain law, policy, or marketing, etc. It involves a way that development for the collection and examination of comments and opinions about legislation, laws, policies, etc., which are posted on the social media. The process of information extraction is very important because it is a very useful technique but also a challenging task. That mean, to extract sentiment from an object in the web-wide, need to automate opinion-mining systems to do it. The existing techniques for sentiment analysis include machine learning (supervised and unsupervised), and lexical-based approaches. Hence, the main aim of this paper presents a survey of sentiment analysis (SA) and opinion mining (OM) approaches, various techniques used that related in this field. As well, it discusses the application areas and challenges for sentiment analysis with insight into the past researcher's works.

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An Information-Extraction System for Urdu---A Resource-Poor Language

Cited by 50 publications

References 43 publications

Text Segmentation for Analysing Different Languages

Text Segmentation for Analysing Different Languages

Template based Medical Reports Summarization

Sentiment Analysis or Opinion Mining: A Review

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