Part-of-speech tagging based on dictionary and statistical machine learning

Ye, Zhonglin; Jia, Zhen; Huang, Junfu; Yin, Hongpeng

doi:10.1109/chicc.2016.7554459

Cited by 12 publications

(5 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This method also solves the problem of POS tag errors in the dictionary, insufficient training corpus in statistical machine learning method, ignoring context information of words. [4] In this paper, extractive text summarization is obtained by using sentence ranking. The input file is first tokenized and stop words are removed to get the filtered text.…”

Section: Literature Surveymentioning

confidence: 99%

Text Summarizer for Url and .Docx Files

Pinto¹

2020

ijarcs

View full text Add to dashboard Cite

The objective of this model is to compute a block of text and return its equivalent summary. The text provided could be both content from an online website or an offline text document. The purpose of this project is to help generate an efficient algorithm which could provide a meaningful summary of the text and help the user save time on reading and help grasp maximum information in a minimalistic amount of time.

show abstract

Section: Literature Surveymentioning

confidence: 99%

Text Summarizer for Url and .Docx Files

Pinto¹

2020

ijarcs

View full text Add to dashboard Cite

show abstract

“…Zhonglin et al [81], stated that Part-of-speech Tagging is the core of the NLP. The traditional statistical machine learning methods of POS tagging depends on the high quality of training data.…”

Section: Context Identification and Disambiguationmentioning

confidence: 99%

“…However, it is very time-consuming to obtain that data. The methods of POS tagging, based on dictionaries, ignore the context information which leads to lower performance [81]. They provided some algorithms to modify the dictionary and to develop the POS Tagging ways.…”

Section: Context Identification and Disambiguationmentioning

confidence: 99%

“…Thus the context information must be taken into account while translating those words. The other words translations will be taken from the dictionary directly [81]. Also, they compared their work with the other POS tagging methods available at that time and stated that their method is better and its accuracy is 88%.…”

Section: Context Identification and Disambiguationmentioning

confidence: 99%

See 1 more Smart Citation

Machine-Translation History and Evolution: Survey for Arabic-English Translations

Alsohybe

Dahan

Ba-Alwi

2017

CJAST

View full text Add to dashboard Cite

As a result of the rapid changes in information and communication technology (ICT), the world has become a small village where people from all over the world connect with each other in dialogue and communication via the Internet. Also, communications have become a daily routine activity due to the new globalization where companies and even universities become global residing cross countries' borders. As a result, translation becomes a needed activity in this connected world. ICT made it possible to have a student in one country take a course or even a degree from a different country anytime anywhere easily. The resulted communication still needs a language as a means that helps the receiver understands the contents of the sent message. People need an automated translation application because human translators are hard to find all the times, and the human translations are very expensive comparing to the translations automated process. Several types of research describe the electronic process of the Machine-Translation. In this paper, the authors are going to study some of these previous researches, and they will explore some of the needed tools Alsohybe et al.; CJAST, 23(4): 1-19, 2017; Article no.CJAST.36124 2 for the Machine-Translation. This research is going to contribute to the Machine-Translation area by helping future researchers to have a summary for the Machine-Translation groups of research and to let lights on the importance of the translation mechanism. Original Research Article

show abstract

“…(Lardilleux et al, 2010). Such dictionaries are helpful for many natural language processing (NLP) tasks such as Machine Translation (MT) for translating Out-Of-Vocabulary (OOV) words, cross-lingual information retrieval, cross-lingual word embedding and multilingual parts-of-speech tagging (Wołk, 2019;Ye et al, 2016;Sharma and Mittal, 2018). Creating a bilingual dictionary requires high-quality parallel corpora and expert linguists, both of which are scarce and costly in resource-poor languages (Hajnicz et al, 2016;Sarma, 2019).…”

Section: Introductionmentioning

confidence: 99%

Mining BilingualWord Pairs from Comparable Corpus using Apache Spark Framework

Jp¹,

Menon²,

Kp³

et al. 2021

Proceedings of the 14thWorkshop on Building and Using Comparable Corpora (BUCC 2021)

View full text Add to dashboard Cite

Bilingual dictionaries are essential resources in many areas of natural language processing tasks, but resource-scarce and less popular language pairs rarely have such. Efficient automatic methods for inducting bilingual dictionaries are needed as manual resources and efforts are scarce for low-resourced languages. In this paper, we induce word translations using bilingual embedding. We use the Apache Spark ® framework for parallel computation. Further, to validate the quality of the generated bilingual dictionary, we use it in a phrase-table aided Neural Machine Translation (NMT) system. The system can perform moderately well with a manual bilingual dictionary; we change this into our inducted dictionary. The corresponding translated outputs are compared using the Bilingual Evaluation Understudy (BLEU) and Rank-based Intuitive Bilingual Evaluation Score (RIBES) metrics.

show abstract

Part-of-speech tagging based on dictionary and statistical machine learning

Cited by 12 publications

References 1 publication

Text Summarizer for Url and .Docx Files

Text Summarizer for Url and .Docx Files

Machine-Translation History and Evolution: Survey for Arabic-English Translations

Mining BilingualWord Pairs from Comparable Corpus using Apache Spark Framework

Contact Info

Product

Resources

About