A Scalable Solution for Rule-Based Part-of-Speech Tagging on Novel Hardware Accelerators

Sadredini, Elaheh; Guo, Deyuan; Bo, Chunkun; Rahimi, Reza; Skadron, Kevin; Wang, Hongning

doi:10.1145/3219819.3219889

Cited by 24 publications

(7 citation statements)

References 23 publications

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“…As shown in Equation (1), finding the attribute-sentiment pair in a sentence can cause a problem in selecting "up to which part" from the sequential nouns. Regarding the typical prior sentiment mining studies [3,4], there has been a number in which the target word is found by the PMI method or by applying the rule after tagging parts of a speech [5], when the target word is not determined or when it is determined [6,7]. In order to accurately find the parts of a sentence that can be the target word and sentiment word, a statistical model that analyzes the sentence structure and effectively extracts the target-sentiment word pair from the analyzed structure is proposed.…”

Section: Related Workmentioning

confidence: 99%

Sentiment-Target Word Pair Extraction Model Using Statistical Analysis of Sentence Structures

Kim

Park

2021

Electronics

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Product information has been propagated online via forums and social media. Lots of merchandise are recommended via an expert system method and is considered for purchase by online comments or product reviews. For predicting people’s opinions on products, studying people’s thoughts via extracting information in documents is referred to as sentiment analysis. Finding sentiment-target word pairs is an important sentiment mining research issue. With the Korean language, as the predicate appears at the very end, it is not easy to find the exact word pairs without first identifying the syntactic structure of the sentence. In this study, we propose a model that parses sentence structures and extracts sentiment-target word pairs from the parse tree. The proposed model extracts the sentiment-target word pairs that appear in the sentence by using parsing and statistical methods. For extracting sentiment-target word pairs, this model uses a sentiment word extractor and a target word extractor. After testing data from 4000 movie reviews, the applicable model showed high performance in both accuracy 93.25 (+14.45) and F1-score 82.29 (+3.31) compared with others. However, improvements in the recall rate (−0.35) are needed and computational costs must be reduced.

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Section: Related Workmentioning

confidence: 99%

Sentiment-Target Word Pair Extraction Model Using Statistical Analysis of Sentence Structures

Kim

Park

2021

Electronics

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“…With the low amount of training, testing always encounters more and more unknown data and it eventually makes the hidden mark model not much useful for such cases. Recently, some works [34] [35] for resource-poor language and rule-based methods have shown some improvement. But still, rule-based NLP applications are language-specific and the advantages are limited.…”

Section: Related Work For Low Resource Languagesmentioning

confidence: 99%

Highly Efficient Parts of Speech Tagging in Low Resource Languages with Improved Hidden Markov Model and Deep Learning

Baishya¹,

Baruah²

2021

IJACSA

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“…Content repurposing can be detected using a linguistic technique known as part of speech (POS) [43]. Nowadays web spammers are using very advanced techniques for a content generation; machine-generated content is one of them.…”

Section: Spamdexing Detection Using Part Of Speech (Pos)mentioning

confidence: 99%

An Improved Framework for Content-based Spamdexing Detection

Shahzad¹,

Mahdin²,

Mohd³

2020

IJACSA

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To the modern Search Engines (SEs), one of the biggest threats to be considered is spamdexing. Nowadays spammers are using a wide range of techniques for content generation, they are using content spam to fill the Search Engine Result Pages (SERPs) with low-quality web pages. Generally, spam web pages are insufficient, irrelevant and improper results for users. Many researchers from academia and industry are working on spamdexing to identify the spam web pages. However, so far not even a single universally efficient method is developed for identification of all spam web pages. We believe that for tackling the content spam there must be improved methods. This article is an attempt in that direction, where a framework has been proposed for spam web pages identification. The framework uses Stop words, Keywords Density, Spam Keywords Database, Part of Speech (POS) ratio, and Copied Content algorithms. For conducting the experiments and obtaining threshold values WEBSPAM-UK2006 and WEBSPAM-UK2007 datasets have been used. An excellent and promising F-measure of 77.38% illustrates the effectiveness and applicability of proposed method.

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A Scalable Solution for Rule-Based Part-of-Speech Tagging on Novel Hardware Accelerators

Cited by 24 publications

References 23 publications

Sentiment-Target Word Pair Extraction Model Using Statistical Analysis of Sentence Structures

Sentiment-Target Word Pair Extraction Model Using Statistical Analysis of Sentence Structures

Highly Efficient Parts of Speech Tagging in Low Resource Languages with Improved Hidden Markov Model and Deep Learning

An Improved Framework for Content-based Spamdexing Detection

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