scholarly journals Fast and Robust Wrapper Method for $N$ -gram Feature Template Induction in Structured Prediction

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 19897-19908 ◽  
Author(s):  
Yulin Ren ◽  
Dehua Li
Keyword(s):  
Structured Prediction ◽  
Wrapper Method ◽  
Feature Template ◽  
N Gram

Learning N-Gram Language Models from Uncertain Data

2016 ◽  
Author(s):  
Vitaly Kuznetsov ◽  
Hank Liao ◽  
Mehryar Mohri ◽  
Michael Riley ◽  
Brian Roark
Keyword(s):  
Uncertain Data ◽  
Language Models ◽  
N Gram

Rescore in a Flash: Compact, Cache Efficient Hashing Data Structures for n-Gram Language Models

2020 ◽  
Author(s):  
Grant P. Strimel ◽  
Ariya Rastrow ◽  
Gautam Tiwari ◽  
Adrien Piérard ◽  
Jon Webb
Keyword(s):  
Data Structures ◽  
Language Models ◽  
Cache Efficient ◽  
N Gram

scholarly journals OCR correction for Indonesian historic newspapers using word repetition, stemmer and n-gram

2019 ◽  
Vol 1193 ◽  
pp. 012032
Author(s):  
D Purwantoro ◽  
H Akbar ◽  
A Hidayati ◽  
Sfenrianto
Keyword(s):  
Word Repetition ◽  
N Gram

Robustness of Word and Character N-gram Combinations in Detecting Deceptive and Truthful Opinions

2020 ◽  
Vol 12 (1) ◽  
pp. 1-24 ◽  
Author(s):  
Al Hafiz Akbar Maulana Siagian ◽  
Masayoshi Aritsugi
Keyword(s):  
N Gram

GINS: A Global intensifier-based N-Gram sentiment dictionary

2021 ◽  
pp. 1-14
Author(s):  
Hamed Zargari ◽  
Morteza Zahedi ◽  
Marziea Rahimi
Keyword(s):  
Sentiment Analysis ◽  
Essential Elements ◽  
Analysis Methods ◽  
Sentiment Dictionary ◽  
N Gram ◽  
Heterogeneous Effect ◽  
Sentiment Word

Words are one of the most essential elements of expressing sentiments in context although they are not the only ones. Also, syntactic relationships between words, morphology, punctuation, and linguistic phenomena are influential. Merely considering the concept of words as isolated phenomena causes a lot of mistakes in sentiment analysis systems. So far, a large amount of research has been conducted on generating sentiment dictionaries containing only sentiment words. A number of these dictionaries have addressed the role of combinations of sentiment words, negators, and intensifiers, while almost none of them considered the heterogeneous effect of the occurrence of multiple linguistic phenomena in sentiment compounds. Regarding the weaknesses of the existing sentiment dictionaries, in addressing the heterogeneous effect of the occurrence of multiple intensifiers, this research presents a sentiment dictionary based on the analysis of sentiment compounds including sentiment words, negators, and intensifiers by considering the multiple intensifiers relative to the sentiment word and assigning a location-based coefficient to the intensifier, which increases the covered sentiment phrase in the dictionary, and enhanced efficiency of proposed dictionary-based sentiment analysis methods up to 7% compared to the latest methods.

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