Text Classification Using Word Embedding in Rule-Based Methodologies: A Systematic Mapping

Aubaid, Asmaa M.; Mishra, Alok

doi:10.18421/tem74-31

Cited by 5 publications

(3 citation statements)

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“…The term rule-based refers to any schemes using IF-THEN rules [25]. The advantage of this system is that the process is traceable and can add a number of new rules to get good results [20].…”

Section: The Rulesmentioning

confidence: 99%

An adaptable sentence segmentation based on Indonesian rules

Petrus¹,

Ermatita

Sukemi

et al. 2023

IJ-AI

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<p>Sentence segmentation that breaks textual data strings into individual sentences is an important phase in natural language processing (NLP). Each word in the string that is added a punctuation mark such as a period, question mark, or exclamation point, becomes the location for splitting the string. Humans can easily see the punctuation and split the string into sentences, but not machines. Basically, the three punctuation marks also perform other functions so that the sentence segmentation process must really be able to detect whether a word marked with punctuation is a sentence boundary or not. This research proposes a sentence segmentation system called segmentasi kalimat bahasa Indonesia (SKBI) or Indonesian language Sentence Segmentation by applying a set of rules and can be used in Indonesian texts and can be adapted for English. There are 34 rules built with a combination of 27 fairly complete features that contribute to this research. The experimental results for the Indonesian text show that the SKBI is able to achieve an F1-Score of 96.89% and 97.07% for English. Both need to be improved but now better than previous research.</p>

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Section: The Rulesmentioning

confidence: 99%

An adaptable sentence segmentation based on Indonesian rules

Petrus¹,

Ermatita

Sukemi

et al. 2023

IJ-AI

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“…Many diverse aspects and techniques for MWE processing have been attempted, Domainindependent methods for classifying vocabulary [14], Ontology based [15], frequency and pattern classification methods, parallel texts, word embedding and Rule-Based methodologies [16] [17], and Wordnet based methods [18]. MERGE (MWEs from the Recursive Aggregation of Elements) [19], uses the Bigram principle to construct a vocabulary of a certain length.…”

Section: Related Workmentioning

confidence: 99%

Identifying and Analyzing Reduplication Multiword Expressions in Hindi Text Using Machine Learning

Mishra,

Mishra

2023

TEM Journal

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The task of identifying and analyzing Reduplication Multiword Expressions (RMWEs) in Natural Language Processing (NLP) involves extracting repeated words from various text forms and classifying them into Onomatopoeic, non-Onomatopoeic, partial, or semantic types. With the increasing use of low-resource languages in news, opinions, comments, hashtags, reviews, posts, and journals, this study proposes a machine learning-based RMWE identification method for Hindi text. The method employs linguistic patterns and statistical data, along with a proposed threshold boundary detection in statistical filtering. The Jaccard distance of dissimilarity and Sorensen Dice Coefficient of Similarity are used for semantic relation analysis. The proposed approach was evaluated using the publicly available Hindi corpus from IITB, measuring performance between two consecutive thresholds with the lowest error and highest recall. This study proposes an effective method for Indian computational linguistics, with experimental results highlighting its viability and utility, and providing a blueprint for current procedures.

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“…For the studies in this field, working with machine learning algorithms, for these meanings extracted from the data, such as writing techniques, language tools, linguistic developments for different languages, interpretation of different meanings of the word in linguistics, and the change of the emotion expressed when words come together, has produced very successful results. Besides, there are text classification studies [ 6 , 7 ] in the literature that also worked on text and documents; however, they are aimed to find useful information for business intelligence instead of emotions [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Sentimental Analysis of Twitter Users from Turkish Content with Natural Language Processing

Balli

Güzel

Bostancı

et al. 2022

Computational Intelligence and Neuroscience

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Artificial Intelligence has guided technological progress in recent years; it has shown significant development with increased academic studies on Machine Learning and the high demand for this field in the sector. In addition to the advancement of technology day by day, the pandemic, which has become a part of our lives since early 2020, has led to social media occupying a larger place in the lives of individuals. Therefore, social media posts have become an excellent data source for the field of sentiment analysis. The main contribution of this study is based on the Natural Language Processing method, which is one of the machine learning topics in the literature. Sentiment analysis classification is a solid example for machine learning tasks that belongs to human-machine interaction. It is essential to make the computer understand people emotional situation with classifiers. There are a limited number of Turkish language studies in the literature. Turkish language has different types of linguistic features from English. Since Turkish is an agglutinative language, it is challenging to make sentiment analysis with that language. This paper aims to perform sentiment analysis of several machine learning algorithms on Turkish language datasets that are collected from Twitter. In this research, besides using public dataset that belongs to Beyaz (2021) to get more general results, another dataset is created to understand the impact of the pandemic on people and to learn about public opinions. Therefore, a custom dataset, namely, SentimentSet (Balli 2021), was created, consisting of Turkish tweets that were filtered with words such as pandemic and corona by manually marking as positive, negative, or neutral. Besides, SentimentSet could be used in future researches as benchmark dataset. Results show classification accuracy of not only up to ∼87% with test data from datasets of both datasets and trained models, but also up to ∼84% with small “Sample Test Data” generated by the same methods as SentimentSet dataset. These research results contributed to indicating Turkish language specific sentiment analysis that is dependent on language specifications.

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Text Classification Using Word Embedding in Rule-Based Methodologies: A Systematic Mapping

Cited by 5 publications

References 0 publications

An adaptable sentence segmentation based on Indonesian rules

An adaptable sentence segmentation based on Indonesian rules

Identifying and Analyzing Reduplication Multiword Expressions in Hindi Text Using Machine Learning

Sentimental Analysis of Twitter Users from Turkish Content with Natural Language Processing

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