Survey on Parts of Speech Tagger Techniques

Kanakaraddi, Suvarna G.; Nandyal, Suvarna

doi:10.1109/icctct.2018.8550884

Cited by 28 publications

(8 citation statements)

References 5 publications

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“…by using part-of-speech taggers. POS tagging can be achieved through different approaches including rule-based, probabilistic, or hybrids of the two [11]. Having tagged parts of speech can make additional pre-processing easier by helping identify multiple forms of word sharing a base form.…”

Section: Paradigm 2 -Structural Insight: Traditional Nlpmentioning

confidence: 99%

The changing landscape of text mining - a review of approaches for ecology and evolution

Farrell,

Le Guillarme,

Brierley

et al. 2024

Preprint

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In ecology and evolutionary biology, synthesis and modelling of data from published literature is a common practice for generating insight and testing theories across systems. However, the tasks of searching, screening, and extracting data from literature are often arduous. Researchers may manually process hundreds to thousands of articles for systematic reviews, meta-analyses, and compiling synthetic datasets. As relevant articles expand to tens or hundreds of thousands, computer-based approaches can increase efficiency and dramatically improve the transparency and reproducibility of literature-based research. Methods available for text mining are rapidly changing due to developments in machine learning-based language models. Here we review the growing landscape of approaches, mapping them onto three broad paradigms (Frequency-based approaches, Traditional Natural Language Processing, and Deep learning-based language models). This review serves as an entry point to learn foundational and cutting edge concepts, vocabularies, and methods, and foster better integration of these tools into ecological and evolutionary research. We discuss approaches for modelling ecological texts, generating training data, developing custom models, and interacting with Large Language Models, and we present challenges and possible solutions to implementing these methods in ecology and evolution.

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Section: Paradigm 2 -Structural Insight: Traditional Nlpmentioning

confidence: 99%

The changing landscape of text mining - a review of approaches for ecology and evolution

Farrell,

Le Guillarme,

Brierley

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…There are various technical approaches to training the POS-tagged dataset, such as hidden Markov models (HMMs), conditional random fields (CRFs), or, more recently, deep learning models [26][27][28]. Nevertheless, for practical purposes, in this work, we use a pre-trained model available on SpaCy that trained over a large amount of English text.…”

Section: Part-of-speech (Pos) Taggingmentioning

confidence: 99%

Uncovering Sustainability Insights from Amazon’s Eco-Friendly Product Reviews for Design Optimization

Maarif,

Syafrudin,

Fitriyani

2023

Sustainability

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This research investigates consumer reviews of eco-friendly products on Amazon to uncover valuable sustainability insights that can inform design optimization. Using natural language processing (NLP) techniques, including sentiment analysis, key terms extraction, and topic modeling, this research reveals diverse perspectives related to sustainability aspects in eco-friendly products. Innovatively, we integrate the NLP approach with correspondence analysis (CA) to understand consumer sentiments and preferences related to sustainability aspects. Leveraging CA, we visualize the interplay between eco-friendly product features and consumer sentiments, revealing underlying relationships and patterns. The CA biplot showcases the alignment of specific sustainability attributes with consumer satisfaction, highlighting which sustainability aspects hold greater influence over overall product ratings. As sustainability becomes an increasingly crucial aspect of consumer choices, our paper emphasizes the significance of a multidimensional approach that embraces both qualitative and quantitative insights. By blending CA with consumer reviews, we equip designers and stakeholders with an innovative and comprehensive toolkit to enhance sustainable design practices, paving the way for more informed and effective product development strategies in the realm of eco-friendliness.

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“…POS tags such as nouns, verbs, pronouns, prepositions, and adjectives assign meaning to a word and help the computer to understand sentences. There are different techniques and categories, as shown in Figure 5, to perform POS tagging [45,46]: rule-based, stochastic or statistical, hybrid.…”

Section: Parts Of Speech Taggingmentioning

confidence: 99%

“…Knowledge-driven taggers, supported by human and built dictionaries, have achieved high accuracy results. On the other hand, it needs manual construction, and there are some limitations in using a defined number of rules [45].…”

Section: Rule-based Approachesmentioning

confidence: 99%

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Markov Models Applications in Natural Language Processing: A Survey

Almutiri¹,

Nadeem²

2022

IJITCS

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Markov models are one of the widely used techniques in machine learning to process natural language. Markov Chains and Hidden Markov Models are stochastic techniques employed for modeling systems that are dynamic and where the future state relies on the current state. The Markov chain, which generates a sequence of words to create a complete sentence, is frequently used in generating natural language. The hidden Markov model is employed in named-entity recognition and the tagging of parts of speech, which tries to predict hidden tags based on observed words. This paper reviews Markov models' use in three applications of natural language processing (NLP): natural language generation, named-entity recognition, and parts of speech tagging. Nowadays, researchers try to reduce dependence on lexicon or annotation tasks in NLP. In this paper, we have focused on Markov Models as a stochastic approach to process NLP. A literature review was conducted to summarize research attempts with focusing on methods/techniques that used Markov Models to process NLP, their advantages, and disadvantages. Most NLP research studies apply supervised models with the improvement of using Markov models to decrease the dependency on annotation tasks. Some others employed unsupervised solutions for reducing dependence on a lexicon or labeled datasets.

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Survey on Parts of Speech Tagger Techniques

Cited by 28 publications

References 5 publications

The changing landscape of text mining - a review of approaches for ecology and evolution

The changing landscape of text mining - a review of approaches for ecology and evolution

Uncovering Sustainability Insights from Amazon’s Eco-Friendly Product Reviews for Design Optimization

Markov Models Applications in Natural Language Processing: A Survey

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