Analyzing Tagging Accuracy of Part-of-Speech Taggers

Khin, Nyein Pyae Pyae; Aung, Than Nwe

doi:10.1007/978-3-319-23207-2_35

Cited by 3 publications

(1 citation statement)

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“…To construct a combination system, two or more individual taggers should be involved. Usually, a combination of taggers obtains a higher accuracy than the application of just a single tagger [12]. The reason is that different taggers eventually produce different errors and these differences can be exploited to yield better results.…”

Section: Introductionmentioning

confidence: 99%

Feature-rich PoS Tagging through Taggers Combination : Experience in Arabic

Zeroual

Lakhouaja

2017

TMLAI

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Since words can play different syntactic roles in different contexts, it is not trivial to assign the appropriate morphosyntactic category to each word according to the context. Part of Speech (PoS) tagging is the task which manage this issue. Several probabilistic methods have been adapted for PoS tagging such as Hidden Markov Models, Support Vector Machines, and Decision Tree. Based on these methods, language independent PoS taggers have been developed such as TnT, SVMTool, and Treetagger. The main purpose of this work is to combine automatically the output of these standard PoS taggers and investigate several options for how to do this combination. The experiments are applied to one of the morphologically complex languages, Arabic. In this paper, we highlight the use of these taggers via various experiments. In fact, the evaluations involve several tests on both Classical and Modern Standard Arabic, trained/untrained and tagged/untagged data. Finally, a deeper investigation of Arabic PoS tagging through these languageindependent taggers combination is performed.

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Section: Introductionmentioning

confidence: 99%

Feature-rich PoS Tagging through Taggers Combination : Experience in Arabic

Zeroual

Lakhouaja

2017

TMLAI

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Analysis of Topic Propagation in Therapy Sessions Using Partially Labeled Latent Dirichlet Allocation

Chaoua

Consoli

Härmä

et al. 2019

Lecture Notes in Computer Science

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Application of machine learning algorithms in wind power: a review

Deng

Xue-hua

Zhou

et al. 2021

Energy Sources, Part A: Recovery, Utilization, and Environmenta

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Literature review is an overview of existing research, it can also be used to understand research trends and directions. In recent years, the literature associated with wind power has grown rapidly, and it seems inadequate to rely on human resources to study all papers. Very few studies have used machine learning algorithms and visualization approaches to analyze the trends and directions in wind power. To explore these, we collected 50,579 articles (2012-2019) from Web of Science Core Collection (WoSCC) and 785 papers (2012-2019) from China National Knowledge Infrastructure (CNKI). We applied machine learning algorithms including text mining, word segmentation, T-Distributed Stochastic Neighbor Embedding (T-SNE), Auto-Encoder (AE), visual imagery and other methods to analyze and visually display literature in the field of wind power via analysis of the trends with time-sequence, hotspots in abstracts and keywords, and spatial distribution. China, the United States, and Iran are the top three countries in the field of wind power. Through analyzing the trends between 2012-2019, we find that research hotspots have changed. The usage rate of terms such as Power Generation Control, Power Grids, Wind Power Plants, and Wind Turbines has significantly increased, and the corresponding growth rates are 10.91%, 7.06%, 6.28% and 4.33%, respectively. This study also provides information on the relationship of words of abstracts in papers, which shows that these words are mainly divided into four categories: forecasting, optimization, investment, energy and equipment. An implication of this study is that machine learning algorithms may play an important role in the analysis of wind power literature.

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Analyzing Tagging Accuracy of Part-of-Speech Taggers

Cited by 3 publications

References 1 publication

Feature-rich PoS Tagging through Taggers Combination : Experience in Arabic

Feature-rich PoS Tagging through Taggers Combination : Experience in Arabic

Analysis of Topic Propagation in Therapy Sessions Using Partially Labeled Latent Dirichlet Allocation

Application of machine learning algorithms in wind power: a review

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