Kernel methods for word sense disambiguation

Li, Xiangjun; Qing, Song; Zhang, Huawei; Wang, Ting‐Hua; Yang, Huping

doi:10.1007/s10462-015-9455-5

Cited by 5 publications

(2 citation statements)

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“…Knowledge-based methods take advantage of the knowledge resources such as dictionaries whereas corpus-based methods use manually sense annotated datasets to train a model. For machine learning, the general approach is to use corpus-based methods since the performance of supervised methods are higher than the unsupervised ones [18]. Another approach for resolving the WSD problem is to use Kernel methods [4,20,26].…”

Section: Introductionmentioning

confidence: 99%

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Word Sense Disambiguation using Diffusion Kernel PCA

Sipal,

Sari,

Teke

et al. 2019

Preprint

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One of the major problems in natural language processing (NLP) is the word sense disambiguation (WSD) problem. It is the task of computationally identifying the right sense of a polysemous word based on its context. Resolving the WSD problem boosts the accuracy of many NLP focused algorithms such as text classification and machine translation. In this paper, we introduce a new supervised algorithm for WSD, that is based on Kernel PCA and Semantic Diffusion Kernel, which is called Diffusion Kernel PCA (DKPCA). DKPCA grasps the semantic similarities within terms, and it is based on PCA. These properties enable us to perform feature extraction and dimension reduction guided by semantic similarities and within the algorithm. Our empirical results on SensEval data demonstrate that DKPCA achieves higher or very close accuracy results compared to SVM and KPCA with various well-known kernels when the labeled data ratio is meager. Considering the scarcity of labeled data, whereas large quantities of unlabeled textual data are easily accessible, these are highly encouraging first results to develop DKPCA further.

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Section: Introductionmentioning

confidence: 99%

“…On the other hand, there are kernel studies that make use of Principal Component Analysis (PCA), which is called Kernel Principal Component Analysis (KPCA) [25,28,34]. For further reading on kernel methods, we refer to [10], and for further knowledge of the usage of kernels for WSD, we refer to [18].…”

Section: Introductionmentioning

confidence: 99%