Sprinkled semantic diffusion kernel for word sense disambiguation

Wang, Ting‐Hua; Li, Wei; Liu, Fulai; Hua, Jialin

doi:10.1016/j.engappai.2017.05.010

Cited by 12 publications

(17 citation statements)

References 31 publications

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“…Supervised semantic smoothing kernels exist that utilise class information in building a semantic matrix [1,2,36]. A sprinkled diffusion kernel that uses both co-occurrence information and class information for word sense disambiguation is presented in [36]. In this approach, the smoothing helps in increasing the semantic relationship between terms in the same class.…”

Section: Related Workmentioning

confidence: 99%

Knowledge-driven graph similarity for text classification

Shanavas

Wang

Lin

et al. 2020

Int. J. Mach. Learn. & Cyber.

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Automatic text classification using machine learning is significantly affected by the text representation model. The structural information in text is necessary for natural language understanding, which is usually ignored in vector-based representations. In this paper, we present a graph kernel-based text classification framework which utilises the structural information in text effectively through the weighting and enrichment of a graph-based representation. We introduce weighted co-occurrence graphs to represent text documents, which weight the terms and their dependencies based on their relevance to text classification. We propose a novel method to automatically enrich the weighted graphs using semantic knowledge in the form of a word similarity matrix. The similarity between enriched graphs, knowledge-driven graph similarity, is calculated using a graph kernel. The semantic knowledge in the enriched graphs ensures that the graph kernel goes beyond exact matching of terms and patterns to compute the semantic similarity of documents. In the experiments on sentiment classification and topic classification tasks, our knowledge-driven similarity measure significantly outperforms the baseline text similarity measures on five benchmark text classification datasets.

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Section: Related Workmentioning

confidence: 99%

Knowledge-driven graph similarity for text classification

Shanavas

Wang

Lin

et al. 2020

Int. J. Mach. Learn. & Cyber.

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“…Machine learning approaches, also called corpus-based approaches, do not make use of any knowledge resources for disambiguation (Raganato et al, 2017). Most accurate WSD systems to date exploit supervised methods which automatically learn cues useful for disambiguation from manually sense-annotated data (Wang et al, 2017). All above analyses are very useful for a company from where a user can purchase a physical product as well as on line service provider.…”

Section: Literature Reviewmentioning

confidence: 99%

Semi-supervised method for sensitivity based documents’ classification for online service providers

Malebary¹,

Ahmad²

2020

Int. j. adv. appl. sci.

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In today's digital era, many services providing companies exist on the web whereas service is the logical product of a company, which can be utilized through the Internet. Different service providers provide these services i.e., Online counselling service, online doctor consultation, cloud service provider, web hosting service, etc. to their customers. When customers face some problems, they may text to their providers. One solution is that providers can solve these issues based on the First-Come-First-Serve formula. But there should be an option to detect sensitive issue which may need to be solved first. How can this sensitivity be determined? Already there is a lot of researched work based on text to determine the polarity as positive and negative. Besides this classification, there are also some other classification methods investigated, such as aspect, not aspect, subjective, objective, spam, not spam, etc. regarding text sensitivity, whether it is sensitive or not? This classification is not yet considered for service providers. This paper presents a strategy for sensitivity based classification using Latent Semantic Indexing (LSI). The purpose of LSI is to rank documents concerning a given query. However, in this study, a mechanism was provided to generate query automatically based on sensitive general words with the words from all documents. This is a semi-supervised approach because 4782 sensitive words have been labeled from various sources and used based on an unsupervised approach to detect the sensitivity of the document. The sorted lists of documents based on the LSI scores generated by the sensitive-query were checked manually and were proved to be highly satisfactory. The topmost document in this list was the most sensitive, and the last document in the list was least sensitive.

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“…For instance in [29], latent semantic indexing (LSI) is performed both on standard term-document matrix and term-document matrix augmented with sprinkled terms. The sprinkling process is shown in Figure 1: In Figure 1, to explain the sprinkling process, we use the toy corpus from [28] that has 2 different class labels with 3 documents (Doc-1, Doc-2, and Doc-3 ) and 4 different terms ( t 1 , t 2 , t 3 , and t 4 ). In (a) we get the document-term matrix with 3 documents and 4 terms.…”

Section: Sprinklingmentioning

confidence: 99%

Word sense disambiguation using semantic kernels with class-based term values

Altınel¹,

Ganil²,

Sipal³

et al. 2019

Turk J Elec Eng & Comp Sci

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In this study, we propose several semantic kernels for word sense disambiguation (WSD). Our approaches adapt the intuition that class-based term values help in resolving ambiguity of polysemous words in WSD. We evaluate our proposed approaches with experiments, utilizing various sizes of training sets of disambiguated corpora (SensEval 1 ).With these experiments we try to answer the following questions: 1.) Do our semantic kernel formulations yield higher classification performance than traditional linear kernel?, 2.) Under which conditions a kernel design performs better than others?, 3.) Does the addition of class labels into standard term-document matrix improve the classification accuracy?, 4.) Is their combination superior to either type?, 5.) Is ensemble of these kernels perform better than the baseline?, 6.) What is the effect of training set size? Our experiments demonstrate that our kernel-based WSD algorithms can outperform baseline in terms of F-score. of words (BOW), which is a well-known feature representation technique that only regards the frequency of the words, a basic similarity calculation such as Cosine or Jaccard among sentences id 1 , id 3 , and id 4 will be zero; since they have no words in common. The same situation is valid for the similarity between sentences id 2 and id 5 . On the other hand, the similarity between sentences id 1 and id 2 will probably be greater than zero since they shared a word; "mouse". Moreover, although they convey different messages, the similarity between sentences id 2 and id 4 will probably be greater than zero since they shared a word "cell".

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Sprinkled semantic diffusion kernel for word sense disambiguation

Cited by 12 publications

References 31 publications

Knowledge-driven graph similarity for text classification

Knowledge-driven graph similarity for text classification

Semi-supervised method for sensitivity based documents’ classification for online service providers

Word sense disambiguation using semantic kernels with class-based term values

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