TWE‐WSD: An effective topical word embedding based word sense disambiguation

Jia, Lianyin; Tang, Jilin; Li, Mengjuan; You, Jane; Ding, Jiang; Chen, Yinong

doi:10.1049/cit2.12006

Cited by 14 publications

(4 citation statements)

References 33 publications

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“…The architecture of VGG16 is shown in Figure 2. [36] by integrating VGG16 and Latent Dirichlet Allocation (LDA) [37].…”

Section: Vgg16mentioning

confidence: 99%

“…The classifier of VGG16 consists of two fully connected layers with 4096 nodes and one fully connected layer with 1000 nodes, which are finally classified by the softmax function. The authors choose VGG16 as the basic model to research flower classification because it has achieved successful results in the ImageNet challenge and has been widely used in flower classification and many other fields, e.g., A-LDCNN proposed in[36] by integrating VGG16 and Latent Dirichlet Allocation (LDA)[37].…”

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confidence: 99%

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A Parallel Convolution and Decision Fusion-Based Flower Classification Method

et al. 2022

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Flower classification is of great significance to the fields of plants, food, and medicine. However, due to the inherent inter-class similarity and intra-class differences of flowers, it is a difficult task to accurately classify them. To this end, this paper proposes a novel flower classification method that combines enhanced VGG16 (E-VGG16) with decision fusion. Firstly, facing the shortcomings of the VGG16, an enhanced E-VGG16 is proposed. E-VGG16 introduces a parallel convolution block designed in this paper on VGG16 combined with several other optimizations to improve the quality of extracted features. Secondly, considering the limited decision-making ability of a single E-VGG16 variant, parallel convolutional blocks are embedded in different positions of E-VGG16 to obtain multiple E-VGG16 variants. By introducing information entropy to fuse multiple E-VGG16 variants for decision-making, the classification accuracy is further improved. The experimental results on the Oxford Flower102 and Oxford Flower17 public datasets show that the classification accuracy of our method reaches 97.69% and 98.38%, respectively, which significantly outperforms the state-of-the-art methods.

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“…The architecture of VGG16 is shown in Figure 2. [36] by integrating VGG16 and Latent Dirichlet Allocation (LDA) [37].…”

Section: Vgg16mentioning

confidence: 99%

mentioning

confidence: 99%

A Parallel Convolution and Decision Fusion-Based Flower Classification Method

et al. 2022

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“…Currently, the most popular method to solve this task is word embedding [5][6][7], which yields low-dimensional word vectors from corpora to calculate word similarity. It also has become a relevant topic in recent years and plays a really important role in NLP downstream tasks, such as Word sense disambiguation [8,9], machine translation [10,11], text summarization [12,13], context identification system [14].…”

Section: Introductionmentioning

confidence: 99%

Modeling multi-prototype Chinese word representation learning for word similarity

Yin

Wang

et al. 2021

Complex Intell. Syst.

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The word similarity task is used to calculate the similarity of any pair of words, and is a basic technology of natural language processing (NLP). The existing method is based on word embedding, which fails to capture polysemy and is greatly influenced by the quality of the corpus. In this paper, we propose a multi-prototype Chinese word representation model (MP-CWR) for word similarity based on synonym knowledge base, including knowledge representation module and word similarity module. For the first module, we propose a dual attention to combine semantic information for jointly learning word knowledge representation. The MP-CWR model utilizes the synonyms as prior knowledge to supplement the relationship between words, which is helpful to solve the challenge of semantic expression due to insufficient data. As for the word similarity module, we propose a multi-prototype representation for each word. Then we calculate and fuse the conceptual similarity of two words to obtain the final result. Finally, we verify the effectiveness of our model on three public data sets with other baseline models. In addition, the experiments also prove the stability and scalability of our MP-CWR model under different corpora.

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“…An adaptive cross-contextual word embedding process is then defined to learn local word embedding for each polysemous word in different contexts. Jia et al[98] proposed an effective topical word embedding (TWE)-based WSD method to generate topical word vectors for each word under each topic. The authors used LDA to extract high-quality topic models, and then generate contextual vectors generated for an ambiguous word to exploit the context.…”

mentioning

confidence: 99%

Sentiment analysis and opinion mining on educational data: A survey

Shaik,

Tao,

Dann

et al. 2023

Preprint

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Sentiment analysis AKA opinion mining is one of the most widely used NLP applications to identify human intentions from their reviews. In the education sector, opinion mining is used to listen to student opinions and enhance their learning-teaching practices pedagogically. With advancements in sentiment annotation techniques and AI methodologies, student comments can be labelled with their sentiment orientation without much human intervention. In this review article, 1) we consider the role of emotional analysis in education from four levels: document level, sentence level, entity level, and aspect level, 2)sentiment annotation techniques including lexicon-based and corpus-based approaches for unsupervised annotations are explored, 3)the role of AI in sentiment analysis with methodologies like machine learning,

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TWE‐WSD: An effective topical word embedding based word sense disambiguation

Cited by 14 publications

References 33 publications

A Parallel Convolution and Decision Fusion-Based Flower Classification Method

A Parallel Convolution and Decision Fusion-Based Flower Classification Method

Modeling multi-prototype Chinese word representation learning for word similarity

Sentiment analysis and opinion mining on educational data: A survey

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