Part-of-Speech-Based Long Short-Term Memory Network for Learning Sentence Representations

Zhu, Wenhao; Yao, Tengjun; Wu, Zongda; Wei, Baogang

doi:10.1109/access.2019.2911983

Cited by 12 publications

(6 citation statements)

References 8 publications

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“…ey use the similarity between users to find the set of nearest neighbors, calculate user preferences based on the weighted average of the neighbor sets, and filter product searches to users according to their scores. Later, with the development of deep learning, in addition to its power in mining hidden features, more features that express user preferences can be discovered through deep mining, making the user preference model more accurate in describing user preferences [12][13][14].…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Synthetic Network and Search Filter Algorithm in English Oral Duplicate Correction Map

Chen

2021

Complexity

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Combining the communicative language competence model and the perspective of multimodal research, this research proposes a research framework for oral communicative competence under the multimodal perspective. This not only truly reflects the language communicative competence but also fully embodies the various contents required for assessment in the basic attributes of spoken language. Aiming at the feature sparseness of the user evaluation matrix, this paper proposes a feature weight assignment algorithm based on the English spoken category keyword dictionary and user search records. The algorithm is mainly based on the self-built English oral category classification dictionary and converts the user’s query vector into a user-English-speaking type vector. Through the calculation rules proposed in this paper, the target user’s preference score for a specific type of spoken English is obtained, and this score is assigned to the unrated item of the original user’s feature matrix as the initial starting score. At the same time, in order to solve the problem of insufficient user similarity calculation accuracy, a user similarity calculation algorithm based on “Synonyms Cilin Extended Edition” and search records is proposed. The algorithm introduces “Synonyms Cilin” to calculate the correlation between the semantic items, vocabulary, and query vector in the user query record to obtain the similarity between users and finally gives a user similarity calculation that integrates user ratings and query vectors method. For the task of Chinese grammar error correction, this article uses two methods of predicting the relationship between words in the corpus, Word2Vec and GloVe, to train the word vectors of different dimensions and use the word vectors to represent the text features of the experimental samples, avoiding sentences brought by word segmentation. On the basis of word vectors, the advantages and disadvantages of CNN, LSTM, and SVM models in this shared task are analyzed through experimental data. The comparative experiment shows that the method in this paper has achieved relatively good results.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Synthetic Network and Search Filter Algorithm in English Oral Duplicate Correction Map

Chen

2021

Complexity

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“…Task-Driven FC Teaching Model Analysis. Different from the traditional teaching mode, FC teaching mode based on task-driven takes teaching content analysis and partition as the starting point of teaching activities, divides the teaching content into several different task stages according to the logical relationship, and sets up several levels of increasing difficulty for each stage according to students' learning level and learning needs [22,23]. The task-driven FC teaching model is shown in Figure 3.…”

Section: Fcmentioning

confidence: 99%

Research on Flipped Classroom of University Curriculum Using Eye Movement Analysis and LSTM Neural Network

2022

Wireless Communications and Mobile Computing

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By reversing the arrangement of knowledge transfer and knowledge internalisation, FC (flipped classroom) changed the roles of teachers and students in traditional teaching and replanned the use of classroom time, realizing the innovation of traditional teaching mode. This research uses a network teaching platform to create a network platform supporting environment suitable for FC teaching in universities, allowing for the reform of university classroom teaching modes. An LSTM (Long Short-Term Memory) eye movement analysis algorithm is proposed to solve this problem. By analyzing the sequence of eye movements, this algorithm can predict students’ learning behavior and complete the current learning state detection. The research shows that the design and application of this research have achieved a good teaching effect in practice, which provides an idea for flipped classroom teaching design and curriculum resource design and development.

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“…Zhu et al [ 16 ] incorporated lexical information into the learning of sentence representations. They used a classical network structure like LSTM [ 17 ] to model the sequence of words in a sentence to obtain a sequence of hidden states, where each hidden state corresponds to a word in the input sentence.…”

Section: Related Work: Nlp Using Part-of-speech Informationmentioning

confidence: 99%

Fusing Part‐of‐Speech Information in Low‐Resource Neural Paraphrase Generation

Chi

Xiang

2021

Computational Intelligence and Neuroscience

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Paraphrase generation is an essential yet challenging task in natural language processing. Neural-network-based approaches towards paraphrase generation have achieved remarkable success in recent years. Previous neural paraphrase generation approaches ignore linguistic knowledge, such as part-of-speech information regardless of its availability. The underlying assumption is that neural nets could learn such information implicitly when given sufficient data. However, it would be difficult for neural nets to learn such information properly when data are scarce. In this work, we endeavor to probe into the efficacy of explicit part-of-speech information for the task of paraphrase generation in low-resource scenarios. To this end, we devise three mechanisms to fuse part-of-speech information under the framework of sequence-to-sequence learning. We demonstrate the utility of part-of-speech information in low-resource paraphrase generation through extensive experiments on multiple datasets of varying sizes and genres.

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Part-of-Speech-Based Long Short-Term Memory Network for Learning Sentence Representations

Cited by 12 publications

References 8 publications

[Retracted] Synthetic Network and Search Filter Algorithm in English Oral Duplicate Correction Map

[Retracted] Synthetic Network and Search Filter Algorithm in English Oral Duplicate Correction Map

Research on Flipped Classroom of University Curriculum Using Eye Movement Analysis and LSTM Neural Network

Fusing Part‐of‐Speech Information in Low‐Resource Neural Paraphrase Generation

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