Lex-POS Sequence and WEOE Embedding System
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A Lex-POS Sequence and WEOE Embedding System is a GEC Sequence Tagging System that can detect grammatical and tag-specific errors by calculating pos-tag sequences of Sentence.
- AKA: Lex-POS, Lex-POS Sequence System.
- Context:
- Source code available at: https://github.com/Machine-Learning-and-Data-Science/Lex-POS-Approach
- It was developed by Agarwal et al. (2020).
- It can solve a Lex-POS Sequence Task.
- It creates Word Embedding One-Hot Encoding (WEOE) vector representation that transform Lex-Pos sequences into mathematical values.
- It includes a grammar classifier that is based on Long Short-Term Memory (LSTM) neural network architecture.
- Example(s):
- Counter-Example(s):
- See: Feature-based Grammar Error Detection System, Grammar Error Detection System, Grammar Error Correction System, Natural Language Processing System, Deep Learning System, Part-Of-Speech (POS) Tag, Word Embedding, Word Embedding One-Hot Encoding.
References
2021
- (GitHub, 2021) ⇒ https://github.com/Machine-Learning-and-Data-Science/Lex-POS-Approach#lex-pos-approach Retrieved:2021-02-27.
- QUOTE: Lex-Pos sequence has the potential to imbibe the specific nature of the linguistic words (i.e. lexicals) and generic structural characteristics of a sentence via Part-Of-Speech (POS) tags, and so, can lead to a significant improvement in detecting grammar errors. A new vector representation technique, Word Embedding One hot Encoding (WEOE) is introduced to transform Lex-POS into mathematical values.
2020
- (Agarwal et al., 2020) ⇒ Nancy Agarwal, Mudasir Ahmad Wani, and Patrick Bours. (2020). “Lex-Pos Feature-Based Grammar Error Detection System for the English Language.” In: Electronics, 9(10).
- QUOTE: The main contributions of the work are summarised as follows.
- A new sequence of the English sentence named Lex-Pos is proposed, which tends to infuse the specificity of linguistic and generalization of syntactic characteristics of a sentence;
- A novel vector representation for Lex-Pos sequence of sentences named as Word Embedding One-Hot Encoding (WEOE) has been presented by combining the word embedding and one-hot encoded sequences;
- The novel error induction methods have been proposed to create negative samples containing POS-tag errors for training;
- The grammar classifier is designed using LSTM deep learning architecture;
- Overall, nine experiments have been conducted on three designed datasets to reveal the potential of Lex-Pos sequences; and
- A comparative study is presented where two replicas of existing grammar-aware systems are designed and experiments are conducted to further demonstrate the strength of Lex-Pos sequences.
- QUOTE: The main contributions of the work are summarised as follows.