Spelling checking using conditional random fields with feature induction for secondary language learners

Yeh, Jui‐Feng; Yu, Kai-Hsiang; Yeh, Chan-Kun

doi:10.1080/23080477.2016.1167155

Cited by 1 publication

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many publications have examined ambiguous structures such as his view, which is ambiguous in nature. Occasionally, PPs that share the same structure cannot be correctly identified just based on internal information contained inside the sentence; instead, the contextual information must be employed to accurately identify PPs that have the same structure [30] 2.2. Sequence Annotation.…”

Section: Theoretical Underpinningsmentioning

confidence: 99%

“…Following a review of previous studies, we discovered that the CRF model is the current model with the highest recognition in prepositional phrase recognition. However, the structural level of prepositional phrases has not been thoroughly investigated, and the research on prepositional nesting has not been sufficiently detailed to resolve the situation in which nested and parallel structures of prepositional phrases exist simultaneously [27][28][29][30]. This study offers an algorithm for automatically recognizing Chinese spatial prepositional phrases for academic literature, taking into account the fact that the majority of the phrases following prepositions in PP are made of noun phrase compositions.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Algorithm for Automatic Recognition of Chinese Spatial Prepositional Phrases for Academic Texts

Gao

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Automatic recognition of prepositional phrases has always been pursued in English translation, and its accurate recognition is crucial for various activities and applications in the field of natural language processing. This paper proposes an automatic recognition algorithm for Chinese spatial prepositional phrases in academic texts, which can not only identify parallel prepositional phrases but also improve the recognition accuracy of nested prepositional phrases. First, the method employs a simple noun phrase recognition model to identify and fuse phrase information contained in the corpus, thereby simplifying the corpus and minimizing the internal complexity of prepositional phrases. Second, the method uses the CRF model to identify nested inner prepositional phrases; if there is nesting, it identifies the inner layer of nesting; if there is no nesting, it identifies prepositional phrases; finally, it fuses the inner prepositions identified in the initial corpus phrase, modifies its feature information, and retrains the foreign preposition phrase recognition model for recognition. After the internal and external prepositional phrases are automatically identified, the double error correction method is used to correct the identified prepositional phrases. The experimental analysis shows that the accuracy rate, recall rate, and F -value of the method for identifying prepositional phrases are 95.33%, 94.32%, and 94.73%, respectively, which are higher than those of the other methods for identifying prepositional phrases, which effectively improves the recognition rate of prepositional phrases.

show abstract

Section: Theoretical Underpinningsmentioning

confidence: 99%