Integrating machine learning with linguistic features: A universal method for extraction and normalization of temporal expressions in Chinese texts

Wang, Shunli; Li, Rui; Wu, Huayi

doi:10.1016/j.cmpb.2023.107474

Cited by 3 publications

(1 citation statement)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Against the backdrop of rapid development in technologies such as artificial intelligence and spatiotemporal big data, the increasingly pressing issue of "information overload" poses a challenge to the rapid and accurate extraction of the truly needed information for individuals [1,2]. Dependency syntax trees reveal the structure and grammatical relationships between words in a sentence, serving as a crucial semantic carrier of textual information and providing a foundation for other natural language processing tasks such as sentiment analysis, named entity recognition, and semantic similarity calculation [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

DAT-MT Accelerated Graph Fusion Dependency Parsing Model for Small Samples in Professional Fields

Li,

Shu,

Wang

et al. 2023

Entropy

Self Cite

View full text Add to dashboard Cite

The rapid development of information technology has made the amount of information in massive texts far exceed human intuitive cognition, and dependency parsing can effectively deal with information overload. In the background of domain specialization, the migration and application of syntactic treebanks and the speed improvement in syntactic analysis models become the key to the efficiency of syntactic analysis. To realize domain migration of syntactic tree library and improve the speed of text parsing, this paper proposes a novel approach—the Double-Array Trie and Multi-threading (DAT-MT) accelerated graph fusion dependency parsing model. It effectively combines the specialized syntactic features from small-scale professional field corpus with the generalized syntactic features from large-scale news corpus, which improves the accuracy of syntactic relation recognition. Aiming at the problem of high space and time complexity brought by the graph fusion model, the DAT-MT method is proposed. It realizes the rapid mapping of massive Chinese character features to the model’s prior parameters and the parallel processing of calculation, thereby improving the parsing speed. The experimental results show that the unlabeled attachment score (UAS) and the labeled attachment score (LAS) of the model are improved by 13.34% and 14.82% compared with the model with only the professional field corpus and improved by 3.14% and 3.40% compared with the model only with news corpus; both indicators are better than DDParser and LTP 4 methods based on deep learning. Additionally, the method in this paper achieves a speedup of about 3.7 times compared to the method with a red-black tree index and a single thread. Efficient and accurate syntactic analysis methods will benefit the real-time processing of massive texts in professional fields, such as multi-dimensional semantic correlation, professional feature extraction, and domain knowledge graph construction.

show abstract

Section: Introductionmentioning

confidence: 99%

DAT-MT Accelerated Graph Fusion Dependency Parsing Model for Small Samples in Professional Fields

Li,

Shu,

Wang

et al. 2023

Entropy

Self Cite

View full text Add to dashboard Cite

show abstract

Multi-class identification of tonal contrasts in Chokri using supervised machine learning algorithms

Gope,

Pal,

Tetseo

et al. 2024

Humanit Soc Sci Commun

View full text Add to dashboard Cite

This study examines and explores the effectiveness of various Machine Learning Algorithms (MLAs) in identifying intricate tonal contrasts in Chokri (ISO 639-3), an under-documented and endangered Tibeto-Burman language of the Sino-Tibetan language family spoken in Nagaland, India. Seven different supervised MLAs, viz., [Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naive Bayes (NB)], and one neural network (NN)-based algorithms [Artificial Neural Network (ANN)] are implemented to explore five-way tonal contrasts in Chokri. Acoustic correlates of tonal contrasts, encompassing fundamental frequency fluctuations, viz., f0 height and f0 direction, are examined. Contrary to the prevailing notion of NN supremacy, this study underscores the impressive accuracy achieved by the RF. Additionally, it reveals that combining f0 height and directionality enhances tonal contrast recognition for female speakers, while f0 directionality alone suffices for male speakers. The findings demonstrate MLAs’ potential to attain accuracy rates of 84–87% for females and 95–97% for males, showcasing their applicability in deciphering the intricate tonal systems of Chokri. The proposed methodology can be extended to predict multi-class problems in diverse fields such as image processing, speech classification, medical diagnosis, computer vision, and social network analysis.

show abstract

A general modeling scheme for spatiotemporal DGGS with emphasis on encoding and operating multiscale time grids

Zhou,

Ben,

Liang

et al. 2024

Transactions in GIS

View full text Add to dashboard Cite

One of the basic scientific problems concerning geographic information science is how to rapidly organize, query, and compute spatiotemporal big data. The spatiotemporal discrete global grid system (DGGS) provides a homogenized discrete structure for processing multiscale and multitype spatiotemporal data. To date, most research in spatiotemporal DGGS has focused on spatial discretization while neglecting temporal discretization. Here, we propose a general modeling scheme for spatiotemporal DGGS with emphasis on encoding and operating multiscale time grids. We subdivide continuous time into multiscale temporal grids, which are then encoded as integers. Moreover, we designed integer code operations, including hierarchical traversal, neighborhood finding, and temporal relationship calculations. Compared to the multiscale time segment integer coding (MTSIC) approach, the proposed method resulted in 22% higher encoding efficiency, 10.92 times faster decoding, 2.81 times better parent code finding efficiency, 41% improved efficiency, 100% accuracy in finding children codes (compared to less than 100% with MTSIC), and a 62% enhancement in temporal relationship calculation efficiency. The application of querying spatiotemporal trajectory data validates the feasibility and practicality of substituting conventional string‐based time and floating‐point location coordinates with spatiotemporal integer codes to query data. The time encoding and operation methods proposed here indicate high efficiency, superior accuracy, and broad application prospects.

show abstract

Integrating machine learning with linguistic features: A universal method for extraction and normalization of temporal expressions in Chinese texts

Cited by 3 publications

References 23 publications

DAT-MT Accelerated Graph Fusion Dependency Parsing Model for Small Samples in Professional Fields

DAT-MT Accelerated Graph Fusion Dependency Parsing Model for Small Samples in Professional Fields

Multi-class identification of tonal contrasts in Chokri using supervised machine learning algorithms

A general modeling scheme for spatiotemporal DGGS with emphasis on encoding and operating multiscale time grids

Contact Info

Product

Resources

About