Post Text Processing of Chinese Speech Recognition Based on Bidirectional LSTM Networks and CRF

Yang, Li; Liu, Ying; Wang, Jin; Tang, Zhengqiang

doi:10.3390/electronics8111248

Cited by 20 publications

(6 citation statements)

References 41 publications

(41 reference statements)

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“…LSTM networks are found in many applications of PV forecasting [12,14], residential load forecasting [33], natural language processing [34], and speech recognition [35]. This network has improved PV forecasting accuracy when the input PV series contains numerous repetitive daily PV generation profiles.…”

Section: Proposed Weather Data Mixing Model-based Pv Forecasting Fram...mentioning

confidence: 99%

Weather Data Mixing Models for Day-Ahead PV Forecasting in Small-Scale PV Plants

Acharya

Lee

2021

Energies

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As a large number of small-scale PV plants have been deployed in distribution systems, generation forecasting of such plants has recently been gaining interest. Because the PV power mainly depends on weather conditions, it is important to accurately collect weather data for relevant PV sites to enhance PV forecasting accuracy. However, small-scale PV plants do not often have their own measuring apparatus to get historical weather data, so they have used weather datasets from relatively nearby weather data centers (WDCs). Therefore, these small-scale PV plants have difficulty delivering robust and reliable forecasting accuracy because of inappropriate predicted weather data from a distance. In this paper, two weather data mixing models are proposed: (a) inverse distance weighting (IDW), and (b) inverse correlation weighting (ICW). These models acquire adequate mixed weather data for the day-ahead generation forecasting for small-scale PV plants. Furthermore, the mixed weather data are collected using the geographic distance between the PV site and WDCs, or correlation between the PV generation and weather variables from nearby WDCs. Interestingly, the proposed ICW model outperforms when WDCs are located distant from the PV plants, whereas IDW performs well with the closer WDCs. The forecasting performance of the proposed mixing models was compared with those of the existing weather data collection methods.

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Section: Proposed Weather Data Mixing Model-based Pv Forecasting Fram...mentioning

confidence: 99%

Weather Data Mixing Models for Day-Ahead PV Forecasting in Small-Scale PV Plants

Acharya

Lee

2021

Energies

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“…These gates have the ability to capture the temporal changes for extremely long sequential data. Because of its advantages, it has been utilized widely in various applications such as text [ 43 ], videos [ 44 ], time series analysis [ 39 , 40 ], traffic forecast [ 45 ], speech recognition [ 46 ], and time series anomaly detection [ 47 ]. Lin et al [ 43 ] introduce an application of LSTMs on the task of mention extraction, where LSTMs extract and classify overlapped and nested structure mentions.…”

Section: Related Workmentioning

confidence: 99%

“…Zhao et al [ 45 ] apply LSTMs to the problem of traffic forecast to achieve better performances on the data collected by the Beijing Traffic Management Bureau. Yang et al [ 46 ] combine a Bi-LSTM network with a Conditional Random Field (CRF) model for Chinese speech recognition. The above studies give a fruitful insight into how LSTMs are effective models, and into how there are many practical applications related to the problem of time series analysis.…”

Section: Related Workmentioning

confidence: 99%

Multiple Electric Energy Consumption Forecasting Using a Cluster-Based Strategy for Transfer Learning in Smart Building

Kieu

et al. 2020

Sensors

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Electric energy consumption forecasting is an interesting, challenging, and important issue in energy management and equipment efficiency improvement. Existing approaches are predictive models that have the ability to predict for a specific profile, i.e., a time series of a whole building or an individual household in a smart building. In practice, there are many profiles in each smart building, which leads to time-consuming and expensive system resources. Therefore, this study develops a robust framework for the Multiple Electric Energy Consumption forecasting (MEC) of a smart building using Transfer Learning and Long Short-Term Memory (TLL), the so-called MEC-TLL framework. In this framework, we first employ a k-means clustering algorithm to cluster the daily load demand of many profiles in the training set. In this phase, we also perform Silhouette analysis to specify the optimal number of clusters for the experimental datasets. Next, this study develops the MEC training algorithm, which utilizes a cluster-based strategy for transfer learning the Long Short-Term Memory models to reduce the computational time. Finally, extensive experiments are conducted to compare the computational time and different performance metrics for multiple electric energy consumption forecasting on two smart buildings in South Korea. The experimental results indicate that our proposed approach is capable of economical overheads while achieving superior performances. Therefore, the proposed approach can be applied effectively for intelligent energy management in smart buildings.

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“…• Practical Task Definition of Medical-domain Chinese Spelling Correction: We formally define the Chinese spelling correction task in the medical domain for the first time, which applies to all tasks involving user input such as search, question answering, and translation. as search engine [7,8,19], optical character recognition [6,20,27] and automatic speech recognition [2,9,34,41]. CSC is similar to the task of Chinese Grammatical Error Correction (CGEC) [16].…”

Section: Introductionmentioning

confidence: 99%

MCSCSet: A Specialist-annotated Dataset for Medical-domain Chinese Spelling Correction

Jiang

et al. 2022

Proceedings of the 31st ACM International Conference on Information &Amp; Knowledge Management

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Chinese Spelling Correction (CSC) is gaining increasing attention due to its promise of automatically detecting and correcting spelling errors in Chinese texts. Despite its extensive use in many applications, like search engines and optical character recognition systems, little has been explored in medical scenarios in which complex and uncommon medical entities are easily misspelled. Correcting the misspellings of medical entities is arguably more difficult than those in the open domain due to its requirements of specific domain knowledge. In this work, we define the task of Medicaldomain Chinese Spelling Correction and propose MCSCSet, a largescale specialist-annotated dataset that contains about 200k samples. In contrast to the existing open-domain CSC datasets, MCSCSet involves: i) extensive real-world medical queries collected from Tencent Yidian, ii) corresponding misspelled sentences manually annotated by medical specialists. To ensure automated dataset curation, MCSCSet further offers a medical confusion set consisting of the commonly misspelled characters of given Chinese medical terms. This enables one to create the medical misspelling dataset automatically. Extensive empirical studies have shown significant performance gaps between the open-domain and medical-domain spelling correction, highlighting the need to develop high-quality datasets that allow for Chinese spelling correction in specific domains. Moreover, our work benchmarks several representative Chinese spelling correction models, establishing baselines for future work.

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Post Text Processing of Chinese Speech Recognition Based on Bidirectional LSTM Networks and CRF

Cited by 20 publications

References 41 publications

Weather Data Mixing Models for Day-Ahead PV Forecasting in Small-Scale PV Plants

Weather Data Mixing Models for Day-Ahead PV Forecasting in Small-Scale PV Plants

Multiple Electric Energy Consumption Forecasting Using a Cluster-Based Strategy for Transfer Learning in Smart Building

MCSCSet: A Specialist-annotated Dataset for Medical-domain Chinese Spelling Correction

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