Results 161 to 170 of about 9,285,371 (201)

A Monocular Depth Estimation Method for Autonomous Driving Vehicles Based on Gaussian Neural Radiance Fields. [PDF]

Sensors (Basel)
Nie Z, Zhao Z, Pan J, Ren Y, Yu H, Xu L.
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Analysis of cultivated land changes and driving factors in the Alar Reclamation Area (1990-2019) based on multi-temporal Landsat data and machine learning algorithms. [PDF]

Sci Rep
Song Q, Zhang W.
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Chinese-Named Entity Recognition From Adverse Drug Event Records: Radical Embedding-Combined Dynamic Embedding–Based BERT in a Bidirectional Long Short-term Conditional Random Field (Bi-LSTM-CRF) Model

Chinese-Named Entity Recognition From Adverse Drug Event Records: Radical Embedding-Combined Dynamic Embedding–Based BERT in a Bidirectional Long Short-term Conditional Random Field (Bi-LSTM-CRF) Model
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Adopting Conditional Random Field (CRF) for Khasi Part-of-Speech Tagging (KPOST)

Lecture Notes in Networks and Systems, 2021
The main purpose of this paper is to develop part-of-speech (PoS) tagging for the Khasi language based on conditional random field (CRF) approaches. Khasi belongs to a Mon–Khmer language of the Austroasiatic language family that is spoken by the native people of the state Meghalaya, Northeastern Part of India.
Arnab Kumar Maji, Partha Pakray, Sunita Warjri   +2 more
exaly   +2 more sources

Part-of-Speech (POS) Tagging Using Conditional Random Field (CRF) Model for Khasi Corpora

International Journal of Speech Technology, 2021
Khasi is a language that belongs to the Mon-Khmer language of the Austroasiatic group. Khasi language is spoken by the indigenous people of the state of Meghalaya in India. This paper presents a work on Part-of-speech (POS) tagging for the Khasi language by using the Conditional Random Field (CRF) method.
Arnab Kumar Maji, Partha Pakray, Sunita Warjri   +2 more
exaly   +2 more sources

Automatic Prosody Prediction and Detection With Conditional Random Field (CRF) Models [PDF]

2010 7th International Symposium on Chinese Spoken Language Processing, 2010
While the current TTS systems can deliver quite acceptable segmental quality of synthesized speech for voice user interface applications, its prosody is still perceived by users as “robotic” or not expressive. In this paper, we investigate how to improve TTS prosody prediction and detection.
Frank Soong, Yao Qian
exaly   +2 more sources