The Experts below are selected from a list of 57 Experts worldwide ranked by ideXlab platform
Heyan Huang - One of the best experts on this subject based on the ideXlab platform.
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tibetan syllable based Functional Chunk boundary identification
CCL, 2017Co-Authors: Shumin Shi, Yujian Liu, Tianhang Wang, Congjun Long, Heyan HuangAbstract:Tibetan syntactic Functional Chunk parsing is aimed at identifying syntactic constituents of Tibetan sentences. In this paper, based on the Tibetan syntactic Functional Chunk description system, we propose a method which puts syllables in groups instead of word segmentation and tagging and use the Conditional Random Fields (CRFs) to identify the Functional Chunk boundary of a sentence. According to the actual characteristics of the Tibetan language, we firstly identify and extract the syntactic markers as identification characteristics of syntactic Functional Chunk boundary in the text preprocessing stage, while the syntactic markers are composed of the sticky written form and the non-sticky written form. Afterwards we identify the syntactic Functional Chunk boundary using CRF. Experiments have been performed on a Tibetan language corpus containing 46783 syllables and the precision, recall rate and F value respectively achieves 75.70%, 82.54% and 79.12%. The experiment results show that the proposed method is effective when applied to a small-scale unlabeled corpus and can provide foundational support for many natural language processing applications such as machine translation.
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CCL - Tibetan Syllable-Based Functional Chunk Boundary Identification
Lecture Notes in Computer Science, 2017Co-Authors: Shumin Shi, Yujian Liu, Tianhang Wang, Congjun Long, Heyan HuangAbstract:Tibetan syntactic Functional Chunk parsing is aimed at identifying syntactic constituents of Tibetan sentences. In this paper, based on the Tibetan syntactic Functional Chunk description system, we propose a method which puts syllables in groups instead of word segmentation and tagging and use the Conditional Random Fields (CRFs) to identify the Functional Chunk boundary of a sentence. According to the actual characteristics of the Tibetan language, we firstly identify and extract the syntactic markers as identification characteristics of syntactic Functional Chunk boundary in the text preprocessing stage, while the syntactic markers are composed of the sticky written form and the non-sticky written form. Afterwards we identify the syntactic Functional Chunk boundary using CRF. Experiments have been performed on a Tibetan language corpus containing 46783 syllables and the precision, recall rate and F value respectively achieves 75.70%, 82.54% and 79.12%. The experiment results show that the proposed method is effective when applied to a small-scale unlabeled corpus and can provide foundational support for many natural language processing applications such as machine translation.
Shumin Shi - One of the best experts on this subject based on the ideXlab platform.
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tibetan syllable based Functional Chunk boundary identification
CCL, 2017Co-Authors: Shumin Shi, Yujian Liu, Tianhang Wang, Congjun Long, Heyan HuangAbstract:Tibetan syntactic Functional Chunk parsing is aimed at identifying syntactic constituents of Tibetan sentences. In this paper, based on the Tibetan syntactic Functional Chunk description system, we propose a method which puts syllables in groups instead of word segmentation and tagging and use the Conditional Random Fields (CRFs) to identify the Functional Chunk boundary of a sentence. According to the actual characteristics of the Tibetan language, we firstly identify and extract the syntactic markers as identification characteristics of syntactic Functional Chunk boundary in the text preprocessing stage, while the syntactic markers are composed of the sticky written form and the non-sticky written form. Afterwards we identify the syntactic Functional Chunk boundary using CRF. Experiments have been performed on a Tibetan language corpus containing 46783 syllables and the precision, recall rate and F value respectively achieves 75.70%, 82.54% and 79.12%. The experiment results show that the proposed method is effective when applied to a small-scale unlabeled corpus and can provide foundational support for many natural language processing applications such as machine translation.
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CCL - Tibetan Syllable-Based Functional Chunk Boundary Identification
Lecture Notes in Computer Science, 2017Co-Authors: Shumin Shi, Yujian Liu, Tianhang Wang, Congjun Long, Heyan HuangAbstract:Tibetan syntactic Functional Chunk parsing is aimed at identifying syntactic constituents of Tibetan sentences. In this paper, based on the Tibetan syntactic Functional Chunk description system, we propose a method which puts syllables in groups instead of word segmentation and tagging and use the Conditional Random Fields (CRFs) to identify the Functional Chunk boundary of a sentence. According to the actual characteristics of the Tibetan language, we firstly identify and extract the syntactic markers as identification characteristics of syntactic Functional Chunk boundary in the text preprocessing stage, while the syntactic markers are composed of the sticky written form and the non-sticky written form. Afterwards we identify the syntactic Functional Chunk boundary using CRF. Experiments have been performed on a Tibetan language corpus containing 46783 syllables and the precision, recall rate and F value respectively achieves 75.70%, 82.54% and 79.12%. The experiment results show that the proposed method is effective when applied to a small-scale unlabeled corpus and can provide foundational support for many natural language processing applications such as machine translation.
Congjun Long - One of the best experts on this subject based on the ideXlab platform.
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tibetan syllable based Functional Chunk boundary identification
CCL, 2017Co-Authors: Shumin Shi, Yujian Liu, Tianhang Wang, Congjun Long, Heyan HuangAbstract:Tibetan syntactic Functional Chunk parsing is aimed at identifying syntactic constituents of Tibetan sentences. In this paper, based on the Tibetan syntactic Functional Chunk description system, we propose a method which puts syllables in groups instead of word segmentation and tagging and use the Conditional Random Fields (CRFs) to identify the Functional Chunk boundary of a sentence. According to the actual characteristics of the Tibetan language, we firstly identify and extract the syntactic markers as identification characteristics of syntactic Functional Chunk boundary in the text preprocessing stage, while the syntactic markers are composed of the sticky written form and the non-sticky written form. Afterwards we identify the syntactic Functional Chunk boundary using CRF. Experiments have been performed on a Tibetan language corpus containing 46783 syllables and the precision, recall rate and F value respectively achieves 75.70%, 82.54% and 79.12%. The experiment results show that the proposed method is effective when applied to a small-scale unlabeled corpus and can provide foundational support for many natural language processing applications such as machine translation.
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CCL - Tibetan Syllable-Based Functional Chunk Boundary Identification
Lecture Notes in Computer Science, 2017Co-Authors: Shumin Shi, Yujian Liu, Tianhang Wang, Congjun Long, Heyan HuangAbstract:Tibetan syntactic Functional Chunk parsing is aimed at identifying syntactic constituents of Tibetan sentences. In this paper, based on the Tibetan syntactic Functional Chunk description system, we propose a method which puts syllables in groups instead of word segmentation and tagging and use the Conditional Random Fields (CRFs) to identify the Functional Chunk boundary of a sentence. According to the actual characteristics of the Tibetan language, we firstly identify and extract the syntactic markers as identification characteristics of syntactic Functional Chunk boundary in the text preprocessing stage, while the syntactic markers are composed of the sticky written form and the non-sticky written form. Afterwards we identify the syntactic Functional Chunk boundary using CRF. Experiments have been performed on a Tibetan language corpus containing 46783 syllables and the precision, recall rate and F value respectively achieves 75.70%, 82.54% and 79.12%. The experiment results show that the proposed method is effective when applied to a small-scale unlabeled corpus and can provide foundational support for many natural language processing applications such as machine translation.
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Tibetan Functional Chunk recognition using statistical based method
Himalayan Linguistics, 2016Co-Authors: Congjun Long, Weina ZhaoAbstract:Functional Chunk can reveal the skeleton of a sentence and the relation among Chunks. Recognizing Functional Chunk is a sub-field of Natural Language Process, which can effectively improve the performance of syntactic parsing. This paper proposes a Tibetan Functional Chunk classification. To testify the feasibility ofthe proposed theory, we observe the distribution of Tibetan Functional Chunks in our corpus. The statistics prove that the classification can describe sentence structure comprehensively. Then we establish a Functional Chunking model based on a sequencetag model. By introducing appropriate features, a couple of experiments have been conducted. The F1 achieves 82.30 by employing extended features.
Yujian Liu - One of the best experts on this subject based on the ideXlab platform.
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tibetan syllable based Functional Chunk boundary identification
CCL, 2017Co-Authors: Shumin Shi, Yujian Liu, Tianhang Wang, Congjun Long, Heyan HuangAbstract:Tibetan syntactic Functional Chunk parsing is aimed at identifying syntactic constituents of Tibetan sentences. In this paper, based on the Tibetan syntactic Functional Chunk description system, we propose a method which puts syllables in groups instead of word segmentation and tagging and use the Conditional Random Fields (CRFs) to identify the Functional Chunk boundary of a sentence. According to the actual characteristics of the Tibetan language, we firstly identify and extract the syntactic markers as identification characteristics of syntactic Functional Chunk boundary in the text preprocessing stage, while the syntactic markers are composed of the sticky written form and the non-sticky written form. Afterwards we identify the syntactic Functional Chunk boundary using CRF. Experiments have been performed on a Tibetan language corpus containing 46783 syllables and the precision, recall rate and F value respectively achieves 75.70%, 82.54% and 79.12%. The experiment results show that the proposed method is effective when applied to a small-scale unlabeled corpus and can provide foundational support for many natural language processing applications such as machine translation.
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CCL - Tibetan Syllable-Based Functional Chunk Boundary Identification
Lecture Notes in Computer Science, 2017Co-Authors: Shumin Shi, Yujian Liu, Tianhang Wang, Congjun Long, Heyan HuangAbstract:Tibetan syntactic Functional Chunk parsing is aimed at identifying syntactic constituents of Tibetan sentences. In this paper, based on the Tibetan syntactic Functional Chunk description system, we propose a method which puts syllables in groups instead of word segmentation and tagging and use the Conditional Random Fields (CRFs) to identify the Functional Chunk boundary of a sentence. According to the actual characteristics of the Tibetan language, we firstly identify and extract the syntactic markers as identification characteristics of syntactic Functional Chunk boundary in the text preprocessing stage, while the syntactic markers are composed of the sticky written form and the non-sticky written form. Afterwards we identify the syntactic Functional Chunk boundary using CRF. Experiments have been performed on a Tibetan language corpus containing 46783 syllables and the precision, recall rate and F value respectively achieves 75.70%, 82.54% and 79.12%. The experiment results show that the proposed method is effective when applied to a small-scale unlabeled corpus and can provide foundational support for many natural language processing applications such as machine translation.
Tianhang Wang - One of the best experts on this subject based on the ideXlab platform.
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tibetan syllable based Functional Chunk boundary identification
CCL, 2017Co-Authors: Shumin Shi, Yujian Liu, Tianhang Wang, Congjun Long, Heyan HuangAbstract:Tibetan syntactic Functional Chunk parsing is aimed at identifying syntactic constituents of Tibetan sentences. In this paper, based on the Tibetan syntactic Functional Chunk description system, we propose a method which puts syllables in groups instead of word segmentation and tagging and use the Conditional Random Fields (CRFs) to identify the Functional Chunk boundary of a sentence. According to the actual characteristics of the Tibetan language, we firstly identify and extract the syntactic markers as identification characteristics of syntactic Functional Chunk boundary in the text preprocessing stage, while the syntactic markers are composed of the sticky written form and the non-sticky written form. Afterwards we identify the syntactic Functional Chunk boundary using CRF. Experiments have been performed on a Tibetan language corpus containing 46783 syllables and the precision, recall rate and F value respectively achieves 75.70%, 82.54% and 79.12%. The experiment results show that the proposed method is effective when applied to a small-scale unlabeled corpus and can provide foundational support for many natural language processing applications such as machine translation.
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CCL - Tibetan Syllable-Based Functional Chunk Boundary Identification
Lecture Notes in Computer Science, 2017Co-Authors: Shumin Shi, Yujian Liu, Tianhang Wang, Congjun Long, Heyan HuangAbstract:Tibetan syntactic Functional Chunk parsing is aimed at identifying syntactic constituents of Tibetan sentences. In this paper, based on the Tibetan syntactic Functional Chunk description system, we propose a method which puts syllables in groups instead of word segmentation and tagging and use the Conditional Random Fields (CRFs) to identify the Functional Chunk boundary of a sentence. According to the actual characteristics of the Tibetan language, we firstly identify and extract the syntactic markers as identification characteristics of syntactic Functional Chunk boundary in the text preprocessing stage, while the syntactic markers are composed of the sticky written form and the non-sticky written form. Afterwards we identify the syntactic Functional Chunk boundary using CRF. Experiments have been performed on a Tibetan language corpus containing 46783 syllables and the precision, recall rate and F value respectively achieves 75.70%, 82.54% and 79.12%. The experiment results show that the proposed method is effective when applied to a small-scale unlabeled corpus and can provide foundational support for many natural language processing applications such as machine translation.
