A local and global context representation learning model for Chinese characters is designed and a Chinese word segmentation method based on character representations is proposed in this paper.First,the proposed Chines...A local and global context representation learning model for Chinese characters is designed and a Chinese word segmentation method based on character representations is proposed in this paper.First,the proposed Chinese character learning model uses the semantics of local context and global context to learn the representation of Chinese characters.Then,Chinese word segmentation model is built by a neural network,while the segmentation model is trained with the character representations as its input features.Finally,experimental results show that Chinese character representations can effectively learn the semantic information.Characters with similar semantics cluster together in the visualize space.Moreover,the proposed Chinese word segmentation model also achieves a pretty good improvement on precision,recall and f-measure.展开更多
ESA is an unsupervised approach to word segmentation previously proposed by Wang, which is an iterative process consisting of three phases: Evaluation, Selection and Adjustment. In this article, we propose Ex ESA, the...ESA is an unsupervised approach to word segmentation previously proposed by Wang, which is an iterative process consisting of three phases: Evaluation, Selection and Adjustment. In this article, we propose Ex ESA, the extension of ESA. In Ex ESA, the original approach is extended to a 2-pass process and the ratio of different word lengths is introduced as the third type of information combined with cohesion and separation. A maximum strategy is adopted to determine the best segmentation of a character sequence in the phrase of Selection. Besides, in Adjustment, Ex ESA re-evaluates separation information and individual information to overcome the overestimation frequencies. Additionally, a smoothing algorithm is applied to alleviate sparseness. The experiment results show that Ex ESA can further improve the performance and is time-saving by properly utilizing more information from un-annotated corpora. Moreover, the parameters of Ex ESA can be predicted by a set of empirical formulae or combined with the minimum description length principle.展开更多
To solve the complicated feature extraction and long distance dependency problem in Word Segmentation Disambiguation (WSD), this paper proposes to apply rough sets in WSD based on the Maximum Entropy model. Firstly, r...To solve the complicated feature extraction and long distance dependency problem in Word Segmentation Disambiguation (WSD), this paper proposes to apply rough sets in WSD based on the Maximum Entropy model. Firstly, rough set theory is applied to extract the complicated features and long distance features, even from noise or inconsistent corpus. Secondly, these features are added into the Maximum Entropy model, and consequently, the feature weights can be assigned according to the performance of the whole disambiguation model. Finally, the semantic lexicon is adopted to build class-based rough set features to overcome data sparseness. The experiment indicated that our method performed better than previous models, which got top rank in WSD in 863 Evaluation in 2003. This system ranked first and second respectively in MSR and PKU open test in the Second International Chinese Word Segmentation Bakeoff held in 2005.展开更多
Chinese word segmentation is the basis of natural language processing. The dictionary mechanism significantly influences the efficiency of word segmentation and the understanding of the user’s intention which is impl...Chinese word segmentation is the basis of natural language processing. The dictionary mechanism significantly influences the efficiency of word segmentation and the understanding of the user’s intention which is implied in the user’s query. As the traditional dictionary mechanisms can't meet the present situation of personalized mobile search, this paper presents a new dictionary mechanism which contains the word classification information. This paper, furthermore, puts forward an approach for improving the traditional word bank structure, and proposes an improved FMM segmentation algorithm. The results show that the new dictionary mechanism has made a significant increase on the query efficiency and met the user’s individual requirements better.展开更多
Semi-Markov conditional random fields(Semi-CRFs)have been successfully utilized in many segmentation problems,including Chinese word segmentation(CWS).The advantage of Semi-CRF lies in its inherent ability to exploit ...Semi-Markov conditional random fields(Semi-CRFs)have been successfully utilized in many segmentation problems,including Chinese word segmentation(CWS).The advantage of Semi-CRF lies in its inherent ability to exploit properties of segments instead of individual elements of sequences.Despite its theoretical advantage,Semi-CRF is still not the best choice for CWS because its computation complexity is quadratic to the sentenced length.In this paper,we propose a simple yet effective framework to help Semi-CRF achieve comparable performance with CRF-based models under similar computation complexity.Specifically,we first adopt a bi-directional long short-term memory(BiLSTM)on character level to model the context information,and then use simple but effective fusion layer to represent the segment information.Besides,to model arbitrarily long segments within linear time complexity,we also propose a new model named Semi-CRF-Relay.The direct modeling of segments makes the combination with word features easy and the CWS performance can be enhanced merely by adding publicly available pre-trained word embeddings.Experiments on four popular CWS datasets show the effectiveness of our proposed methods.The source codes and pre-trained embeddings of this paper are available on https://github.com/fastnlp/fastNLP/.展开更多
In this paper a novel word-segmentation algorithm is presented todelimit words in Chinese natural language queries in NChiql system, a Chinese natural language query interface to databases. Although there are sizable ...In this paper a novel word-segmentation algorithm is presented todelimit words in Chinese natural language queries in NChiql system, a Chinese natural language query interface to databases. Although there are sizable literatureson Chinese segmentation, they cannot satisfy particular requirements in this system. The novel word-segmentation algorithm is based on the database semantics,namely Semantic Conceptual Model (SCM) for specific domain knowledge. Basedon SCM, the segmenter labels the database semantics to words directly, which easesthe disambiguation and translation (from natural language to database query) inNChiql.展开更多
Chinese word segmentation plays an important role in search engine,artificial intelligence,machine translation and so on.There are currently three main word segmentation algorithms:dictionary-based word segmentation a...Chinese word segmentation plays an important role in search engine,artificial intelligence,machine translation and so on.There are currently three main word segmentation algorithms:dictionary-based word segmentation algorithms,statistics-based word segmentation algorithms,and understandingbased word segmentation algorithms.However,few people combine these three methods or two of them.Therefore,a Chinese word segmentation model is proposed based on a combination of statistical word segmentation algorithm and understanding-based word segmentation algorithm.It combines Hidden Markov Model(HMM)word segmentation and Bi-LSTM word segmentation to improve accuracy.The main method is to make lexical statistics on the results of the two participles,and to choose the best results based on the statistical results,and then to combine them into the final word segmentation results.This combined word segmentation model is applied to perform experiments on the MSRA corpus provided by Bakeoff.Experiments show that the accuracy of word segmentation results is 12.52%higher than that of traditional HMM model and 0.19%higher than that of BI-LSTM model.展开更多
Automatic translation of Chinese text to Chinese Braille is important for blind people in China to acquire information using computers or smart phones. In this paper, a novel scheme of Chinese-Braille translation is p...Automatic translation of Chinese text to Chinese Braille is important for blind people in China to acquire information using computers or smart phones. In this paper, a novel scheme of Chinese-Braille translation is proposed. Under the scheme, a Braille word segmentation model based on statistical machine learning is trained on a Braille corpus, and Braille word segmentation is carried out using the statistical model directly without the stage of Chinese word segmentation. This method avoids establishing rules concerning syntactic and semantic information and uses statistical model to learn the rules stealthily and automatically. To further improve the performance, an algorithm of fusing the results of Chinese word segmentation and Braille word segmentation is also proposed. Our results show that the proposed method achieves accuracy of 92.81% for Braille word segmentation and considerably outperforms current approaches using the segmentation-merging scheme.展开更多
Automatic word-segmentation is widely used in the ambiguity cancellation when processing large-scale real text,but during the process of unknown word detection in Chinese word segmentation,many detected word candidate...Automatic word-segmentation is widely used in the ambiguity cancellation when processing large-scale real text,but during the process of unknown word detection in Chinese word segmentation,many detected word candidates are invalid.These false unknown word candidates deteriorate the overall segmentation accuracy,as it will affect the segmentation accuracy of known words.In this paper,we propose several methods for reducing the difficulties and improving the accuracy of the word-segmentation of written Chinese,such as full segmentation of a sentence,processing the duplicative word,idioms and statistical identification for unknown words.A simulation shows the feasibility of our proposed methods in improving the accuracy of word-segmentation of Chinese.展开更多
针对畜禽疫病文本语料匮乏、文本内包含大量疫病名称及短语等未登录词问题,提出了一种结合词典匹配的BERT-BiLSTM-CRF畜禽疫病文本分词模型。以羊疫病为研究对象,构建了常见疫病文本数据集,将其与通用语料PKU结合,利用BERT(Bidirectiona...针对畜禽疫病文本语料匮乏、文本内包含大量疫病名称及短语等未登录词问题,提出了一种结合词典匹配的BERT-BiLSTM-CRF畜禽疫病文本分词模型。以羊疫病为研究对象,构建了常见疫病文本数据集,将其与通用语料PKU结合,利用BERT(Bidirectional encoder representation from transformers)预训练语言模型进行文本向量化表示;通过双向长短时记忆网络(Bidirectional long short-term memory network,BiLSTM)获取上下文语义特征;由条件随机场(Conditional random field,CRF)输出全局最优标签序列。基于此,在CRF层后加入畜禽疫病领域词典进行分词匹配修正,减少在分词过程中出现的疫病名称及短语等造成的歧义切分,进一步提高了分词准确率。实验结果表明,结合词典匹配的BERT-BiLSTM-CRF模型在羊常见疫病文本数据集上的F1值为96.38%,与jieba分词器、BiLSTM-Softmax模型、BiLSTM-CRF模型、未结合词典匹配的本文模型相比,分别提升11.01、10.62、8.3、0.72个百分点,验证了方法的有效性。与单一语料相比,通用语料PKU和羊常见疫病文本数据集结合的混合语料,能够同时对畜禽疫病专业术语及疫病文本中常用词进行准确切分,在通用语料及疫病文本数据集上F1值都达到95%以上,具有较好的模型泛化能力。该方法可用于畜禽疫病文本分词。展开更多
基于口令的认证是常见的身份认证机制。然而,大规模口令泄露事件时有发生,表明口令仍面临着被猜测或者盗用等风险。由于口令可以被视作一种特殊的自然语言,近年来运用自然语言处理技术进行口令分析的研究工作逐渐展开。目前少有工作在...基于口令的认证是常见的身份认证机制。然而,大规模口令泄露事件时有发生,表明口令仍面临着被猜测或者盗用等风险。由于口令可以被视作一种特殊的自然语言,近年来运用自然语言处理技术进行口令分析的研究工作逐渐展开。目前少有工作在大语言模型(LLM,large language model)上探究口令文本分词粒度对口令分析效果的影响。为此,提出了基于LLM的多粒度口令分析框架,总体上沿用预训练范式,在大量未标记数据集上自主学习口令分布先验知识。该框架由同步网络、主干网络、尾部网络3个模块构成。其中,同步网络模块实现了char-level、template-level和chunk-level这3种粒度的口令分词,并提取了口令的字符分布、结构、词块组成等特征知识;主干网络模块构建了通用的口令模型来学习口令组成规律;尾部网络模块生成了候选口令对目标库进行猜测分析。在Tianya、Twitter等8个口令库上进行大量实验,分析总结了多粒度分词下所提框架在不同语言环境中的口令分析效果。实验结果表明,在中文用户场景中,基于char-level和chunk-level分词的框架口令分析性能接近一致,且显著优于基于template-level分词的框架;在英文用户场景中,基于chunk-level分词的框架口令分析性能最佳。展开更多
基金Supported by the National Natural Science Foundation of China(No.61303179,U1135005,61175020)
文摘A local and global context representation learning model for Chinese characters is designed and a Chinese word segmentation method based on character representations is proposed in this paper.First,the proposed Chinese character learning model uses the semantics of local context and global context to learn the representation of Chinese characters.Then,Chinese word segmentation model is built by a neural network,while the segmentation model is trained with the character representations as its input features.Finally,experimental results show that Chinese character representations can effectively learn the semantic information.Characters with similar semantics cluster together in the visualize space.Moreover,the proposed Chinese word segmentation model also achieves a pretty good improvement on precision,recall and f-measure.
基金supported in part by National Science Foundation of China under Grants No. 61303105 and 61402304the Humanity & Social Science general project of Ministry of Education under Grants No.14YJAZH046+2 种基金the Beijing Natural Science Foundation under Grants No. 4154065the Beijing Educational Committee Science and Technology Development Planned under Grants No.KM201410028017Beijing Key Disciplines of Computer Application Technology
文摘ESA is an unsupervised approach to word segmentation previously proposed by Wang, which is an iterative process consisting of three phases: Evaluation, Selection and Adjustment. In this article, we propose Ex ESA, the extension of ESA. In Ex ESA, the original approach is extended to a 2-pass process and the ratio of different word lengths is introduced as the third type of information combined with cohesion and separation. A maximum strategy is adopted to determine the best segmentation of a character sequence in the phrase of Selection. Besides, in Adjustment, Ex ESA re-evaluates separation information and individual information to overcome the overestimation frequencies. Additionally, a smoothing algorithm is applied to alleviate sparseness. The experiment results show that Ex ESA can further improve the performance and is time-saving by properly utilizing more information from un-annotated corpora. Moreover, the parameters of Ex ESA can be predicted by a set of empirical formulae or combined with the minimum description length principle.
文摘To solve the complicated feature extraction and long distance dependency problem in Word Segmentation Disambiguation (WSD), this paper proposes to apply rough sets in WSD based on the Maximum Entropy model. Firstly, rough set theory is applied to extract the complicated features and long distance features, even from noise or inconsistent corpus. Secondly, these features are added into the Maximum Entropy model, and consequently, the feature weights can be assigned according to the performance of the whole disambiguation model. Finally, the semantic lexicon is adopted to build class-based rough set features to overcome data sparseness. The experiment indicated that our method performed better than previous models, which got top rank in WSD in 863 Evaluation in 2003. This system ranked first and second respectively in MSR and PKU open test in the Second International Chinese Word Segmentation Bakeoff held in 2005.
文摘Chinese word segmentation is the basis of natural language processing. The dictionary mechanism significantly influences the efficiency of word segmentation and the understanding of the user’s intention which is implied in the user’s query. As the traditional dictionary mechanisms can't meet the present situation of personalized mobile search, this paper presents a new dictionary mechanism which contains the word classification information. This paper, furthermore, puts forward an approach for improving the traditional word bank structure, and proposes an improved FMM segmentation algorithm. The results show that the new dictionary mechanism has made a significant increase on the query efficiency and met the user’s individual requirements better.
基金supported by the National Natural Science Foundation of China under Grant Nos.61751201 arid 61672162the Shanghai Municipal Science and Technology Major Project under Grant Nos.2018SHZDZX01 and ZJLab.
文摘Semi-Markov conditional random fields(Semi-CRFs)have been successfully utilized in many segmentation problems,including Chinese word segmentation(CWS).The advantage of Semi-CRF lies in its inherent ability to exploit properties of segments instead of individual elements of sequences.Despite its theoretical advantage,Semi-CRF is still not the best choice for CWS because its computation complexity is quadratic to the sentenced length.In this paper,we propose a simple yet effective framework to help Semi-CRF achieve comparable performance with CRF-based models under similar computation complexity.Specifically,we first adopt a bi-directional long short-term memory(BiLSTM)on character level to model the context information,and then use simple but effective fusion layer to represent the segment information.Besides,to model arbitrarily long segments within linear time complexity,we also propose a new model named Semi-CRF-Relay.The direct modeling of segments makes the combination with word features easy and the CWS performance can be enhanced merely by adding publicly available pre-trained word embeddings.Experiments on four popular CWS datasets show the effectiveness of our proposed methods.The source codes and pre-trained embeddings of this paper are available on https://github.com/fastnlp/fastNLP/.
文摘In this paper a novel word-segmentation algorithm is presented todelimit words in Chinese natural language queries in NChiql system, a Chinese natural language query interface to databases. Although there are sizable literatureson Chinese segmentation, they cannot satisfy particular requirements in this system. The novel word-segmentation algorithm is based on the database semantics,namely Semantic Conceptual Model (SCM) for specific domain knowledge. Basedon SCM, the segmenter labels the database semantics to words directly, which easesthe disambiguation and translation (from natural language to database query) inNChiql.
基金a National Nature Science Fund Project(61661051)Key Laboratory of Education Information of Nationalities Ministry of Education+2 种基金Yunnan Key Laboratory of Smart EducationProgram for innovative research team (in Scienceand Technology) in University of Yunnan ProvinceKunming Key Laboratory of EducationInformation.
文摘Chinese word segmentation plays an important role in search engine,artificial intelligence,machine translation and so on.There are currently three main word segmentation algorithms:dictionary-based word segmentation algorithms,statistics-based word segmentation algorithms,and understandingbased word segmentation algorithms.However,few people combine these three methods or two of them.Therefore,a Chinese word segmentation model is proposed based on a combination of statistical word segmentation algorithm and understanding-based word segmentation algorithm.It combines Hidden Markov Model(HMM)word segmentation and Bi-LSTM word segmentation to improve accuracy.The main method is to make lexical statistics on the results of the two participles,and to choose the best results based on the statistical results,and then to combine them into the final word segmentation results.This combined word segmentation model is applied to perform experiments on the MSRA corpus provided by Bakeoff.Experiments show that the accuracy of word segmentation results is 12.52%higher than that of traditional HMM model and 0.19%higher than that of BI-LSTM model.
基金Fthe National Key Technology R&D Program of China(No.2014BAK15B02)the National Natural Science Foundation of China(No.61202209)
文摘Automatic translation of Chinese text to Chinese Braille is important for blind people in China to acquire information using computers or smart phones. In this paper, a novel scheme of Chinese-Braille translation is proposed. Under the scheme, a Braille word segmentation model based on statistical machine learning is trained on a Braille corpus, and Braille word segmentation is carried out using the statistical model directly without the stage of Chinese word segmentation. This method avoids establishing rules concerning syntactic and semantic information and uses statistical model to learn the rules stealthily and automatically. To further improve the performance, an algorithm of fusing the results of Chinese word segmentation and Braille word segmentation is also proposed. Our results show that the proposed method achieves accuracy of 92.81% for Braille word segmentation and considerably outperforms current approaches using the segmentation-merging scheme.
文摘Automatic word-segmentation is widely used in the ambiguity cancellation when processing large-scale real text,but during the process of unknown word detection in Chinese word segmentation,many detected word candidates are invalid.These false unknown word candidates deteriorate the overall segmentation accuracy,as it will affect the segmentation accuracy of known words.In this paper,we propose several methods for reducing the difficulties and improving the accuracy of the word-segmentation of written Chinese,such as full segmentation of a sentence,processing the duplicative word,idioms and statistical identification for unknown words.A simulation shows the feasibility of our proposed methods in improving the accuracy of word-segmentation of Chinese.
文摘针对畜禽疫病文本语料匮乏、文本内包含大量疫病名称及短语等未登录词问题,提出了一种结合词典匹配的BERT-BiLSTM-CRF畜禽疫病文本分词模型。以羊疫病为研究对象,构建了常见疫病文本数据集,将其与通用语料PKU结合,利用BERT(Bidirectional encoder representation from transformers)预训练语言模型进行文本向量化表示;通过双向长短时记忆网络(Bidirectional long short-term memory network,BiLSTM)获取上下文语义特征;由条件随机场(Conditional random field,CRF)输出全局最优标签序列。基于此,在CRF层后加入畜禽疫病领域词典进行分词匹配修正,减少在分词过程中出现的疫病名称及短语等造成的歧义切分,进一步提高了分词准确率。实验结果表明,结合词典匹配的BERT-BiLSTM-CRF模型在羊常见疫病文本数据集上的F1值为96.38%,与jieba分词器、BiLSTM-Softmax模型、BiLSTM-CRF模型、未结合词典匹配的本文模型相比,分别提升11.01、10.62、8.3、0.72个百分点,验证了方法的有效性。与单一语料相比,通用语料PKU和羊常见疫病文本数据集结合的混合语料,能够同时对畜禽疫病专业术语及疫病文本中常用词进行准确切分,在通用语料及疫病文本数据集上F1值都达到95%以上,具有较好的模型泛化能力。该方法可用于畜禽疫病文本分词。
文摘基于口令的认证是常见的身份认证机制。然而,大规模口令泄露事件时有发生,表明口令仍面临着被猜测或者盗用等风险。由于口令可以被视作一种特殊的自然语言,近年来运用自然语言处理技术进行口令分析的研究工作逐渐展开。目前少有工作在大语言模型(LLM,large language model)上探究口令文本分词粒度对口令分析效果的影响。为此,提出了基于LLM的多粒度口令分析框架,总体上沿用预训练范式,在大量未标记数据集上自主学习口令分布先验知识。该框架由同步网络、主干网络、尾部网络3个模块构成。其中,同步网络模块实现了char-level、template-level和chunk-level这3种粒度的口令分词,并提取了口令的字符分布、结构、词块组成等特征知识;主干网络模块构建了通用的口令模型来学习口令组成规律;尾部网络模块生成了候选口令对目标库进行猜测分析。在Tianya、Twitter等8个口令库上进行大量实验,分析总结了多粒度分词下所提框架在不同语言环境中的口令分析效果。实验结果表明,在中文用户场景中,基于char-level和chunk-level分词的框架口令分析性能接近一致,且显著优于基于template-level分词的框架;在英文用户场景中,基于chunk-level分词的框架口令分析性能最佳。