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基于深度神经网络的法语定名实体辨认模子

发布时间:2020-04-05 01:06:01 文章来源:未来智讯    
    基于深度神经网络的法语定名实体辨认模子作者:未知  摘 要:现有法语定名实体辨认(NER)切磋中,机械进修模子多使用词的字符形态特性,多说话通用定名实体模子使用字词嵌入代表的语义特性,都异国综合思虑语义、字符形态和语法特性。针对上述不及,设计了一种基于深度神经网络的法语定名实体辨认模子CGCfr。起首从文本中提取单词的词嵌入、字符嵌入和语法特性向量; 然后由卷积神经网络(CNN)从单词的字符嵌入序列中提取单词的字符特性; 最终议决双向门控轮回神经网络(BiGRU)和前提随机场(CRF)分类器凭据词嵌入、字符特性和语法特性向量辨认出法语文本中的定名实体。尝试中,CGCfr在测试集的F1值可以到达82.16%,相对付机械进修模子NERCfr、多说话通用的神经网络模子LSTMCRF和Char attention模子,离别升迁了5.67、1.79和1.06个百分点。尝试了局证明,融合三种特性的CGCfr模子比其他模子更具有上风。
  关头词:定名实体辨认;法语;深度神经网络;天然说话处置;序列标注
  中图分类号:TP391.1
  文献标志码:A
  Abstract: In the existing French Named Entity Recognition (NER) research, the machine learning models mostly use the character morphological features of words, and the multilingual generic named entity models use the semantic features represented by word embedding, both without taking into account the semantic, character morphological and grammatical features comprehensively. Aiming at this shortcoming, a deep neural network based model CGCfr was designed to recognize French named entity. Firstly, word embedding, character embedding and grammar feature vector were extracted from the text. Then, character feature was extracted from the character embedding sequence of words by using Convolution Neural Network (CNN). Finally, Bidirectional Gated Recurrent Unit Network (BiGRU) and Conditional Random Field (CRF) were used to label named entities in French text according to word embedding, character feature and grammar feature vector. In the experiments, F1 value of CGCfr model can reach 82.16% in the test set, which is 5.67 percentage points, 1.79 percentage points and 1.06 percentage points higher than that of NERCfr, LSTM(Long ShortTerm Memory network)CRF and Char attention models respectively. The experimental results show that CGCfr model with three features is more advantageous than the others.
  英文关头词Key words: Named Entity Recognition (NER); French; neural network; Natural Language Processing (NLP); sequence labeling
  0 引言
  定名實体辨认(Named Entity Recognition, NER)是指从文本中辨认出特定类型事件名称或者标记的过程[1]。它提掏出更具有意义的人名、组织名、地名等,使得后续的天然说话处置义务能凭据定名实体进一步猎取必要的信息。跟着全球化成长,列国之间信息互换日益频仍。相对付中文,外语信息更能影响其他国度对中国的主见,多说话舆情剖析应运而生。法语在非英语的语种中影响力相对较大,其文本是多语种舆情剖析中严重指标之一。法语NER作为法语文天职析的根本义务,严重性不行忽略。
  专门针对法语NER进行的切磋较少,早期切磋首要是基于准则和辞书的要领[2], 后来,平日将人造选择的特性输入到机械进修模子来辨认出文本中存在的定名实体[3-7]。Azpeitia等[3]提议了NERCfr模子,模子选取最大熵要领来辨认法语定名实体,用到的特性包罗词后缀、字符窗口、临近词、词前缀、单词长度和首字母是否大写等。该要领取得了不错的了局,但能够看出用到的特性多为单词的形态布局特性而非语义特性,缺乏语义特性可能限定了模子的辨认正确率。
  近几年深度神经网络在天然说话处置范畴取得了很好的效率: Hammerton[8]将是非时印象网络(Long ShortTerm Memory network, LSTM)用于英语NER; Rei等[9]提议了多说话通用的Char attention模子,行使Attention机制融合词嵌入和字符嵌入,将其作为特性输入到双向是非时印象网络(Bidirectional Long ShortTerm Memory network, BiLSTM)中,获得序列标注发生的定名实体; Lample等[10]提议BiLSTM后接前提随机场(Conditional Random Field, CRF)的LSTMCRF模子,它也是多说话通用的,使用了字词嵌入作为特性来辨认英语的定名实体, 但LSTMCRF模子应用在法语上,和英语差距较大,这个问题可能是由于异国用到该说话的语法特性,终究法语语法的纷乱水平大幅跨越英语。
         為了在抽取过程中两全语义、字符形态和语法特性,更为正确地抽取法语的定名实体,本文设计了模子CGCfr。该模子使用词嵌入表现文本中单词的语义特性,使用卷积神经网络(Convolutional Neural Network, CNN)提取字符嵌入包含的单词字符形态特性以及预先提取的法语语法特性,拼接后输入到双向门控轮回网络(Gated Recurrent Unit Neural Network, GRU)和前提随机场联合的复合网络中,来辨认出法语定名实体。CGCfr充分行使了这些特性,议决尝试表明了每种特性的进献度,并与其他模子进行比力表明了融合三种特性的CGCfr模子更具有上风。除此之外,本文进献了一个法语的数据集,蕴含1005篇文章,29016个实体, 添加了法语定名实体辨认的数据集,使得后续能够有更多的切磋不被数据集的问题困扰。
  4 结语
  本文设计了用于法语定名实体辨认的深度神经网络CGCfr模子,并构建了一个法语定名实体辨认数据集。CGCfr模子将法语文本中单词的词嵌入作为语义特性,从单词对应的字符嵌入序列提取单词的形态布局特性,联合语法特性完成对定名实体的辨认。这添加了传通盘计机械进修要领中特性的多样性,雄厚了特性的内在, 也幸免了多说话通用要领对法语语法的忽略。尝试对照模子中各个特性的进献度,验证了它们的有用性;还将CGCfr模子与最大熵模子NERCfr、多说话通用模子Char attention和LSTMCRF对照。尝试了局证明,CGCfr模子相对三者的F1值都有提高,验证了融合三种特性的本文模子在法语定名实体辨认上的有用性,进一步提高了法语定名实体的辨认率。
  然而,本文模子也存在着不及,在法语文本中组织名的辨认率比拟其余两种定名实体类型差距较大,模子对体例存在较大改变的定名实体类型的辨认效率不是很好;其次,相对付英语较高的定名实体辨认正确率,法语定名实体辨认还有较大的升迁空间。
  参考文献 (References)
  [1] NADEAU D, SEKINE S. A survey of named entity recognition and classification[J]. Lingvisticae Investigationes, 2007, 30(1): 3-26.
  [2] WOLINSKI F, VICHOT F, DILLET B. Automatic processing of proper names in texts[C]// Proceedings of the 7th Conference on European Chapter of the Association for Computational Linguistics. San Francisco, CA: Morgan Kaufmann Publishers, 1995: 23-30.
  [3] AZPEITIA A, CUDADROS M, GAINES S, et al. NERCfr: supervised named entity recognition for French[C]// TSD 2014: Proceedings of the 2014 International Conference on Text, Speech and Dialogue. Berlin: Springer, 2014: 158-165.
  [4] POIBEAU T. The multilingual named entity recognition framework[C]// Proceedings of the 10th Conference on European Chapter of the Association for Computational Linguistics. Stroudsburg, PA: Association for Computational Linguistics, 2003: 155-158.
  [5] PETASIS G, VICHOT F, WOLINSKI F, et al. Using machine learning to maintain rulebased namedentity recognition and classification systems[C]// Proceedings of the 39th Annual Meeting on Association for Computational Linguistics. Stroudsburg, PA: Association for Computational Linguistics, 2001: 426-433.
  [6] WU D, NGAI G, CARPUAT M. A stacked, voted, stacked model for named entity recognition[C]// Proceedings of the 7th Conference on Natural Language Learning at HLT. Stroudsburg, PA: Association for Computational Linguistics, 2003: 200-203.
  [7] NOTHMAN J, RINGLAND N, RADFORD W, et al. Learning multilingual named entity recognition from Wikipedia[J]. Artificial Intelligence, 2013, 194:151-175.
         [8] HAMMERTON J. Named entity recognition with long shortterm memory[C]// Proceedings of the 7th Conference on Natural Language Learning at HLT. Stroudsburg, PA: Association for Computational Linguistics, 2003: 172-175.
  [9] REI M, CRICHTON G, PYYSALO S. Attending to characters in neural sequence labeling models[J/OL]. arXiv Preprint, 2016, 2016: arXiv:1611.04361[2016-11-14]. https://arxiv.org/abs/1611.04361.
  [10] LAMPLE G, BALLESTEROS M, SUBRAMANIAN S, et al. Neural architectures for named entity recognition[C]// Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics. Stroudsburg, PA: Association for Computational Linguistics, 2016: 260-270.
  [11] LE Q, MIKOLOV T. Distributed representations of sentences and documents[C]// Proceedings of the 31st International Conference on Machine Learning. New York: JMLR.org, 2014: 1188-1196.
  [12] PENNINGTON J, SOCHER R, MANNING C. Glove: global vectors for word representation[C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA: Association for Computational Linguistics, 2014: 1532-1543.
  [13] SANTOS C D, ZADROZNY B. Learning characterlevel representations for partofspeech tagging[C]// Proceedings of the 31st International Conference on Machine Learning. New York: JMLR.org, 2014: 1818-1826.
  [14] CHO K, van MERRIENBOER B, GULCEHRE C, et al. Learning phrase representations using RNN encoderdecoder for statistical machine translation[C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA: Association for Computational Linguistics, 2014: 1724-1734.
  [15] SANG E F, VEENSTRA J. Representing text chunks[C]// Proceedings of the 9th Conference on European Chapter of the Association for Computational Linguistics. Stroudsburg, PA: Association for Computational Linguistics, 1999: 173-179.
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