PACLIC 20 / Wuhan, China / 1-3 November, 200

Fan Xiaozhong

Yin Jihao

Yu Jiangde

Zhang Kaixuan

English

PACLIC 20 / Wuhan, China / 1-3 November, 2006conference pape

Yin, Jihao

50196

Fan, Xiaozhong

50197

Zhang, Kaixuan

50198

Yu, Jiangde

50199

Waseda University Repository

Chinese Organization Name Recognition Using Chunk Analysis  Jihao YinP1P, Xiaozhong FanP1P, Kaixuan ZhangP2P, Jiangde Yu P1,3 P P1P Department of Computer Science and Engineering, Beijing Institute of Technology, Beijing, 100081, China;  P2PLiaoning Technical University, Fuxin, Liaoning, 123000, China;  P3P Department of Computer Science, Anyang Teachers’ College, Anyang, Henan, 455000, China yin_jhgg@bit.edu.cn  Abstract. A simplified N-best cascade model is put forward about Chinese organization names automatic recogni-tion. This model can do words segmentation, part-of-speech tag, chunks analysis and Chinese organization names recognition. The N-best cascade method can limit not only the errors propagation but also the search space. In the experiments, we integrate heuristic information and Organization name abbreviations processing into the model to achieve the better experiment results. The last precision and recall of Chinese organization names recognition are 92.31% and 81.01% in IEER99 newswire test set. Keywords: Chunk analysis; Chinese organization names recognition; N-best cascade model; Heuristic information  1 Introduction Named entity recognition is a foundational job in natural language processing, is an important pretreat-ment module too. Generally, the assignment of named entity recognition is identifying person names, location names, organization names, digital and time expressionP[1]P from a text. Person names, location names and organization names are not only difficult but also important. Many researches about person names and location names have been done, and their precision and re-call have meet practical need. But organization names recognition cannot satisfy need whether in theory or in practiceP[2]P. Many scholars have done some researches about Chinese chunk analysis. GuoDong Zhou et al identi-fied English named entities using an HMM-based chunk taggerP[3]P. But the study of Chinese organization names recognition using chunk analysis technology is a little. Noun-chunk is almost like the structure of Chinese organization names, so it is feasible to identify Chinese organization names using Noun-chunk analysis technology. Based on Chinese chunk analysis technology and name entity recognition, we put forward a simpli-fied N-best cascade model by mining the characters of Chinese organization names. In the experiments, we added heuristic information and organization name abbreviations processing, which improved the precision and recall of Chinese organization names recognition obviously. 2 Noun-chunk Automatic recognition 2.1 Noun-chunk Definition Noun-chunk usually includes noun and its former modifier. The former modifier includes number, ad-jective and noun, but excludes “的” structure. The modifier and center word of “的” structure fall into the different chunks separately[4]. (1) Center word of noun-chunk 347The center words of noun-chunk must be nouns. According to the part-of-speech tag criterion of Beijing University, the center words include: noun (n), person name (nr), location name (ns), proper noun (nz), gerund (vn), noun element (Ng), noun abbreviation (j) etc. For example: [NP 中国/ns 宋庆龄/nr 基金会/n ]; [NP 中共/j 北京/ns 市委/n ]. (2) Modifier One noun-chunk cannot include the other noun-chunk. The modifier must include: number phrase, ad-jective phrase, noun, pronoun. For example: [NP 第一/m 汽车/n 制造/vn 厂/Ng] 2.2 HMM-based Chunk Analysis Hidden Markov Model (HMM) is simple and feasible, so it is applied in speech recognition and natural language processing generally. HMM can observe the surface information, but cannot ob-serve the language structure. The chunk analysis is surface and linear, and the chunk interior struc-ture doesn’t need be recognized, so identifying chunks using HMM is proper P[5] P. 2.2.1 HMM Theory on Chunk Analysis Given tagged chunk corpusP[4] P, we can mine some useful linguistics knowledge from the chunk corpus. Then chunks can be identified automatically using these linguistics knowledge. Given a Chinese words sequence ),,( 21 mwwwW L , and one of possible part-of-speech tag results is ),,( 21 mTTTT L . We have mined the linguistics rule-set R, so the chunks recognition can be interpreted to recognize all of chunks ),,( 21 krrrC L  from segmented and tagged text S=(W, T) using rule-set R. Ac-cording to statistics, our assignment is to search the most optimal chunk sequence from the given text S=(W, T). ),|(maxarg     ),|(maxarg 21 TWrrrPTWCPC kRrC iL∈∗ ==      （1） Equation (1) can be transformed into Equation (2) using the Bayes formula. ),(),(),|(maxarg      ),|(maxargTWPTCPTCWPTWCPCCC==∗      （2） P(W,T) is invariable in the process of chunks analysis because word segmentation and part-of-speech tag have been completed before Chinese chunks recognition. So Equation (2) can be transformed into Equa-tion (3).                                              （3） P(W|C,T) denotes probability of words sequence in a generative chunk, and it corresponds the observa-tion generative probability in HMM model. It can be established using unigram model, as shown in Equation (4).                                                             （4） where m is chunk boundary coding; x is chunk type; t is part of speech. P(C, T) corresponds the states transform probability. It can be established using bigram model, as shown in Equation (5). ),(),|(maxarg TCPTCWPCC=∗∏=≈miiiii txmwPTCWP1),,|(),|(348∏=−≈kiii rrPrPTCP211 )|()(),(            （5） So the chunk analysis model is a typical HMM model. All of part-of-speech rules compose state set, and each word corresponds its state observation. 2.2.2 Model Estimation We adopt Maximum Likelihood Estimate (MLE) method to estimate each probability of Equation (4) and Equation (5) from the training corpus. ),,(),,,(),,|(iiiiiiiiiii txmcounttxmwcounttxmwP =     （6） )(),()|(111−−− =iiiii rcountrrcountrrP             （7） where count(.) denotes appearance times of parameters in bracket in training corpus. 2.2.3 Chunks Recognition HMM model decoding usually adopts Viterbi algorithm, which is a dynamic programming method, in-cluding three parts: initialization, recursion and back-off. The best chunks recognition results can be ac-quired using Viterbi algorithm. 3 Chunk Analysis in Chinese Organization Name Recognition We adopt chunk analysis technology in Chinese organization names recognition because of the compa-rability between noun-chunk definition and Chinese organization names structure. We propose a simpli-fied N-best cascade model. 3.1 Algorithm Realization Given a sequence of Chinese characters, the Chinese organization names recognition process consists of the following four steps: Step 1: Word segmentations. All possible word segmentations are generated using a Chinese lexicon. We don’t explain word segmentation processing because its technology is maturate. Step 2: Part-of-speech tagging. Part-of-speech tagging use HMM model too. As is shown in Equation (8). )()|(maxarg      )|(maxarg TPTWPWTPTTT==∗    （8） P(W|T) denotes the probability of generative words sequence based on part-of-speech sequence, and it corresponds the observation generative probability in HMM model. It can be established using unigram model, as shown in Equation (4).   ∏=≈miii twPTWP1)|()|(                            （9） P(T) corresponds the states transform probability. It can be established using bigram model, as shown in Equation (5). ∏=−≈miii ttPtPTP211 )|()()(                          (10) P(wBiB|tBiB) and P(tBiB| tBi-1B) can be acquired using Maximum Likelihood Estimation method from training cor-349pus. Step 3: Noun-Chunk Recognition (Referring to section 2.2) Step 4: Simplified N-best cascade model is established, which can accomplish words segmentation, part-of-speech tag, chunks analysis unto Chinese organization names recognition. This model can be shown in Equation (11). )],,|(),|()|()|([maxarg      )|(maxarg,,,CTWOPTWCPWTPSWPSOPOOCTWO==∗    （11） where W is the words segmentation result, T is the part-of-speech tag result, C is the chunks analysis re-sult, O is Chinese organization names recognition result. Every probability of this model is acquired in various conditions, and its search space is huge, so this model cannot apply in practice. We must predi-gest this model, reduce search space, and adjust each probability weight. So we adopt logarithm linear model to solve these problems, which is shown in Equation (12): )],,|(log),|(log)|(log)|(log[maxarg )|(log  maxarg4321,,,CTWOPTWCPWTPSWPSOPOOCTWOλλλλ+++==∗                       （12） The most direct predigestion is that only one best result is generated from every phase, which in-cludes words segmentation, part-of-speech tag, chunks recognition and Chinese organization names recognition. But this method can lead the errors propagation easily. The errors in words segmentation, part-of-speech tag and chunks recognition can impact Chinese organization names recognition resultP[6] P. We adopt neutral method, i.e. N-best results are acquired from previous phases, only one best result is generated in Chinese organization names recognition phase. We select words segmentation, part-of-speech tag and chunks recognition which generate the best Chinese organization names recogni-tion as the last results. This method is called N-best cascade, as is shown in Equation (13). ),,|(logmaxarg),|(logmaxarg)|(logargNmax)|(logargNmax ***4,,**3,*21************iiiCCTTWWiiTTWWiWWWCTWOPTWCPNWTPSWPOiiiiiiλλλλ∈∈∈∈∈∈∗+++≈   (13) where WP*P is N-best words segmentation results, TP*P is N-best part-of-speech tag results based on N-best words segmentation results, CP*P is N-best chunks recognition results based on N-best part-of-speech tag results, O is the only best Chinese organization names recognition result based on N-best chunks recog-nition results. This method’s theory is that N-best results excel one best result in identifying Chinese organization names, so it can limit the errors propagation. And the search space can be reduced greatly because we only search N-best results in each phase. 3.2 Improvement There are some problems only using the simplified N-best cascade model in Chinese organization names recognition. First, many irrelevant organization candidates will be produced, which results in great search space. Second, the abbreviations of organization names cannot be handled effectively. In the fol-lowing, we provide solutions about these problems. 3.2.1 Heuristic Information In order to overcome the redundant candidates generation, the heuristic information is introduced into 350the simplified N-best cascade model. Chinese organization name keywords list includes 1,355 wordsP[7] P (e.g. 大学(university), 公司(corporation)). A keyword and its front two to six words can be considered to construct a Chinese organization name. The heuristic information can limit redundant entities genera-tion. If one organization name doesn’t include the words in keyword list, this organization name will be eliminated directly. 3.2.2 Dealing with Abbreviation We found many errors when identifying organization name abbreviations. Therefore, some strategies are adopted to deal with abbreviations. We use the simple rule-based method to identify organization name abbreviations. 4 Experiments and Analysis 4.1 Evaluation Metric and Data Sets This model was evaluated in terms of precision (P), recall (R) and F-measure (F1). responsesofnumberresponsescorrectofnumberP =      (14) namesonorganizatiallofnumberresponsescorrectofnumberR =     (15) RP)(RP1)(22+×××+= ββF  （ 1=β ）       (16) The training set is chunk corpus of Microsoft Research Asia, including 502,141 words, 259,843 chunks. In the corpus, Chinese organization name chunks were marked especially. The test sets select IEER99 newswire test set and “Xinhuanet---Chinese simplified webpages” (http://www.xinhuanet.com) tagged texts, as is shown in Table 1. Table1. Chinese simplified edition of xinhuanet ID Domain Number of ORG Size 1 2 3 4 5 Sports Economy Politics EntertainmentEducation Total 643 356 227 152 97 1475 117K 103K 126K 108K 145K 599 K 4.2 Experiments We conduct incrementally the following three experiments: (1) Simplified N-best cascade model, we view the results as the baseline performance; (2) Integrating heuristic information into (1); (3) Integrating Cache-based model with (2). 4.3 Experiment Results 4.3.1 Simplified N-best Cascade Model Based on the basic simplified N-best cascade model, we obtained the baseline performance, as is shown in Table 2. We found that the performance of IEER99 newswire test set is better than “Xinhuanet” test set because “Xinhuanet” test set is much bigger than IEER99 newswire test set (“Xinhuanet” test set in-351cludes 1475 organization names, but IEER99 test set includes 497 organization names). Table 2. Test 1 experiment results Test Set Precision(%) Recall(%) F1(%)IEER99 Xinhuanet 75.23 71.19 68.86 59.97 71.9065.104.3.2 Integrating Heuristic Information After heuristic information is added to the experiment, we found that decoding became simple and the precision of organization names recognition was improved, as is shown in Table 3. For example, the precision of organization names recognition in IEER99 test set increases from 75.23% to 87.31%. The reason is that integrating heuristic information reduces the noise influence. Table 3. Test 2 experiment results Test Set Precision(%) Recall(%) F1(%)IEER99 Xinhuanet 87.31 89.52 64.22 60.37 74.0172.11However, we noticed that the recall of organization names recognition decreased. The reason is or-ganization names without organization ending keywords were not identified. For example, “中国保险 (China assurance)” is the abbreviation of “中国保险公司 (China assurance corp.)”, so it cannot be identified. 4.3.3 Dealing with Organization Names Abbreviation In this experiment, we integrate the organization name abbreviations processing, the results are shown in Table 4. Comparing Table 3 and Table 4, we found the recall of organization names recognition in IEER99 test set increases from 64.22% to 81.01%. Table 4. Test 3 experiment results Test Set Precision(%) Recall(%) F1(%)IEER99 Xinhuanet 92.31 90.75 81.01 76.92 86.2983.264.3.4 Experiment Results and Analysis From above experiments, we can make some conclusions: (1) The simplified N-best cascade model can select the front N-best results as the candidates of the next phase, so the errors propagation will be lim-ited. Moreover, N-best results were searched in each phrase, which can reduce the search space greatly. (2) Integrating heuristic information, i.e. using keywords list and some simply rules, can restrict redun-dant organization names generation. So the precision of organization names recognition increased obvi-ously. (3) The organization name abbreviations processing can improve the recall of organization names recognition. 5 Conclusions and Future Work Based on HMM model, Chinese words segmentation, part-of-speech tag, chunks analysis and Chinese organization names recognition have been integrated into a unified framework, then we establish a sim-plified N-best cascade model. In the experiments, integrating heuristic information increases the preci-sion of organization names recognition; integrating organization name abbreviations processing in-352creases the recall of organization names recognition. In the IEER99 newswire test set, the last precision and recall of Chinese organization names recognition are 92.31% and 81.01%, each of values is higher about 9% than precision and recall of reference [8]. In the process of organization names recognition, one organization name usually appears in the dif-ferent style. This problem is named entity coreference. So we will focus more on name entity corefer-ence. Furthermore, we intend to extend our model to the specific domain for proper names recognition.  Acknowledgment. This paper is supported by the Doctor Foundation project 20050007023 of China Ministry of Education.  References 1. Sun Bin. A summarization of information extraction[J]. Language Information Processing, 2003, 2(1): 34-35. 2. Yu Hongkui, Zhang Huaping. Recognition of chinese organization name based on role tagging[A]. Proceedings of 20th International Conference on Computer Processing of Oriental Languages[C]. Beijing, China, 2003, 79-87. 3. GuoDong Zhou, Jian Su. Named entity recognition using an HMM-based chunk tagger[A]. Proceedings of the 40th Annual Meeting of ACL[C]. Philadelphia, America, 2002, 473-480. 4. Li Hongqiao. Chinese chunking and its applications[D]. Beijing: Beijing Institute of Technology, 2004. 5. L. R. Rabiner, B. H. Juang. An introduction to Hidden Markov Models[J]. IEEE ASSP Magazine, 1986, 3(1): 4-16. 6. Sabine Buchholz, Jorn Veenstra, Walter Daelemans. Cascaded grammatical relation assignment[A].  Proceed-ings of EMNLP/VLC-99[C]. University of Maryland, USA, 1999, 239-246. 7. Jian Sun, Jianfeng Gao. Chinese named entity iIdentification using class-based language model[A]. Proceedings of the 19th International Conference on Computational Linguistics[C]. Taipei, China, 2002, 24-25. 8. Youzheng Wu et.al. Chinese named entity recognition combining a statistical model with human knowledge[A]. Proceedings of the Workshop on Multilingual and Mixed-language Named Entity Recognition[C], Japan, 2003, 65-72.        353

Chinese Organization Name Recognition Using Chunk Analysis

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Chinese Organization Name Recognition Using Chunk Analysis

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