• <tr id="yyy80"></tr>
  • <sup id="yyy80"></sup>
  • <tfoot id="yyy80"><noscript id="yyy80"></noscript></tfoot>
  • 99热精品在线国产_美女午夜性视频免费_国产精品国产高清国产av_av欧美777_自拍偷自拍亚洲精品老妇_亚洲熟女精品中文字幕_www日本黄色视频网_国产精品野战在线观看 ?

    Negation scope detection with a conditional random field model①

    2017-06-27 08:09:23LydiaLazibZhaoYanyanQinBingLiuTing
    High Technology Letters 2017年2期

    Lydia Lazib, Zhao Yanyan, Qin Bing, Liu Ting

    (Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, Harbin 150001, P.R.China)

    Negation scope detection with a conditional random field model①

    Lydia Lazib, Zhao Yanyan, Qin Bing②, Liu Ting

    (Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, Harbin 150001, P.R.China)

    Identifying negation cues and their scope in a text is an important subtask of information extraction that can benefit other natural language processing tasks, including but not limited to medical data mining, relation extraction, question answering and sentiment analysis. The tasks of negation cue and negation scope detection can be treated as sequence labelling problems. In this paper, a system is presented having two components: negation cue detection and negation scope detection. In the first phase, a conditional random field (CRF) model is trained to detect the negation cues using a lexicon of negation words and some lexical and contextual features. Then, another CRF model is trained to detect the scope of each negation cue identified in the first phase, using basic lexical and contextual features. These two models are trained and tested using the dataset distributed within the *Sem Shared Task 2012 on resolving the scope and focus of negation. Experimental results show that the system outperformed all the systems submitted to this shared task.

    negation detection, negation cue detection, negation scope detection, natural language processing

    0 Introduction

    Negation, as simple as it can be in concept, is a complex and an essential phenomenon in any language. It has the ability to inverse the meaning of an affirmative statement into its opposite meaning. In a sentence, the presence of negation is indicated by the presence of a negation cue. The negation cue is a lexical element that carries negation meaning. A negation cue can occur in different forms[1]: as an explicit negation, which can be a single word negation (e.g., “no”, “not”) or a multiple words negation (e.g., “neither…nor” or “rather than”), as an implicit negation, where syntactic patterns imply negative semantics (e.g., “This movie was below my expectations.”), or as a morphological negation, where word roots are modified with a negating prefix (e.g., “un-”, “in-” or “dis-”) or negating suffix (e.g., “-less”). The scope of negation is the sequence of words in the sentence that is affected by the negation cue[2]. For example, in Sentence (1) the wordnotis the negation cue, and the discontinuous word sequences ‘Holmes’ and ‘sayanything’ form the scope.

    [Holmes] did not [say anything].

    (1)

    For many NLP (natural language processing) applications, distinguishing between affirmative and negative information is an important task. A system that does not deal with negation would treat the facts in these cases incorrectly as positives. For example, in sentiment analysis detecting the negation is a critical process, as it may change the polarity of a text and results in a wrong prediction. And in query answering systems failing to account for negation can result in giving wrong answers.

    However, most of the systems developed for processing natural language data do not consider the negation present in the sentence. Although, various works have dealt with the identification of negations and their scope in sentences, machine learning techniques started to be used since the creation of the Bioscope corpus[3]. This corpus boosted several research experiments on scope resolution. Ref.[4] proposed a supervised system that finded the negation cue and their scopes in biomedical texts. The system consists of two memory-based engines, one decides if the tokens in a sentence are negation cues, and another finds the full scope of these negation cues. Ref.[2] used a decision tree to classify whether a token is at the beginning, inside or outside a negation cue. In the scope finding task, they use three classifiers (k-nearest neighbor, SVM and CRF classifier) to predict whether a token is the first token in the scope sequence, the last or neither. And Ref.[5] proposed a new approach for tree kernel-based scopes detection by using structured syntactic parse information. Their experiments on the Bioscope corpus showed that both constituent and dependency structured syntactic parse features have the advantage in capturing the potential relationship between cues and their scopes. But the processing of these feature-rich methods takes a lot of effort and knowledge, and is time-consuming.

    Negation has also been studied in the context of sentiment analysis, as a wrong interpretation of a negated fact may lead to assign a wrong polarity to the text, and results in a wrong prediction. Ref.[6] affirmed that applying a simple rule that considers only the two words following the negation keyword as being negated by that keyword to be effective. This method yielded a significant increase in overall sentiment classification accuracy. Ref.[1] proposed a negation detection system based on CRF, modeled using features from an English dependency parser. And report the impact of accurate negation detection on state-of-the-art sentiment analysis system. Their system improved the precision of positive sentiment polarity detection by 35.9% and negative sentiment polarity detection by 46.8%. Ref.[7] proposed a sophisticated approach to identify negation scopes for Twitter sentiment analysis. They incorporated to their sentiment classifier several features that benefit from negation scope detection. The results confirm that taking negation into account improves sentiment classification performance significantly. And Ref.[8] used machine learning methods to recognize automatically negative and speculative information, and incorporated their approach to a sentiment classifier. The results achieved demonstrate that accurate detection of negations is of vital importance to the sentiment classification task.

    In this paper, a system is proposed to detect the negation cues and their corresponding scopes, in respect to the closed task of the *Sem Shared Task 2012[9]. The system is divided into two cascade sub-tasks, one that detects the negation cues, and another that detects the scope of negation cues identified by the first sub-task. A CRF model is trained using lexical and contextual features on both sub-tasks. These features, compared to those used in previous work[5,10-12]are simple to process and are less time-consuming.

    This paper includes four sections. In Section 1 the approach is presented to solve the two tasks of the system. Section 2 describes the corpus used in the experiment. Then, in Section 3 different experimental settings, the experimental results and the corresponding analysis are presented. And finally, concluding remarks are contained in Section 4.

    1 System description

    The system is decomposed to identify the scope of negation into two cascade sub-tasks: negation cue detection and negation scope detection. The scope detection is dependent on the task of finding the negation cues.

    To conduct the experiment, the corpus provided by organizers of the *Sem Shared Task 2012 (will be described in Section 2) is used. In order to do a fair comparison with the work submitted during this shared task, all the requirements of the task are followed, which means that the classifier can use only the information provided in the training dataset, without using any external tool to respect the requirement of the closed task.

    The approach to detect the negation cues and their respective scope in a sentence is to consider the two tasks as sequence labelling problems. The conditional random fields (CRF)[13]classifier has proved in several previous work to be more efficient in solving this kind of problems[10-12]compared with other machine learning techniques.

    The negation cues present in the test data are identified by training a CRF model using some lexical and contextual features and a lexicon of negation cues that are present in the training data. To identify the scope of negation, a CRF model is also trained using lexical and contextual features.

    The illustration of the approach is shown in Fig.1.

    The details of the two sub-tasks are described in the subsections below.

    1.1 Negation cue detection

    In this task, all the negation cues presented in the sentences are identified. The negation cues are grouped under five types, depending upon how they are present in the data. They can be:

    ? Single word cues: not, no, never, etc.

    ? Continuous multiword cues: rather than, by no means, etc.

    ? Discontinuous multiword cues: neither…nor,etc.

    ? Prefix cues: words starting with the prefixes: in-, im-, un-, etc. e.g. impossible.

    ? Suffix cues: words ending with the suffix: -less, e.g. useless.

    Fig.1 Illustration of the whole approach of the proposed negation cue and scope detection

    Because of the restriction of the closed task of the shared task, any external dictionary or lexicon for negation words couldn’t be used. The system creates two lexicons, one corresponding to the negation words (single word, continuous multiword, and discontinuous multiword) by collecting all the negation words that can appear in the training dataset, and another corresponding to the different negation affixes (prefixes, suffixes) found in the training dataset. These two lists are shown in Table 1 and Table 2.

    The CRF model was trained on a set of lexical and contextual features. The process of features selection started by combining different features used in some previous work similar to the task[10,11]. This feature set has been polished from the features that was proved empirically to be useless, and added new features that improved the cue detection task. The final feature set used to train the CRF model is presented in Table 3.

    Table 1 Lexicon of negation cues (single and multi-words)

    Table 2 Lexicon of negation cues (prefixes and suffixes)

    Table 3 Feature set for negation cue detection

    The CRF model used considers the features of the current token, two previous and two forward tokens. It also uses features conjunctions by combining features of neighboring tokens, and bigram features.

    The CRF model can classify a word in a sentence as being:

    ? “O”: not part of any negation cue

    ? “NEG”: as a single word negation

    ? “NEG_DIS”: as a discontinuous multiword negation

    ? “NEG_CONT”: as a continuous multiword negation

    ? “PRE”: as a prefix negation

    ? “SUF”: as a suffix negation

    The system uses the lexicon presented in Table 1 to set the values of the three features: “Match_Single”, “Multi_Continuous” or “Multi_Discontinuous”, by searching all the words in the sentences of the data that match the words in the lexicon. For each word that matches the system sets the corresponding feature to 1 and set the others to 0.

    The system uses the lexicon presented in Table 2 the same way, except that here it searches whether the words in the sentence start with a prefix, end with a suffix or are just simple words. The features modified in this case are: “StartsWithPrefix” and “EndsWithSuffix”.If the current token is detected as a prefix (or suffix) negation cue, a special treatment should be done on this token to split it into a prefix (or suffix) negation cue and its corresponding sub-token, using a simple regular expression method.

    1.2 Negation scope detection

    A CRF model is also trained to identify the scope of negation. The negation cues identified in the first sub-task are used as the new cues, and the scope of which will be identified. If a sentence contains more than one negation cue, then each one will be treated separately, by creating a new training/test instance of the same sentence for each negation cue presenting in the same sentence.

    To identify the scope, the model considers the features shown in Table 4. These features are token specific features (Token) and contextual features (e.g., relative position of the token to the cue, whether the token and the cue are in the same segment, etc.), and have the advantage in capturing the potential relationship between cues and their scopes (e.g. the number of token between the current token and the cue, the relative position of the current token from the cue: before, after or same, etc.), and make the prediction of the scope more relevant. They are also simple features, which are less time-consuming, comparing to other features used in other methods[10-12]. New features are also created by combining neighboring features and bigram features.

    Table 4 Feature set for negation scope detection

    The CRF model can classify a token in a sentence as being inside (I) or outside (O) the scope of negation. And if the negation cue starts or ends with one of the affixes (prefixes or suffixes) listed in Table 2, the scope of negation includes only the part of the negation cue excluding the affix. Thus, for each negation cue having these affixes, the affix is removed from the cue and the remaining part is considered as the scope.

    2 Data set

    Research on negation scope detection has mainly focused on the biomedical domain, and has been neglected in open-domain because of the lack of corpora. One of the only freely available corpus is the dataset released by the organizers of the *Sem Shared Task 2012. This dataset includes stories of Conan Doyle, and is annotated with negation cues and their corresponding scope, as well as the event that is negated. The cues are the words that express negation, and the scope is the part of the sentence that is affected by the negation cue. The negation event is the main event actually negated by the negation cue. Table 5 shows an example sentence from the dataset.

    Column 1: contains the name of the file

    Column 2: contains the sentence number within the file

    Column 3: contains the token number within the sentence

    Column 4: contains the token

    Column 5: contains the lemma

    Column 6: contains the part-of-speech (POS) tag

    Column 7: contains the parse tree information

    Column 8 to last:

    ? If the sentence does not contain a negation, column 8 contains “***” and there are no more columns.

    ? If the sentence does contain negations, the information for each one is provided in three columns: the cue, a word that belongs to the scope, and the negated event, respectively.

    Table 5 Example sentence annotated for negation following *Sem Shared Task 2012

    The annotation of cues and scopes is inspired by the Bioscope corpus annotation[3], but there are some differences. The first difference is that the cue is not considered to be part of the scope. The second, the scopes can be discontinuous, and includes the subject, which is not the case in the Bioscope corpus. And finally, the morphological negations are annotated as in the example in Sentence (2) below:

    [He] declares that he heard cries but [is] un [able to state from what direction they came].

    (2)

    Statistics for the corpus are presented in Table 6. More information about the annotations guidelines are provided by Ref.[14], including inter-annotator agreement.

    Table 6 Corpus statistics

    3 Experiments and Results

    The CRF model for negation cue and scope detection is trained and tested against the datasets described in Section 2. The identification of cues and scopes is evaluated using the evaluation tool provided by the organizer of the *Sem Shared Task 2012 which uses the standard precision, recall, and F-measure metrics to evaluate the system. The evaluation is performed on different levels:

    1) Cue level: the metrics are performed only for the cue detection.

    2) Scope CM (cue match): the metrics are calculated at scope level, but require a strict cue match. All tokens of the cue have to be correctly identified.

    3) Scope tokens (no cue match): the metrics are performed at the token level. The total of scope tokens in a sentence is the sum of tokens of all scopes. For example, if a sentence has 2 scopes, one with 5 tokens and another with 4, the total number of scope tokens is 9.

    The punctuation marks are ignored by the evaluation tool to relax the scope evaluation.

    The CRF++ tool is used to train the two CRF models for negation cue and negation scope detection.

    The results obtained by the system over the test data are shown in Table 7.

    Table 7 Results of negation cue and negation scope detection

    The analysis of the results obtained by each component are described in the subsections below.

    3.1 Negation cue detection

    The system achieves an F1 score of 93.69% in the task of negation cue detection using a CRF model.The effectiveness of the system is limited by the coverage of the lexicon. Due to the low coverage of the lexicon, the system fails to identify negation cues that are present only in the test data and never appear in the training data. However, the results still outperform all the results submitted to the shared task. As can be seen in Table 9, the system outperforms the system of the participant FBK[11], who got the first place on cue detection task in the shared task.

    Also, some words such asnever,nothing,not,noandwithoutare mostly present as negation cues in the data, but not always. Such in the phrasenodoubt, which is present nine times in the test data, but the wordnois a negation cue in only four of them. The wordsaveis also present once as a negation cue in the training data, but is never a negation cue in the test data. Therefore, our system invariability predicts these occurrences ofsavein the test data as negation cues.

    3.2 Negation scope detection

    The system is able to achieve an F1 score of 86.51% for negation scope detection on scope tokens level (without cue match),and an F1 score of 75.59% on scope level (with cue match). The results show that our system has a higher precision than recall in identifying the scope. As mentioned earlier, the negation cues identified in the first task are used to identify the scope of negation. Using a test data with a 7% error in negation cues as the input to this component and some of the errors of the system in predicting the scope lead to a low recall value in the scope detection.

    Table 8 shows the results of the negation scope detection system using the gold cues. These results demonstrate the effectiveness of our system on scope detection task, with an increase of almost 5% on the scope level (with cue match) and 3% on scope tokens level (without cue match).

    Table 8 Results of negation scope detection based on Gold Cues

    The results are compared with the three best work results submitted for the *Sem shared task 2012 in Table 9. The system outperforms these three best work on both cue and scope detection tasks. The participant FBK[11]also used a CRF model to identify the negation cues in a sentence, but has omitted to use the feature related to the “Token”, which is considered as the most valuable feature to identify the negation cue. For scope detection (on scope level), the participant UWashington[12]used essentially lexical and syntactic features, but has neglected the features that capture the relationship between the current token and the negation cue in a sentence. And for scope detection (on scope tokens level), the participant UiO1[15]used the SVM classifier to identify the scope of negation. Their results are outperformed by more than 3%, which emphasis the theory that the CRF classifier is more effective in resolving sequence labelling problems.

    Table 9 Performance comparison with the results of the participants of the *Sem Shared Task

    4 Conclusion

    In this paper an approach is proposed to identify the negation cue and the scope of negation in a sentence. It is shown that considering these two tasks as sequence labelling problems, and using CRF model to solve them achieves a considerable accuracy. However, the system cannot cover the negation cues that are not present in the training data. It also misclassifies some negation cues that can appear in non-negated contexts. Moreover, in order to improve the overall accuracy of the scope detection, an accurate system is needed to detect the negation cues, since the errors in the negation cue detection propagates to the identification of the scope. Using features that capture the relationship between the tokens and the negation cue are relevant in identifying the scope of negation.

    As future work, we would like to use an extensive lexicon of negation cues to better predict the negation cues. It is also intended to use the current system to solve some problems related to the negation. It is believed that this kind of system can improve the accuracy of several work that are sensitive to the polarity in the information extraction and natural language processing domain, like sentiment analysis or question answering systems.

    [ 1] Councill I G, McDonald R, Velikovich L. What's great and what's not: learning to classify the scope of negation for improved sentiment analysis. In: Proceedings of the Workshop on Negation and Speculation in Natural Language Processing, ACL, Uppsala, Sweden, 2010. 51-59

    [ 2] Morante R, Daelemans W. A metalearning approach to processing the scope of negation. In: Proceedings of the 13th Conference on Computational Natural Language Learning, Boulder, USA, 2009. 21-29

    [ 3] Szarvas G, Vincze V, Farkas R, et al. The BioScope corpus: annotation for negation, uncertainty and their scope in biomedical texts. In: Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing, Columbus, USA, 2008. 38-45

    [ 4] Morante R, Liekens A, Daelemans W. Learning the scope of negation in biomedical texts. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, Honolulu, USA, 2008. 715-724

    [ 5] Zou B, Zhou G, Zhu Q. Tree Kernel-based Negation and Speculation Scope Detection with Structured Syntactic Parse Features, In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, Seattle, USA, 2013. 968-976

    [ 6] Hogenboom A, Van Iterson P, Heerschop B, et al. Determining negation scope and strength in sentiment analysis. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC), Kyoto, Japan, 2011. 2589-2594

    [ 7] Reitan J, Faret J, Gamb?ck B, et al. Negation scope detection for twitter sentiment analysis. In: Proceedings of the 6thWorkshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA), Lisbon, Portugal, 2015. 99-108

    [ 8] Cruz N P, Taboada M, Mitkov R. A machine learning approach to negation and speculation detection for sentiment analysis.Journaloftheassociationforinformationscienceandtechnology, 2016, 67(9): 2118-2136

    [ 9] Morante R, Blanco E. *SEM 2012 shared task: Resolving the scope and focus of negation. In: Proceedings of the First Joint Conference on Lexical and Computational Semantics-Volume 1: Proceedings of the Main Conference and the Shared Task, and Volume 2: Proceedings of the 6th International Workshop on Semantic Evaluation, Montreal, Canada, 2012. 265-274

    [10] Abu-Jbara A, Radev D. UMichigan: A conditional random field model for resolving the scope of negation. In: Proceedings of the 1st Joint Conference on Lexical and Computational Semantics-Volume 1: Proceedings of the Main Conference and the Shared Task, and Volume 2: Proceedings of the 6th International Workshop on Semantic Evaluation, Montreal, Canada, 2012. 328-334

    [11] Chowdhury M, Mahbub F. FBK: Exploiting phrasal and contextual clues for negation scope detection. In: Proceedings of the First Joint Conference on Lexical and Computational Semantics-Volume 1: Proceedings of the Main Conference and the Shared Task, and Volume 2: Proceedings of the 6th International Workshop on Semantic Evaluation, Montreal, Canada, 2012. 340-346

    [12] White J P. UWashington: Negation resolution using machine learning methods. In: Proceedings of the First Joint Conference on Lexical and Computational Semantics-Volume 1: Proceedings of the Main Conference and the Shared Task, and Volume 2: Proceedings of the 6th International Workshop on Semantic Evaluation, Montreal, Canada, 2012. 335-339

    [13] Lafferty J, McCallum A, Pereira F C N. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proceedings of the 18th International Conference on Machine Learning (ICML),Massachusetts, USA, 2001. 282-289

    [14] Morante R, Schrauwen S, Daelemans W. Annotation of negation cues and their scope: Guidelines v1.Computationallinguisticsandpsycholinguisticstechnicalreportseries, CTRS-003, 2011

    [15] Read J, Velldal E, ?vrelid L, et al. Uio1: Constituent-based discriminative ranking for negation resolution. In: Proceedings of the First Joint Conference on Lexical and Computational Semantics-Volume 1: Proceedings of the Main Conference and the Shared Task, and Volume 2: Proceedings of the 6th International Workshop on Semantic Evaluation, Montreal, Canada, 2012. 310-318

    Lydia Lazib, born in 1990. She is a Ph.D candidate in Harbin Institute of Technology. She received her B.S. and M.S. degrees in Computer Science Department of Mouloud MAMMERI University of Tizi-Ouzou, Algeria in 2011 and 2013 respectively. Her research interests include sentiment analysis and negation detection.

    10.3772/j.issn.1006-6748.2017.02.011

    ①Supported by the National High Technology Research and Development Programme of China (No. 2015AA015407), the National Natural Science Foundation of China (No. 61273321), and the Specialized Research Fund for the Doctoral Program of Higher Education (No. 20122302110039).

    ②To whom correspondence should be addressed. E-mail: bing.qin@gmail.com

    on May 12, 2016

    亚洲婷婷狠狠爱综合网| 国产av在哪里看| 国产真实伦视频高清在线观看| 国产亚洲91精品色在线| av卡一久久| 国产精品伦人一区二区| 久久久久久久久久成人| 亚洲美女黄片视频| 日本免费a在线| 日韩欧美精品免费久久| 亚洲精品国产av成人精品 | 特大巨黑吊av在线直播| 蜜桃亚洲精品一区二区三区| 国产精品伦人一区二区| 如何舔出高潮| 少妇猛男粗大的猛烈进出视频 | 日本爱情动作片www.在线观看 | 特级一级黄色大片| 2021天堂中文幕一二区在线观| АⅤ资源中文在线天堂| 一本一本综合久久| 欧美国产日韩亚洲一区| 黄色日韩在线| 国产成人91sexporn| 成人午夜高清在线视频| 精品久久久久久成人av| 一个人看视频在线观看www免费| 日韩精品青青久久久久久| 波多野结衣高清作品| 中国美女看黄片| 中文字幕精品亚洲无线码一区| 国产精品伦人一区二区| 精品人妻一区二区三区麻豆 | 日韩,欧美,国产一区二区三区 | 久久久久国内视频| 欧美色视频一区免费| 能在线免费观看的黄片| 亚洲成人久久爱视频| 久久九九热精品免费| 简卡轻食公司| 国产精品一二三区在线看| 特大巨黑吊av在线直播| 老师上课跳d突然被开到最大视频| 日韩一区二区视频免费看| 免费一级毛片在线播放高清视频| 中文资源天堂在线| 成人亚洲欧美一区二区av| 亚洲成人久久性| 无遮挡黄片免费观看| 免费电影在线观看免费观看| 91久久精品国产一区二区三区| 亚洲欧美日韩卡通动漫| 免费观看的影片在线观看| 最近的中文字幕免费完整| 久久久久久久久久久丰满| 欧美高清成人免费视频www| 青春草视频在线免费观看| 中文字幕人妻熟人妻熟丝袜美| 床上黄色一级片| 噜噜噜噜噜久久久久久91| 亚洲性夜色夜夜综合| 波多野结衣高清作品| 三级国产精品欧美在线观看| av在线老鸭窝| 久久久久精品国产欧美久久久| 亚洲在线观看片| 日韩一区二区视频免费看| 日日摸夜夜添夜夜添av毛片| av福利片在线观看| 五月玫瑰六月丁香| 日本黄色片子视频| 免费av不卡在线播放| 国产蜜桃级精品一区二区三区| 欧美成人一区二区免费高清观看| 观看免费一级毛片| 日韩av不卡免费在线播放| 色综合站精品国产| 亚洲成人久久爱视频| 国产视频内射| 成年女人毛片免费观看观看9| 亚洲久久久久久中文字幕| АⅤ资源中文在线天堂| 变态另类丝袜制服| 欧美日韩乱码在线| 亚洲国产精品久久男人天堂| av在线蜜桃| 国产精品三级大全| 国产单亲对白刺激| 中文亚洲av片在线观看爽| 国产精品国产三级国产av玫瑰| 99久国产av精品国产电影| 啦啦啦观看免费观看视频高清| 我的老师免费观看完整版| 成年女人永久免费观看视频| 又爽又黄无遮挡网站| 久久久久国产网址| 亚洲av免费在线观看| 国产片特级美女逼逼视频| 国产av麻豆久久久久久久| 91午夜精品亚洲一区二区三区| 国产一区二区三区在线臀色熟女| 日本在线视频免费播放| 欧美一区二区精品小视频在线| 亚洲精品粉嫩美女一区| 久久99热6这里只有精品| 熟妇人妻久久中文字幕3abv| 日韩中字成人| 一本久久中文字幕| 成人一区二区视频在线观看| 亚洲自拍偷在线| 免费搜索国产男女视频| 国产老妇女一区| 国产爱豆传媒在线观看| 国产精品久久久久久精品电影| 久久久久久国产a免费观看| 日韩av在线大香蕉| 午夜精品在线福利| 麻豆乱淫一区二区| 色尼玛亚洲综合影院| 欧美激情国产日韩精品一区| 国产蜜桃级精品一区二区三区| 给我免费播放毛片高清在线观看| 免费一级毛片在线播放高清视频| avwww免费| 俺也久久电影网| 99精品在免费线老司机午夜| 亚洲国产精品sss在线观看| 毛片一级片免费看久久久久| 中文字幕久久专区| 欧美日韩精品成人综合77777| 一区二区三区免费毛片| 亚洲av中文字字幕乱码综合| 久久国产乱子免费精品| 综合色丁香网| 国产三级在线视频| 别揉我奶头 嗯啊视频| 久久久久久久久久久丰满| 欧美+日韩+精品| 又粗又爽又猛毛片免费看| 国产亚洲91精品色在线| 俺也久久电影网| 午夜精品国产一区二区电影 | 国产高清视频在线观看网站| 最后的刺客免费高清国语| 最新在线观看一区二区三区| 成人一区二区视频在线观看| 插逼视频在线观看| 日韩中字成人| 18禁在线播放成人免费| 日本欧美国产在线视频| 成人鲁丝片一二三区免费| a级毛片免费高清观看在线播放| 久久这里只有精品中国| 亚洲最大成人av| 久久久精品欧美日韩精品| 久久精品国产自在天天线| 最近视频中文字幕2019在线8| 精品一区二区三区视频在线| 亚洲国产精品国产精品| 啦啦啦韩国在线观看视频| 一级黄色大片毛片| 国产精品无大码| 激情 狠狠 欧美| av女优亚洲男人天堂| 国产精品永久免费网站| 国产精品乱码一区二三区的特点| 日日啪夜夜撸| 男女边吃奶边做爰视频| 一本一本综合久久| 尤物成人国产欧美一区二区三区| 天堂av国产一区二区熟女人妻| 国产精品一区二区三区四区免费观看 | 看片在线看免费视频| 亚洲一区二区三区色噜噜| 久久精品国产清高在天天线| 少妇猛男粗大的猛烈进出视频 | 美女cb高潮喷水在线观看| 日本在线视频免费播放| 日韩精品中文字幕看吧| 村上凉子中文字幕在线| 久久久精品欧美日韩精品| 久99久视频精品免费| 国产色婷婷99| av在线蜜桃| 精品久久久久久久久av| 天堂√8在线中文| 久久国产乱子免费精品| 舔av片在线| 国产麻豆成人av免费视频| 美女被艹到高潮喷水动态| 国产白丝娇喘喷水9色精品| av中文乱码字幕在线| 偷拍熟女少妇极品色| 国产亚洲精品综合一区在线观看| 婷婷精品国产亚洲av| 少妇被粗大猛烈的视频| videossex国产| 亚洲第一电影网av| 网址你懂的国产日韩在线| 久久人人精品亚洲av| 男女边吃奶边做爰视频| 成人无遮挡网站| 啦啦啦啦在线视频资源| 啦啦啦韩国在线观看视频| 国产老妇女一区| 九九久久精品国产亚洲av麻豆| 校园人妻丝袜中文字幕| 久久精品国产亚洲网站| 国内精品久久久久精免费| 小说图片视频综合网站| 日本色播在线视频| 国产亚洲91精品色在线| 久久午夜福利片| a级毛色黄片| 午夜精品一区二区三区免费看| 白带黄色成豆腐渣| 我的老师免费观看完整版| 九九久久精品国产亚洲av麻豆| 免费观看人在逋| 久久久久性生活片| 老女人水多毛片| 婷婷精品国产亚洲av| 免费看a级黄色片| 亚洲av成人av| 欧美在线一区亚洲| 人妻久久中文字幕网| 亚洲人成网站高清观看| 欧美色视频一区免费| 精品国内亚洲2022精品成人| 欧美一区二区精品小视频在线| 亚洲无线在线观看| 在线看三级毛片| 欧美日韩国产亚洲二区| 亚洲乱码一区二区免费版| 99九九线精品视频在线观看视频| 欧美色欧美亚洲另类二区| 搡老熟女国产l中国老女人| av在线观看视频网站免费| 在线观看美女被高潮喷水网站| 欧美丝袜亚洲另类| 波多野结衣巨乳人妻| 亚洲精品亚洲一区二区| 国产精品精品国产色婷婷| 啦啦啦啦在线视频资源| 成年版毛片免费区| 日韩制服骚丝袜av| 99在线视频只有这里精品首页| av专区在线播放| 国产精品无大码| 色综合站精品国产| 最近的中文字幕免费完整| 日韩在线高清观看一区二区三区| 美女大奶头视频| 欧美成人a在线观看| 精品乱码久久久久久99久播| 亚洲精品影视一区二区三区av| 欧美3d第一页| 日韩在线高清观看一区二区三区| 成人亚洲欧美一区二区av| 日韩国内少妇激情av| 久久精品国产自在天天线| 在线观看午夜福利视频| 午夜久久久久精精品| 麻豆久久精品国产亚洲av| 久久久久久大精品| 久久草成人影院| 欧美日韩综合久久久久久| 久久国产乱子免费精品| 精品一区二区三区视频在线观看免费| 91午夜精品亚洲一区二区三区| 国产亚洲精品久久久久久毛片| 亚洲av免费在线观看| 国产真实乱freesex| 亚洲中文日韩欧美视频| av国产免费在线观看| 97热精品久久久久久| 亚洲三级黄色毛片| 午夜影院日韩av| 少妇猛男粗大的猛烈进出视频 | 给我免费播放毛片高清在线观看| 天堂av国产一区二区熟女人妻| 亚洲自拍偷在线| 欧美xxxx黑人xx丫x性爽| 青春草视频在线免费观看| 蜜臀久久99精品久久宅男| 精品免费久久久久久久清纯| 极品教师在线视频| 九九在线视频观看精品| 亚洲精品粉嫩美女一区| 午夜激情欧美在线| 国产色爽女视频免费观看| 日本黄大片高清| 我要看日韩黄色一级片| 99热这里只有精品一区| 国产成人精品久久久久久| 91久久精品国产一区二区三区| 欧美成人精品欧美一级黄| 在线观看免费视频日本深夜| 男女边吃奶边做爰视频| 中出人妻视频一区二区| 插逼视频在线观看| a级毛片a级免费在线| 99热这里只有精品一区| 人妻夜夜爽99麻豆av| 成人漫画全彩无遮挡| 别揉我奶头 嗯啊视频| 一区二区三区高清视频在线| 欧美一区二区亚洲| 久久精品国产鲁丝片午夜精品| 日韩欧美 国产精品| 免费大片18禁| 中文在线观看免费www的网站| 嫩草影视91久久| 成人午夜高清在线视频| 精品久久久久久久久久免费视频| 免费看a级黄色片| 成人无遮挡网站| 看黄色毛片网站| 最新中文字幕久久久久| 麻豆成人午夜福利视频| 日本熟妇午夜| 亚洲国产欧洲综合997久久,| a级毛色黄片| 一个人看的www免费观看视频| 99在线人妻在线中文字幕| 成人永久免费在线观看视频| 亚洲av.av天堂| 热99re8久久精品国产| 网址你懂的国产日韩在线| 麻豆国产97在线/欧美| 欧美中文日本在线观看视频| 日本黄色视频三级网站网址| 亚洲最大成人av| 尤物成人国产欧美一区二区三区| 97碰自拍视频| 午夜福利在线观看吧| 精品久久久噜噜| 日本色播在线视频| 好男人在线观看高清免费视频| 久久精品国产亚洲av涩爱 | 综合色av麻豆| 精品久久久久久久久久久久久| 嫩草影视91久久| videossex国产| 亚洲国产日韩欧美精品在线观看| 国产蜜桃级精品一区二区三区| 非洲黑人性xxxx精品又粗又长| 人人妻人人澡人人爽人人夜夜 | 一个人观看的视频www高清免费观看| 成人综合一区亚洲| 一级a爱片免费观看的视频| 国产亚洲精品久久久com| 欧美绝顶高潮抽搐喷水| 97超视频在线观看视频| 亚洲一区高清亚洲精品| av.在线天堂| 日本a在线网址| 联通29元200g的流量卡| 少妇丰满av| 国产黄色视频一区二区在线观看 | 中出人妻视频一区二区| 1000部很黄的大片| 少妇猛男粗大的猛烈进出视频 | 亚洲av成人精品一区久久| 亚洲自拍偷在线| 好男人在线观看高清免费视频| 日韩精品中文字幕看吧| 国国产精品蜜臀av免费| 亚洲国产精品国产精品| 午夜影院日韩av| 日韩亚洲欧美综合| 国产毛片a区久久久久| 亚洲无线在线观看| 老师上课跳d突然被开到最大视频| 午夜福利18| 在线免费十八禁| 小说图片视频综合网站| or卡值多少钱| 久久人妻av系列| 97人妻精品一区二区三区麻豆| 午夜精品在线福利| 老熟妇乱子伦视频在线观看| 亚洲国产欧美人成| 国产aⅴ精品一区二区三区波| 午夜影院日韩av| 亚洲国产精品成人久久小说 | 大型黄色视频在线免费观看| 国产精品久久视频播放| 国产免费男女视频| 免费高清视频大片| 成年av动漫网址| 日产精品乱码卡一卡2卡三| 给我免费播放毛片高清在线观看| 看免费成人av毛片| 一级毛片电影观看 | 亚洲av美国av| 精品久久久久久成人av| 亚洲精品一区av在线观看| 日韩亚洲欧美综合| 国产精品1区2区在线观看.| 欧美一区二区亚洲| 欧美潮喷喷水| 国产精品一及| 免费看av在线观看网站| 91久久精品电影网| 午夜老司机福利剧场| 日韩精品中文字幕看吧| 淫秽高清视频在线观看| 国产色婷婷99| 国产精品久久久久久精品电影| 美女高潮的动态| 悠悠久久av| 国产精品久久视频播放| 内射极品少妇av片p| 97在线视频观看| 日本一本二区三区精品| 成人国产麻豆网| 麻豆av噜噜一区二区三区| 12—13女人毛片做爰片一| 中国美女看黄片| 国内精品一区二区在线观看| 我要看日韩黄色一级片| 免费人成在线观看视频色| 国产成人91sexporn| 精品午夜福利视频在线观看一区| 日韩av不卡免费在线播放| 亚洲最大成人手机在线| 精品福利观看| 一个人免费在线观看电影| 真人做人爱边吃奶动态| 在线免费观看的www视频| 国产精品久久电影中文字幕| 亚洲成人精品中文字幕电影| 国产麻豆成人av免费视频| 国产一级毛片七仙女欲春2| 精品久久久久久久末码| 黄色配什么色好看| 99国产精品一区二区蜜桃av| 国产探花极品一区二区| 国产一区二区三区在线臀色熟女| 日本三级黄在线观看| 婷婷精品国产亚洲av在线| 亚洲国产色片| 精品熟女少妇av免费看| 亚洲五月天丁香| 亚洲欧美日韩东京热| 亚洲性夜色夜夜综合| 国产激情偷乱视频一区二区| 精品乱码久久久久久99久播| 亚洲国产精品国产精品| 看非洲黑人一级黄片| 欧美3d第一页| av中文乱码字幕在线| 最近2019中文字幕mv第一页| 亚洲中文字幕一区二区三区有码在线看| 精品久久久久久久末码| 色综合色国产| а√天堂www在线а√下载| 精品久久国产蜜桃| 精品久久久久久久久久免费视频| 99热网站在线观看| 欧美高清性xxxxhd video| 婷婷色综合大香蕉| 国产单亲对白刺激| 亚洲乱码一区二区免费版| 少妇熟女欧美另类| 亚洲,欧美,日韩| 97在线视频观看| 99精品在免费线老司机午夜| 99热只有精品国产| 国产大屁股一区二区在线视频| 国产蜜桃级精品一区二区三区| 校园春色视频在线观看| 五月伊人婷婷丁香| 极品教师在线视频| 亚洲精品日韩av片在线观看| 日本黄色片子视频| 自拍偷自拍亚洲精品老妇| aaaaa片日本免费| 此物有八面人人有两片| 日韩 亚洲 欧美在线| 欧美一区二区精品小视频在线| 好男人在线观看高清免费视频| 精品熟女少妇av免费看| 春色校园在线视频观看| 国产精品亚洲一级av第二区| 一边摸一边抽搐一进一小说| .国产精品久久| 中国美女看黄片| 一级黄色大片毛片| 欧美xxxx黑人xx丫x性爽| 一个人看的www免费观看视频| 亚洲精品影视一区二区三区av| 伦理电影大哥的女人| 欧美zozozo另类| 亚洲最大成人中文| 最近中文字幕高清免费大全6| 国产精品乱码一区二三区的特点| 观看美女的网站| 久久精品国产亚洲av涩爱 | 好男人在线观看高清免费视频| 国产精品福利在线免费观看| 久久久久久久久久久丰满| 成年女人永久免费观看视频| 亚洲av五月六月丁香网| 熟女人妻精品中文字幕| 午夜a级毛片| 一级毛片aaaaaa免费看小| 91在线精品国自产拍蜜月| 国产极品精品免费视频能看的| 一级黄片播放器| 久久99热这里只有精品18| 天堂av国产一区二区熟女人妻| 欧美激情在线99| 悠悠久久av| 精品午夜福利视频在线观看一区| 亚洲av成人av| 黄色配什么色好看| 国产真实乱freesex| 精品久久久久久久久亚洲| 啦啦啦观看免费观看视频高清| 男女之事视频高清在线观看| 亚洲国产高清在线一区二区三| 夜夜夜夜夜久久久久| 我的女老师完整版在线观看| 国产午夜精品论理片| 春色校园在线视频观看| 日韩欧美精品v在线| 久久久久久久亚洲中文字幕| 男女之事视频高清在线观看| 成人特级黄色片久久久久久久| 99热全是精品| av免费在线看不卡| 一级毛片aaaaaa免费看小| 一级毛片电影观看 | 久久九九热精品免费| 悠悠久久av| 如何舔出高潮| 国产一区二区在线av高清观看| 伦理电影大哥的女人| 国产成人aa在线观看| 草草在线视频免费看| 人人妻人人看人人澡| 亚洲婷婷狠狠爱综合网| 99国产极品粉嫩在线观看| 天堂影院成人在线观看| 俄罗斯特黄特色一大片| 精品一区二区三区视频在线观看免费| 国产精品伦人一区二区| 少妇的逼好多水| 熟妇人妻久久中文字幕3abv| 欧美色视频一区免费| 男人舔女人下体高潮全视频| 赤兔流量卡办理| 国模一区二区三区四区视频| av在线观看视频网站免费| av在线老鸭窝| 日日干狠狠操夜夜爽| 欧美一区二区精品小视频在线| 国产精品久久电影中文字幕| a级毛片a级免费在线| 插逼视频在线观看| 免费看日本二区| 变态另类丝袜制服| 亚洲精品一卡2卡三卡4卡5卡| 少妇人妻精品综合一区二区 | 九九热线精品视视频播放| 欧美日韩综合久久久久久| 成人鲁丝片一二三区免费| 长腿黑丝高跟| 成年版毛片免费区| 久久精品国产亚洲网站| 深夜精品福利| 99久久九九国产精品国产免费| 最近的中文字幕免费完整| 久久精品国产清高在天天线| 国产免费男女视频| 久99久视频精品免费| 亚洲综合色惰| 久久久欧美国产精品| 亚洲欧美日韩卡通动漫| 国产亚洲精品久久久久久毛片| 国产人妻一区二区三区在| 男人舔奶头视频| 性欧美人与动物交配| 婷婷六月久久综合丁香| 国内精品久久久久精免费| 激情 狠狠 欧美| 国产精品一区二区三区四区免费观看 | 91精品国产九色| 亚洲熟妇中文字幕五十中出| 网址你懂的国产日韩在线| 久久99热6这里只有精品| 久久人人爽人人爽人人片va| h日本视频在线播放| 久久99热6这里只有精品| 国产亚洲91精品色在线| 波野结衣二区三区在线| 精品一区二区三区视频在线| 色在线成人网| 午夜久久久久精精品| 成年女人永久免费观看视频| 国产免费男女视频| 久久久久国内视频| 中文字幕av成人在线电影| 国产v大片淫在线免费观看| 免费av毛片视频| 亚洲丝袜综合中文字幕| 日韩成人av中文字幕在线观看 | 一区二区三区免费毛片| 1000部很黄的大片| 麻豆av噜噜一区二区三区| 久久99热这里只有精品18| 欧美最黄视频在线播放免费| 亚洲精品久久国产高清桃花|