Learning Chinese language structures with multiple views

Motivated by the inadequacy of single view approaches in many areas in NLP, we study multi-view Chinese language processing, including word segmentation, part-of-speech (POS) tagging, syntactic parsing and semantic role labeling (SRL), in this thesis. We consider three situations of multiple views in statistical NLP: (1) Heterogeneous computational models have been designed for a given problem; (2) Heterogeneous annotation data is available to train systems; (3) Supervised and unsupervised machine learning techniques are applicable. First, we comparatively analyze successful single view approaches for Chinese lexical, syntactic and semantic processing. Our analysis highlights the diversity between heterogenous systems built on different views, and motivates us to improve the state-of-the-art by combining or integrating heterogeneous approaches. Second, we study the annotation ensemble problem, i.e. learning from multiple data sets under different annotation standards. We propose a series of generalized stacking models to effectively utilize heterogeneous labeled data to reduce approximation errors for word segmentation and parsing. Finally, we are concerned with bridging the gap between unsupervised and supervised learning paradigms. We introduce feature induction solutions that harvest useful linguistic knowledge from large-scale unlabeled data and effectively use them as new features to enhance discriminative learning based systems. For word segmentation, we present a comparative study of word-based and character-based approaches. Inspired by the diversity of the two views, we design a novel stacked sub-word tagging model for joint word segmentation and POS tagging, which is robust to integrate different models, even models trained on heterogeneous annotations. To benefit from unsupervised word segmentation, we derive expressive string knowledge from unlabeled data which significantly enhances a strong supervised segmenter. For POS tagging, we introduce two linguistically motivated improvements: (1) combining syntax-free sequential tagging and syntax-based chart parsing results to better capture syntagmatic lexical relations and (2) integrating word clusters acquired from unlabeled data to better capture paradigmatic lexical relations. For syntactic parsing, we present a comparative analysis for generative PCFG-LA constituency parsing and discriminative graph-based dependency parsing. To benefit from the diversity of parsing in different formalisms, we implement a previously introduced stacking method and propose a novel Bagging model to combine complementary strengths of grammar-free and grammar-based models. In addition to the study on the syntactic formalism, we also propose a reranking model to explore heterogenous treebanks that are labeled under different annotation scheme. Finally, we continue our efforts on combining strengths of supervised and unsupervised learning, and evaluate the impact of word clustering on different syntactic processing tasks. Our work on SRL focus on improving the full parsing method with linguistically rich features and a chunking strategy. Furthermore, we developed a partial parsing based semantic chunking method, which has complementary strengths to the full parsing based method. Based on our work, Zhuang and Zong (2010) successfully improve the state-of-the-art by combining full and partial parsing based SRL systems.Motiviert durch die Unzulänglichkeit der Ansätze mit dem einzigen Ansicht in vielen Bereichen in NLP, untersuchen wir Chinesische Sprache Verarbeitung mit mehrfachen Ansichten, einschließlich Wortsegmentierung, Part-of-Speech (POS)-Tagging und syntaktische Parsing und die Kennzeichnung der semantische Rolle (SRL) in dieser Arbeit . Wir betrachten drei Situationen von mehreren Ansichten in der statistischen NLP: (1) Heterogene computergestützte Modelle sind für ein gegebenes Problem entwurft, (2) Heterogene Annotationsdaten sind verfügbar, um die Systeme zu trainieren, (3) überwachten und unüberwachten Methoden des maschinellen Lernens sind zur Verfügung gestellt. Erstens, wir analysieren vergleichsweise erfolgreiche Ansätze mit einzigen Ansicht für chinesische lexikalische, syntaktische und semantische Verarbeitung. Unsere Analyse zeigt die Unterschiede zwischen den heterogenen Systemen, die auf verschiedenen Ansichten gebaut werden, und motiviert uns, die state-of-the-Art durch die Kombination oder Integration heterogener Ansätze zu verbessern. Zweitens, untersuchen wir die Annotation Ensemble Problem, d.h. das Lernen aus mehreren Datensätzen unter verschiedenen Annotation Standards. Wir schlagen eine Reihe allgemeiner Stapeln Modelle, um eine effektive Nutzung heterogener Daten zu beschriften, und um Approximationsfehler für Wort Segmentierung und Analyse zu reduzieren. Schließlich sind wir besorgt mit der Überbrückung der Kluft zwischen unüberwachten und überwachten Lernens Paradigmen. Wir führen Induktion Feature-Lösungen, die nützliche Sprachkenntnisse von großflächigen unmarkierter Daten ernte, und die effektiv nutzen als neue Features, um die unterscheidenden Lernen basierten Systemen zu verbessern. Für die Wortsegmentierung, präsentieren wir eine vergleichende Studie der Wort-basierte und Charakter-basierten Ansätzen. Inspiriert von der Vielfalt der beiden Ansichten, entwerfen wir eine neuartige gestapelt Sub-Wort-Tagging-Modell für gemeinsame Wort-Segmentierung und POS-Tagging, die robust ist, um verschiedene Modelle zu integrieren, auch Modelle auf heterogenen Annotationen geschult. Um den unbeaufsichtigten Wortsegmentierung zu profitieren, leiten wir ausdrucksstarke Zeichenfolge Wissen von unmarkierten Daten. Diese Methode hat eine überwachte Methode erheblich verbessert. Für POS-Tagging, führen wir zwei linguistisch motiviert Verbesserungen: (1) die Kombination von Syntaxfreie sequentielle Tagging und Syntaxbasierten Grafik-Parsing-Ergebnisse, um syntagmatische lexikalische Beziehungen besser zu erfassen (2) die Integration von Wortclusteren von nicht markierte Daten, um die paradigmatische lexikalische Beziehungen besser zu erfassen. Für syntaktische Parsing präsentieren wir eine vergleichenbare Analyse für generative PCFG-LA Wahlkreis Parsing und diskriminierende Graphen-basierte Abhängigkeit Parsing. Um aus der Vielfalt der Parsen in unterschiedlichen Formalismen zu profitieren, setzen wir eine zuvor eingeführte Stacking-Methode und schlagen eine neuartige Schrumpfbeutel-Modell vor, um die ergänzenden Stärken der Grammatik und Grammatik-free-basierte Modelle zu kombinieren. Neben dem syntaktischen Formalismus, wir schlagen auch ein Modell, um heterogene reranking Baumbanken, die unter verschiedenen Annotationsschema beschriftet sind zu erkunden. Schließlich setzen wir unsere Bemühungen auf die Bündelung von Stärken des überwachten und unüberwachten Lernen, und bewerten wir die Auswirkungen der Wort-Clustering auf verschiedene syntaktische Verarbeitung Aufgaben. Unsere Arbeit an SRL ist konzentriert auf die Verbesserung der vollen Parsingsmethode mit linguistischen umfangreichen Funktionen und einer Chunkingstrategie. Weiterhin entwickelten wir eine semantische Chunkingmethode basiert auf dem partiellen Parsing, die die komplementäre Stärken gegen die die Methode basiert auf dem vollen Parsing hat. Basiert auf unserer Arbeit, Zhuang und Zong (2010) hat den aktuelle Stand erfolgreich verbessert durch die Kombination von voll-und partielle-Parsing basierte SRL Systeme

Classifier combination approach for question classification for Bengali question answering system

[EN] Question classification (QC) is a prime constituent of an automated question answering system. The work presented here demonstrates that a combination of multiple models achieves better classification performance than those obtained with existing individual models for the QC task in Bengali. We have exploited state-of-the-art multiple model combination techniques, i.e., ensemble, stacking and voting, to increase QC accuracy. Lexical, syntactic and semantic features of Bengali questions are used for four well-known classifiers, namely Naive Bayes, kernel Naive Bayes, Rule Induction and Decision Tree, which serve as our base learners. Single-layer question-class taxonomy with 8 coarse-grained classes is extended to two-layer taxonomy by adding 69 fine-grained classes. We carried out the experiments both on single-layer and two-layer taxonomies. Experimental results confirmed that classifier combination approaches outperform single-classifier classification approaches by 4.02% for coarse-grained question classes. Overall, the stacking approach produces the best results for fine-grained classification and achieves 87.79% of accuracy. The approach presented here could be used in other Indo-Aryan or Indic languages to develop a question answering system.Somnath Banerjee and Sudip Kumar Naskar are supported by Digital India Corporation (formerly Media Lab Asia), MeitY, Government of India, under the Visvesvaraya Ph.D. Scheme for Electronics and IT. The work of Paolo Rosso was partially funded by the Spanish MICINN under the research project PGC2018-096212-B-C31.Banerjee, S.; Kumar Naskar, S.; Rosso, P.; Bndyopadhyay, S. (2019). Classifier combination approach for question classification for Bengali question answering system. Sadhana. 44(12):1-14. https://doi.org/10.1007/s12046-019-1224-81144412Jurafsky D and Martin J H 2014 Speech and language processing. Pearson, LondonMartin J H and Jurafsky D 2000 Speech and language processing, international edition 710Voorhees E M 2002 Overview of the TREC 2001 question answering track. NIST Special Publication, pp. 42–51Hovy E, Gerber L, Hermjakob U, Lin C Y and Ravichandran D 2001 Toward semantics-based answer pinpointing. In: Proceedings of Human Language Technology Research, ACL, pp. 1–7Ittycheriah A, Franz M, Zhu W J, Ratnaparkhi A and Mammone R J 2000 IBM’s statistical question answering system. In: Proceedings of TRECMoldovan D, Paşca M, Harabagiu S and Surdeanu M 2003 Performance issues and error analysis in an open-domain question answering system. ACM Trans. Inf. Syst. 21(2): 133–154Banerjee S and Bandyopadhyay S 2012 Bengali question classification: towards developing QA system. In: Proceedings of the 3rd Workshop on South and Sotheast Asian Language Processing (SANLP), COLING, pp. 25–40Loni B 2011 A survey of state-of-the-art methods on question classification. Technical Report, Delft University of TechnologyHull D A 1999 Xerox TREC-8 question answering track report. In: Proceedings of TRECPrager J, Radev D, Brown E, Coden A and Samn V 1999 The use of predictive annotation for question answering in TREC8. Inf. Retr. 1(3): 4Moschitti A, Quarteroni S, Basili R and Manandhar S 2007 Exploiting syntactic and shallow semantic kernels for question answer classification. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics, p. 776Zhang D and Lee W S 2003 Question classification using support vector machines. In: Proceedings of Research and Development in Informaion Retrieval, ACM, pp. 26–32Huang Z, Thint M and Qin Z 2008 Question classification using head words and their hypernyms. In: Proceedings of Empirical Methods in Natural Language Processing, ACL, pp. 927–936Silva J, Coheur L, Mendes A C and Wichert A 2011 From symbolic to sub-symbolic information in question classification. Artif. Intell. Rev. 35(2): 137–154Li X and Roth D 2006 Learning question classifiers: the role of semantic information. Nat. Lang. Eng. 12(03): 229–249McCallum A, Freitag D and Pereira F C N 2000 Maximum entropy markov models for information extraction and segmentation. In: Proceedings of the International Conference on Machine Learning (ICML), vol. 17, pp. 591–598Cortes C and Vapnik V 1995 Support-vector networks. Mach. Learn. 20(3): 273–297Breiman L 1996 Bagging predictors. Mach. Learn. 24(2): 123–140Clemen R T 1989 Combining forecasts: a review and annotated bibliography. Int. J. Forecast. 5(4): 559–583Perrone M P 1993 Improving regression estimation: averaging methods for variance reduction with extensions to general convex measure optimization. Ph.D. Thesis, Brown UniversityWolpert D H 1992 Stacked generalization. Neural Netw. 5(2): 241–259Hansen L K and Salamon P 1990 Neural network ensembles. IEEE Trans. Pattern Anal. Mach. Intell. 12: 993–1001Krogh A, Vedelsby J et al 1995 Neural network ensembles, cross validation, and active learning. Adv. Neural Inf. Process. Syst. 7: 231–238Hashem S 1997 Optimal linear combinations of neural networks. Neural Netw. 10(4): 599–614Opitz D W and Shavlik J W 1996 Actively searching for an effective neural network ensemble. Connect. Sci. 8(3–4): 337–354Opitz D W and Shavlik J W 1996 Generating accurate and diverse members of a neural-network ensemble. In: Advances in neural information processing systems, pp. 535–541Xin L, Huang X J and Wu L 2006 Question classification by ensemble learning. Int. J. Comput. Sci. Netw. Secur. 6(3): 147Schapire R E 1990 The strength of weak learnability. Mach. Learn. 5(2): 197–227Brill E 1995 Transformation-based error-driven learning and natural language processing: a case study in part-of-speech tagging. Comput. Linguist. 21(4): 543–565Jia K, Chen K, Fan X and Zhang Y 2007 Chinese question classification based on ensemble learning. In: Proceedings of ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, SNPD 2007. IEEE, vol. 3, pp. 342–347Su L, Liao H, Yu Z and Zhao Q 2009 Ensemble learning for question classification. In: Proceedings of Intelligent Computing and Intelligent Systems, ICIS. IEEE, pp. 501–505Ferrucci D, Brown E, Chu-Carroll J, Fan J et al 2010 Building Watson: an overview of the DeepQA project. AI Mag. 31(3): 59–79Pérez-Coutiño M A, Montes-y-Gómez M, López-López A and Villaseñor-Pineda L 2005 Experiments for tuning the values of lexical features in question answering for Spanish. In: CLEF Working NotesNeumann G and Sacaleanu B 2003 A cross-language question/answering system for German and English. In: Proceedings of the Workshop of the Cross-Language Evaluation Forum for European Languages, pp. 559–571Blunsom P, Kocik K and Curran J R 2006 Question classification with log-linear models. In: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval. ACM, pp. 615–616Rosso P, Benajiba Y and Lyhyaoui A 2006 In: Proceedings of the 4th Conference on Scientific Research Outlook and Technology Development in the Arab World, pp. 11–14Abouenour L, Bouzoubaa K and Rosso P 2012 IDRAAQ: new Arabic question answering system based on query expansion and passage retrieval. In: Proceedings of CELCTSakai T, Saito Y, Ichimura Y, Koyama M, Kokubu T and Manabe T 2004 ASKMi: a Japanese question answering system based on semantic role analysis. In: Proceedings of Coupling Approaches, Coupling Media and Coupling Languages for Information Retrieval, pp. 215–231Isozaki H, Sudoh K and Tsukada H 2005 NTT’s Japanese–English cross-language question answering system. In: Proceedings of NTCIRYongkui Z, Zheqian Z, Lijun B and Xinqing C 2003 Internet-based Chinese question-answering system. Comput. Eng. 15: 34Sun A, Jiang M, He Y, Chen L and Yuan B 2008 Chinese question answering based on syntax analysis and answer classification. Acta Electron. Sin. 36(5): 833–839Sahu S, Vasnik N and Roy D 2012 Prashnottar: a Hindi question answering system. Int. J. Comput. Sci. Inf. Technol. 4(2): 149Nanda G, Dua M and Singla K 2016 A Hindi question answering system using machine learning approach. In: Proceedings of Computational Techniques in Information and Communication Technologies (ICCTICT). IEEE, pp. 311–314Sekine S and Grishman R 2003 Hindi–English cross-lingual question-answering system. ACM Trans. Asian Lang. Inf. Process. 2(3): 181–192Shukla P, Mukherjee A and Raina A 2004 Towards a language independent encoding of documents. In: Proceedings of NLUCS 2004, p. 116Ray S K, Ahmad A and Shaalan K 2018 A review of the state of the art in Hindi question answering systems. In: Proceedings of Intelligent Natural Language Processing: Trends and Applications, pp. 265–292Kumar P, Kashyap S, Mittal A and Gupta S 2003 A query answering system for e-learning Hindi documents. South Asian Lang. Rev. 13(1–2): 69–81Reddy R, Reddy N and Bandyopadhyay S 2006 Dialogue based question answering system in Telugu. In: Proceedings of the Workshop on Multilingual Question Answering, pp. 53–60Dhanjal G S, Sharma S and Sarao P K 2016 Gravity based Punjabi question answering system. Int. J. Comput. Appl. 147(3): 30–35Bindu M S and Mary I S 2012 Design and development of a named entity based question answering system for Malayalam language. Ph.D. Thesis, Cochin University of Science and TechnologyLee C W et al 2005 ASQA: academia sinica question answering system for NTCIR-5 CLQA. In: Proceedings of the NTCIR-5 Workshop, pp. 202–208Banerjee S and Bandyopadhyay S 2013 Ensemble approach for fine-grained question classification in Bengali. In: Proceedings of the 27th Pacific–Asia Conference on Language, Information, and Computation (PACLIC-27), pp. 75–84Loni B, Van Tulder G, Wiggers P, Tax D M J and Loog M 2011 Question classification by weighted combination of lexical, syntactic and semantic features. In: Proceedings of the International Conference on Text, Speech, and Dialogue, pp. 243–250Huang Z, Thint M and Celikyilmaz A 2009 Investigation of question classifier in question answering. In: Proceedings of Empirical Methods in Natural Language Processing. ACL, vol. 2, pp. 543–550Blunsom P, Kocik K and Curran J R 2006 Question classification with log-linear models. In: Proceedings of ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, pp. 615–616Diwakar S, Goyal P and Gupta R 2010 Transliteration among indian languages using WX notation. In: Proceedings of the Conference on Natural Language Processing, EPFL-CONF-168805. Saarland University Press, pp. 147–150Banerjee S, Naskar S K and Bandyopadhyay S Bengali named entity recognition using margin infused relaxed algorithm. In: Proceedings of the International Conference on Text, Speech, and Dialogue, pp. 125–132Li X and Roth D Learning question classifiers. In: Proceedings of the 19th International Conference on Computational Linguistics, ACL, vol. 1, pp. 1–7Cohen J 1960 A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 20(1): 37–46Schapire R E 1990 The strength of weak learnability. Mach. Learn. 5(2): 197–22

Challenges in discriminating profanity from hate speech

In this study, we approach the problem of distinguishing general profanity from hate speech in social media, something which has not been widely considered. Using a new dataset annotated specifically for this task, we employ supervised classification along with a set of features that includes -grams, skip-grams and clustering-based word representations. We apply approaches based on single classifiers as well as more advanced ensemble classifiers and stacked generalisation, achieving the best result of accuracy for this 3-class classification task. Analysis of the results reveals that discriminating hate speech and profanity is not a simple task, which may require features that capture a deeper understanding of the text not always possible with surface -grams. The variability of gold labels in the annotated data, due to differences in the subjective adjudications of the annotators, is also an issue. Other directions for future work are discussed

Ensemble methods for instance-based Arabic language authorship attribution

The Authorship Attribution (AA) is considered as a subfield of authorship analysis and it is an important problem as the range of anonymous information increased with fast growing of internet usage worldwide. In other languages such as English, Spanish and Chinese, such issue is quite well studied. However, in Arabic language, the AA problem has received less attention from the research community due to complexity and nature of Arabic sentences. The paper presented an intensive review on previous studies for Arabic language. Based on that, this study has employed the Technique for Order Preferences by Similarity to Ideal Solution (TOPSIS) method to choose the base classifier of the ensemble methods. In terms of attribution features, hundreds of stylometric features and distinct words using several tools have been extracted. Then, Adaboost and Bagging ensemble methods have been applied on Arabic enquires (Fatwa) dataset. The findings showed an improvement of the effectiveness of the authorship attribution task in the Arabic language