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1 NAIST-IS-MR Abstract meaning representation parsing AMR

2 ( )

3 Abstract meaning representation parsing AMR Abstract meaning representation(amr) named entity AMR parsing AMR subgraph AMR parsing 16., NAIST-IS-MR , i

4 A consideration about Alignment of word and AMR subgraph for parsing of Abstract meaning representation Yoichi Enomoto Abstract AMR is meaning representation of sentence using directed acyclic graph It include information about named-entity predicate-argument structure coreference etc and represent meaning of sentence by using them This mastersreport investigate alignment of words and subgraph of AMR to improve parsing of AMR Keywords: AMR meaning representation parsing alignnment Master s Report, Graduate School of Information Science, Nara Institute of Science and Technology, NAIST-IS-MR , March 16, ii

5 python iii

6 iii AMR Abstract meaning representation(amr) AMR AMR aligner AMR AMR be iv

7 Wh yes-no v

8 [3] The boy wants to visit New York City AMR The boy wants to visit New York City phrase structure tree The boy desires the girl to believe him The boy s desire is for the girl to believe him The boy is desirous of the girl believing him AMR AMR subgraph AMR subgraph AMR subgraph IBM alignment model find your American illegitimate fathers... AMR wikitionary illegitimate letigimate Take a look AMR Hurry up! AMR Hurry up! AMR [1] Hurry smoke up everywhere... AMR U.S. military satellite could be vulnerable to attack... AMR The note stated that this is going to happen to all the people who work with AMR Hey ZF! AMR Take a look AMR is aware of... AMR statement that this was so as to clear the way AMR 21 vi

9 22 Hey!ZF! AMR Take a look at North Korea,... AMR What are your thoughts on capitalism and its activities? AMR Have they not been to Mexico? AMR D ont worry though AMR Oh yours? AMR [1] IBM model4 [4] role unrole vii

10 propbank[12] Abstract meaning representation(amr)[1] [1] 1.2 AMR 1 1 Google Anna s catismissingher [3] 2 1

11 AMR AMR 1 [5] 2 [3] 2

12 2. Abstract meaning representation(amr) 2.1 AMR AMR[1] 3 AMR named entity temporal entity [1] 3 The boy wants to visit New York City AMR city name New York City named entity 3 The boy wants to visit New York City AMR 3

13 2.2 AMR AMR 1 want-01 want-02 AMR propbank 2 AMR 3 the to AMR 3 AMR 4 The boy wants to visit New York City phrase structure tree tree AMR 3 boy The boy AMR boy The boy 4 The boy wants to visit New York City phrase structure tree 4

14 4 AMR 3 AMR 5 The boy desires the girl to believe him The boy s desireisforthegirltobelievehim The boy is desirous of the girl believing him 5 The boy desires the girl to believe him The boy s desireisforthe girl to believe him The boy is desirous of the girl believing him AMR 5 AMR wants want 6 AMR creation AMR create governor govern governor 5

15 governor govern [6] creation create AMR 3 AMR parsing AMR subgraph subgraph AMR AMR 4 6

16 [1] AMR parsing parsing 1) 6 AMR subgraph 14 1 wordnet AMR 4 AMR AMR name ops 3 not no non - 4 est degree 5 day month year date-entity 6 il un in - AMR sub graph 7

17 6 AMR subgraph 2) subgraph semi-markov model[7] 1 subgraph 2 named entity tagger[8] 7 AMR subgraph 3) subgraph AMR AMR dependency parser[13] dependency tree 8

18 AMR 2) subgraph concept identification subgraph 3) relation identification [1] AMR parser [9][10] 4 [1] [4] 3.2 [4] 1) AMR The boy wants to go AMR 8 AMR root AMR (w/want-01) (arg0) (b/boy) (arg1) (g/go-01) (arg0) (b) arg0 opt1 temporal-quantity date-entity 8 AMR want boy go the to AMR The boy wants to go boy want to go 9

19 8 AMR subgraph 2) IBM alignment model[11] 2 9 IBM alignment model 2 AMR

20 3.3 this- this this AMR this A G A=[this- this, apple- lemon, lemon - apple, delicious - delicious, I- I ] G=[this- this, apple- apple, lemon - lemon, delicious - delicious, I- I, think- think-01 ] presicion recall fscore precision = G A = 3 A 5 recall = G A G = 3 6 Fscore = 2 precision recall precision + recall = IMB 4 1 [1] precision recall Fscore test

21 2 IBM model4 [4] precision recall Fscore test precision AMR subgraph 2 AMR 3 2 role unrole token type precision recall Fscore role non-role all role non-role Gold polality unpopular arg0 arg1 argment AMR 12

22 4. aligner AMR subgraph [1] 1 AMR 2 3 AMR AMR 10 AMR...find your American illegitimate fathers... illegistimate AMR legitimate - [1] illegitimate legitimate illegitimate legitimate - illegitimate legitimate wikitionary 11 wikitionary illegitimate legitimate 13

23 10... find your American illegitimate fathers... AMR 11 wikitionary illegitimate letigimate 14

24 AMR take a look take subgraph 12 Take a look AMR 13 AMR 13 up AMR 14 hurry-01 hurry up 2 [1] named entity Hurry up! up up 15 AMR [1] Hurry up! parse subgraph hurry-01 up 15

25 13...Hurry up! AMR 14...Hurry up! AMR [1] Hurry 15...smoke up everywhere... AMR 16

26 4.3 AMR 16 possible-01 could [6] 17 is going to happen to event beneficially beneficially 17 event person is going to happen to U.S. military satellite could be vulnerable to attack... AMR 17

27 17 The note stated that this is going to happen to all the people who work with AMR I Hey ZF! say-01 AMR say-01 say-01 say-01 AMR 18 Hey ZF! AMR 18

28 4.5 AMR [6] AMR 12 AMR 4 3 [6] modal be + 19

29 5. [1] 5.1 take a look take AMR [6] take 19 Take a look AMR 5.2 be + be AMR 20 is aware of realize-01 [6] 20 AMR be aware of X X realize-01 argment 20

30 20...is aware of... AMR 5.3 so as to AMR 21 so as to have-purpose statement that this was so as to clear the way AMR 21

31 5.4 AMR 5.5 yes-no 5.6 oh 27 expressive 5.7 imperative 22

32 say Hey!ZF! AMR 5.9 AMR 23 you take a look you 23 Take a look at North Korea,... AMR 23

33 6. [1] 4 1 AMR 2 AMR be AMR 3 AMR AMR [6] AMR 24

34 4 23 Take a look at North Korea, that s our country many years ago. Take a look at their ridiculous propaganda. you North Korea take a look AMR 25

35 7. [1] [6] 4 4 Concept identification AMR parsing 26

36 8. [1]Banarescu, L., Bonial, C., Cai, S., Georgescu, M.,Griffitt, K., Hermjakob, U., Knight, K., Koehn,P., Palmer, M., and Schneider, N. (2013). Abstractmeaningrepresentation for sembanking.in Proceedings of the Linguistic Annotation- Workshop. [2] Flanigan, J., Thomson, S., Carbonell, J., Dyer, C., and Smith, N. A. (2014). A discriminative graph-based parser for the Abstract Meaning Representation. In Proceedings of the Conference of the Association of Computational Linguistics. [3]Jones, B., Andreas, J., Bauer, D., Hermann, K. M., and Knight, K. (2012). Semantics-based machine translation with hyperedge replacement grammars. In Proceedings of the International Conference on Computational Linguistics. [4]Pourdamghani, N., Gao, Y., Hermjakob, U., Knight, K. (2014). Aligning English Strings with Abstract Meaning Representation Graphs. In EMNLP (pp ). [5]Arindam Mitra and Chitta Baral Addressing a Question Answering Challenge by Combining Statistical Methods with Inductive Rule Learning and Reasoning. In Proc. of AAAI [6]Abstract Meaning Representation (AMR) Specification, [7]Janssen, J., and Limnios, N. (Eds.). (2013). Semi-Markov models and applications. Springer Science and Business Media. [8] Ratinov, L., and Roth, D. (2009, June). Design challenges and misconceptions in named entity recognition. In Proceedings of the Thirteenth Conference on Computational Natural Language Learning (pp ). Association for Computational Linguistics. [9]Wang, C., Xue, N., Pradhan, S., and Pradhan, S. (2015). A Transition-based Algorithm for AMR Parsing. In HLT-NAACL (pp ). [10]Artzi, Y., Lee, K., and Zettlemoyer, L. (2015, September). Broad-coverage CCG Semantic Parsing with AMR. In EMNLP (pp ). 27

37 [11]Brown, P. F., Pietra, V. J. D., Pietra, S. A. D., and Mercer, R. L. (1993). The mathematics of statistical machine translation: Parameter estimation. Computational linguistics, 19(2), [12]Kingsbury, P., and Palmer, M. (2002, May). From TreeBank to PropBank. In LREC (pp ). [13]McDonald, R., Pereira, F., Ribarov, K., and Hajic, J. (2005, October). Nonprojective dependency parsing using spanning tree algorithms. In Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing (pp ). Association for Computational Linguistics. 28

38 AMR 5 AMR can could possible-01 possible-01 permit-01 may possible-01 permit-01 might must shall should possible-01 obligate-01 possible-01 recommend yes-no Wh- What are your thoughts on capitalism and its activities? yes-no amr-unknown 24 29

39 24 What are your thoughts on capitalism and its activities? AMR yes-no yes-no interrogative Have they not been to Mexico? AMR 30

40 9.3 imperative 26 D ont worry though AMR 9.4 oh 27 expressive 27 Oh yours? AMR 31

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