We explore various machine learning-based classifiers applied to rule-based features for recognizing textual entailment. The features, extracted with a set of synthesized matching rules, reflect syntactic and semantic similarity between the text and the hypothesis. The fact that we use only seven relatively simple features makes our method suitable for low-resource languages. We test our method on the test sets of the RTE competitions and achieve accuracy of up to 69.13%.
PakrayP., BandyopadhyayS. and GelbukhA., Answer Validation using Textual Entailment, Proc International Conference on Intelligent Text Processing and Computational Linguistics, CICLing 2011 Lecture Notes in Computer Science 6609 (2011);359–364.Springer.
2.
PakrayP., GelbukhA. and BandyopadhyayS., A Syntactic Textual Entailment System Based on Dependency Parser, Proc International Conference on Intelligent Text Processing and Computational Linguistics, CICLing 2010 Lecture Notes in Computer Science 6008 (2010), 269–278.
3.
RiosM., SpeciaL., GelbukhA. and MitkovR., Statistical Relational Learning to Recognise Textual Entailment, Proc International Conference on Intelligent Text Processing and Computational Linguistics, CICLing 2014 Lecture Notes in Computer Science 8403 (2014).330–339.Springer.
4.
AkhmatovaE., Textual Entailment Resolution via Atomic Propositions, Proc of the First PASCAL Recognizing Textual Entailment Workshop.2015.
5.
PakrayP., PoriaS., BandyopadhyayS. and GelbukhA., semantic textual entailment recognition using UNL, Polibits43 (2011), 23–27.
6.
RiosM. and GelbukhA., Recognizing textual entailment with a semantic edit distance metric IEEE, 11th Mexican International Conference on Artificial Intelligence (MICAI 2012) (2012).15–20.
7.
KouylekovM. and MagniniB., Recognizing textual entailment with tree edit distance algorithms, Proc of First Workshop Recognising Textual Entailment (2005), 17–20.
8.
MarsiE.C., KrahmerE.J., BosmaW.E. and TheuneM., Normalized alignment of dependency trees for detecting textual entailment, 2006.
9.
HerreraJ., PenasA. and VerdejoF., Textual entailment recognition based on dependency analysis and wordnet, Machine Learning Challenges, Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Textual Entailment (2006), 231–239.Springer.
10.
BlakeC., The role of sentence structure in recognizing textual entailment, pp, Proc of ACL-PASCAL Workshop on Textual Entailment and Paraphrasing, ACL (2007), 101–106.
11.
PakrayP., BandyopadhyayS. and GelbukhA., Dependency parser based textual entailment system IEEE, International Conference on Artificial Intelligence and Computational Intelligence (AICI). 1, (2010).393–397.
12.
PakrayP., NeogiS., BhaskarP., PoriaS., BandyopadhyayS. and GelbukhA., A Textual Entailment System using Anaphora Resolution, NIST, Proc of TAC 2011, 2011.
13.
PakrayP., PalS., PoriaS., BandyopadhyayS. and GelbukhA., JU_CSE_TAC: Textual Entailment Recognition System at TAC RTE-6, NIST, Proc of TAC 2010.2010.
14.
DinuG. and WangR., Inference Rules and their Application to Recognizing Textual Entailment, Association for Computational Linguistics (2009), 211–219.
15.
MalakasiotisP. and AndroutsopoulosI., Learning Textual Entailment using SVMs and String Similarity Measures, Association for Computational Linguistics (2007), 42–47.
16.
MacCartneyB., GrenagerT., MarneffeM.-C., CerD. and ManningC.D., Learning to recognize features of valid textual entailments, Proc of HLT-NAACL, ACL (2006), 41–48.
17.
SzpektorI. and DaganI., Learning Entailment Rules for Unary Templates, pp, Proc of the 22nd International Conference on Computational Linguistics (Coling) (2008), 849–856.
18.
MirkinS., DaganI. and GeffetM., Integrating pattern-based and distributional similarity methods for lexical entailment acquisition, ACL (2006), 579–586.
19.
LiB., IrwinJ., GarciaE.V. and RamA., Machine Learning Based antic Inference: Experiments and Observations at RTE-3, 45 Annual Meeting of the ACL (ACL’07), Workshop on Textual Entailment and Paraphrasing (WTEP-2007)–Sem.
20.
PhamQ.N.M., NguyenL.M. and ShimazuA., A Machine Learning based Textual Entailment Recognition System of JAIST Team for NTCIR9 RITE, Japan, Proceedings of NTCIR-9 Workshop Meeting.2011.Tokyo, Japan.
21.
ZanzottoF.M., PennacchiottiM. and MoschittiA., A machine learning approach to textual entailment recognition, Natural Language Engineering15(4) (2009).551–582.doi:.Cambridge University Press.
22.
AgichteinE., AskewW. and LiuY., Combining Lexical, Syntactic, and antic Evidence for Textual Entailment Classification, TAC, NIST, 2008–Sem.
23.
SaikhT., NaskarS.K., GiriC. and BandyopadhyayS., Textual Entailment Using Different Similarity Metrics, CICLing 2015, Part I, Lecture Notes in Computer Science, 9041, 491–501. doi:10.1007/978-3-319-18111-0_37
24.
RiosM.A., GelbukhA. and BandyopadhyayS., Recognizing Textual Entailment Using a Machine Learning Approach. MICAI Lecture Notes in Artificial Intelligence, (2010), 177–185.Springer.
25.
CastilloJ.J., An approach to Recognizing Textual Entailment and TE Search Task using SVM, SEPLN, (2010).
26.
BasakR., NaskarS.K. and GelbukhA., A Lexico-Syntactic-semantic Approach to Recognizing Textual Entailment. Submitted;.https://goo.gl/KsbHRs.
27.
BasakR., NaskarS.K., PakrayP. and GelbukhA., Recognizing textual entailment by soft dependency tree matching, Computación y Sistemas19(4), 685–700. 2015. DOI: 10.13053/CyS-19-4-2331
28.
DaganI., GlickmanO. and MagniniB., The PASCAL recognising textual entailment challenge, In Machine learning challenges. Evaluating predictive uncertainty, visual object classification, and recognising textual entailment, (2006), 177–190.Springer.
29.
HaimR.B.,
DaganI.,
DolanB.,
FerroL.,
GiampiccoloD.,
MagniniB. and SzpektorI., The Second PASCAL Recognising Textual Entailment challenge, 2006.
30.
GiampiccoloD., MagniniB., DaganI. and DolanB., The Third PASCAL Recognizing Textual Entailment challenge, ACL, pp, Proc of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing (2007), 1–9.
31.
GiampiccoloD., DangH.T., MagniniB., DaganI., CabrioE. and DolanB., The Fourth PASCAL Recognizing Textual Entailment Challenge, TAC, 2008.
