LEADER 04045nam 22006735 450 001 9910590079103321 005 20251107111811.0 010 $a9783658386979 010 $a3658386975 024 7 $a10.1007/978-3-658-38697-9 035 $a(CKB)5680000000072234 035 $a(MiAaPQ)EBC7078334 035 $a(Au-PeEL)EBL7078334 035 $a(OCoLC)1344539153 035 $a(DE-He213)978-3-658-38697-9 035 $a(EXLCZ)995680000000072234 100 $a20220829d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aFrom Complex Sentences to a Formal Semantic Representation using Syntactic Text Simplification and Open Information Extraction /$fby Christina Niklaus 205 $a1st ed. 2022. 210 1$aWiesbaden :$cSpringer Fachmedien Wiesbaden :$cImprint: Springer Vieweg,$d2022. 215 $a1 online resource (0 pages) 311 08$a9783658386962 311 08$a3658386967 320 $aIncludes bibliographical references. 327 $aBackground -- Discourse-Aware Sentence Splitting -- Open Information Extraction -- Evaluation -- Conclusion. 330 $aThis work presents a discourse-aware Text Simplification approach that splits and rephrases complex English sentences within the semantic context in which they occur. Based on a linguistically grounded transformation stage, complex sentences are transformed into shorter utterances with a simple canonical structure that can be easily analyzed by downstream applications. To avoid breaking down the input into a disjointed sequence of statements that is difficult to interpret, the author incorporates the semantic context between the split propositions in the form of hierarchical structures and semantic relationships, thus generating a novel representation of complex assertions that puts a semantic layer on top of the simplified sentences. In a second step, she leverages the semantic hierarchy of minimal propositions to improve the performance of Open IE frameworks. She shows that such systems benefit in two dimensions. First, the canonical structure of the simplified sentences facilitates the extraction of relational tuples, leading to an improved precision and recall of the extracted relations. Second, the semantic hierarchy can be leveraged to enrich the output of existing Open IE approaches with additional meta-information, resulting in a novel lightweight semantic representation for complex text data in the form of normalized and context-preserving relational tuples. About the author Christina Niklaus is an Assistant Professor in Computer Science at the University of St.Gallen with a focus on Data Science and NLP. . 606 $aComputational linguistics 606 $aNatural language processing (Computer science) 606 $aProgramming languages (Electronic computers) 606 $aComputational Linguistics 606 $aNatural Language Processing (NLP) 606 $aProgramming Language 606 $aTractament del llenguatge natural (Informàtica)$2thub 606 $aTractament de textos$2thub 606 $aLingüística computacional$2thub 608 $aLlibres electrònics$2thub 615 0$aComputational linguistics. 615 0$aNatural language processing (Computer science) 615 0$aProgramming languages (Electronic computers) 615 14$aComputational Linguistics. 615 24$aNatural Language Processing (NLP). 615 24$aProgramming Language. 615 7$aTractament del llenguatge natural (Informàtica) 615 7$aTractament de textos 615 7$aLingüística computacional 676 $a410.285 700 $aNiklaus$b Christina$01271320 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910590079103321 996 $aFrom Complex Sentences to a Formal Semantic Representation Using Syntactic Text Simplification and Open Information Extraction$92994705 997 $aUNINA