@inbook {BuchardfEtAl_inBoas2009:annoReprAutomation,
	title = {Using {FrameNet} for the semantic analysis of {G}erman: {A}nnotation, representation, and automation},
	booktitle = {Multilingual FrameNets in Computational Lexicography},
	year = {2009},
	pages = {209-244},
	publisher = {Mouton},
	organization = {Mouton},
	author = {Aljoscha Burchardt and Erk, Katrin and Anette Frank and Andrea Kowalski and Sebastian Pad{\'o} and Pinkal, Manfred},
	editor = {Boas, Hans C.}
}
@conference {burchardt05:_WN_detour,
	title = {A {WordNet} detour to {FrameNet}},
	booktitle = {Proceedings of the GLDV 2005 workshop GermaNet II},
	year = {2005},
	address = {Bonn},
	url = {http://www.coli.uni-saarland.de/\%7Ealbu/papers/gnws05_burchardt_erk_frank-final.pdf},
	author = {Aljoscha Burchardt and Erk, Katrin and Anette Frank}
}
@inbook {burchardt05:_WN_detour_FN,
	title = {A {WordNet} Detour to {FrameNet}},
	booktitle = {Sprachtechnologie, mobile Kommunikation und linguistische Resourcen},
	series = {Computer Studies in Language and Speech},
	volume = {8},
	year = {2005},
	pages = {408-421},
	publisher = {Peter Lang},
	organization = {Peter Lang},
	address = {Frankfurt},
	author = {Aljoscha Burchardt and Erk, Katrin and Anette Frank},
	editor = {Bernhard Fisseni and Hans-Christian Schmitz and Bernhard Schr{\"o}der and Petra Wagner}
}
@mastersthesis {BurchardtPhDiss2008:ModelingTextualEntailmentRole-Sem,
	title = {Modeling Textual Entailment with Role-Semantic Information},
	year = {2008},
	school = {Universit{\"a}t des Saarlandes},
	type = {phd},
	abstract = {In this thesis, we present a novel approach for modeling textual entailment using lexical-semantic information on the level of predicate-argument structure. To this end, we adopt information provided by the Berkeley FrameNet repository and embed it into an implemented end-to-end system. The two main goals of this thesis are the following: (i) to provide an analysis of the potential contribution of frame semantic information to the recognition textual entailment and (ii) to present a robust system architecture that can serve as basis for future experiments, research, and improvement. Our work was carried out in the context of the textual entailment initiative, which since 2005 has set the stage for the broad investigation of inference in natural-language processing tasks, including empirical evaluation of its coverage and reliability. In short, textual entailment describes inferential relations between (entailing) texts and (entailed) hypotheses as interpreted by typical language users. This pre-theoretic notion captures a natural range of inferences as compared to logical entailment, which has traditionally been used within theoretical approaches to natural language semantics. Various methods for modeling textual entailment have been proposed in the literature, ranging from shallow techniques like lexical overlap to shallow syntactic parsing and the exploitation of WordNet relations. Recently, there has been a move towards more structured meaning representations. In particular, the level of predicate-argumentstructure has gained much attention, which seems to be a natural and straightforwardchoice. Predicate-argument structure allows annotating sentences or texts with nuclear meaning representations ("who did what to whom"), which are of obvious relevance for this task. For example, it can account for paraphrases like "Ghosts scare John" vs. "John is scared by ghosts". In this thesis, we present an approach to textual entailment that is centered around the analysis of predicate-argument structure. It combines LFG grammatical analysis, predicate-argument structure in the FrameNet paradigm, and taxonomic information from WordNet into tripartite graph structures. By way of a declarative graph matching algorithm, the "structural and semantic" similarity of hypotheses and texts is computed and the result is represented as feature vectors. A supervised machine learning architecture trained on entailment corpora is used to check textual entailment for new text/hypothesis pairs. The approach is implemented in the SALSA RTE system, which successfully participated in the second and third RTE challenge.},
	url = {http://www.dfki.de/lt/diss_detail.php?id=29},
	author = {Aljoscha Burchardt}
}
@article {BurchardtEtAl_NLE2009:ImpactFSonTextEntail,
	title = {Assessing the impact of frame semantics on textual entailment},
	journal = {Natural Language Engineering},
	volume = {15},
	number = {Special Issue 04},
	year = {2009},
	pages = {527-550},
	abstract = {In this article, we underpin the intuition that frame semantic information is a useful resource for modelling textual entailment. To this end, we provide a manual frame semantic annotation for the test set used in the second recognizing textual entailment (RTE) challenge and discuss experiments we conducted on this basis. In particular, our experiments show that the frame semantic lexicon provided by the Berkeley FrameNet project provides surprisingly good coverage for the task at hand. We identify issues of automatic semantic analysis components, as well as insufficient modelling of the information provided by frame semantic analysis as reasons for ambivalent results of current systems based on frame semantics.},
	doi = {10.1017/S1351324909990131},
	url = {http://journals.cambridge.org/action/displayAbstract?fromPage=online\&aid=6171000\&fulltextType=RA\&fileId=S1351324909990131},
	author = {Aljoscha Burchardt and Pennachiotti, Marco and Thater, Stefan and Pinkal, Manfred}
}
