probabilistic graphical model that jointly learns user trustworthiness, statement credibility, and language objectivity. We apply this methodology to the task of extracting rare or unknown side-effects of
Limits of Zero-shot Probing on Object Prediction. (Knowledge Base Construction from Pre-trained Language Models workshop at International Semantic Web Conference (ISWC) 2023) [ pdf ] [ code ] 6. Shrestha
Isabelle Case Study: Expressions in a Programming Language Logic and Proof beyond Equality Isar: A Language for Structured Proofs A Small Imperative Language A Compiler Types Program Analysis Denotational [...] assistant Isabelle/HOL . The second part is an introduction to the semantics of imperative programming languages . This part is formalized in Isabelle. This advanced course is based on a book by Prof. Tobias Nipkow [...] are no formal prerequisites for taking the course. Familiarity with a typed functional programming language (such as Standard ML, OCaml, Haskell, or F#), as taught in Programmierung 1 , is highly recommended
euros over the next three years. More details under https://gepris.dfg.de/gepris/projekt/453095897?language=en Further Information: https://www.akbc.ws/2020/assets/pdfs/pSLmyZKaS.pdf http://people.mpi-inf
preference for rendering), and computer vision. Solid skills in mathematics and related programming languages (e.g., Python) are also required. Please send your application or any inquiry to Dr. Vahid Babaei
shall be estimated [8], as well as how this information can be identified and extracted from natural language documents [13]. 4. Identifying salient negations (16:40-17:00): We show why explicit negations are [...] J., Kisiel, B., Settles, B., Hruschka, E., Mitchell, T.: Toward an architecture for never-ending language learning. In: AAAI (2010) 4. Darari, F., Nutt, W., Pirro, G., Razniewski, S.: Completeness statements
shall be estimated [8], as well as how this information can be identified and extracted from natural language documents [13]. 4. Identifying salient negations (20 min): We show why explicit negations are needed [...] J., Kisiel, B., Settles, B., Hruschka, E., Mitchell, T.: Toward an architecture for never-ending language learning. In: AAAI (2010) 4. Darari, F., Nutt, W., Pirro, G., Razniewski, S.: Completeness statements
shall be estimated [8], as well as how this information can be identified and extracted from natural language documents [13]. 4. Identifying salient negations (40 min): We show why explicit negations are needed [...] J., Kisiel, B., Settles, B., Hruschka, E., Mitchell, T.: Toward an architecture for never-ending language learning. In: AAAI (2010) 4. Darari, F., Nutt, W., Pirro, G., Razniewski, S.: Completeness statements
shall be estimated [8], as well as how this information can be identified and extracted from natural language documents [13]. 4. Identifying salient negations (40 min): We show why explicit negations are needed [...] J., Kisiel, B., Settles, B., Hruschka, E., Mitchell, T.: Toward an architecture for never-ending language learning. In: AAAI (2010) 4. Darari, F., Nutt, W., Pirro, G., Razniewski, S.: Completeness statements
Behavior research methods, 2005 Reference 2: Devereux, Barry J., et al. "The Centre for Speech, Language and the Brain (CSLB) concept property norms." Behavior research methods, 2014 Modelling commonsense [...] Camel? Learning Semantic Plausibility from Text, Porada et al., EMNLP 2019 Reference 2: Do Neural Language Representations Learn Physical Commonsense? Forbes et al., CogSci 2019 Noun compounds Reference [...] NAACL 2018 Reference 2: Chapter 3 in Stephen Tratz. 2011.Semantically-enriched parsingfor natural language understanding. University of Southern California. Reference 3: Advanced Semantics for Commonsense
