The Experts below are selected from a list of 71445 Experts worldwide ranked by ideXlab platform
Malvina Nissim - One of the best experts on this subject based on the ideXlab platform.
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the meaning factory Formal Semantics for recognizing textual entailment and determining semantic similarity
International Conference on Computational Linguistics, 2014Co-Authors: Johannes Bjerva, Rob Van Der Goot, Malvina NissimAbstract:Shared Task 1 of SemEval-2014 comprised two subtasks on the same dataset of sentence pairs: recognizing textual entailment and determining textual similarity. We used an existing system based on Formal Semantics and logical inference to participate in the first subtask, reaching an accuracy of 82%, ranking in the top 5 of more than twenty participating systems. For determining semantic similarity we took a supervised approach using a variety of features, the majority of which was produced by our system for recognizing textual entailment. In this subtask our system achieved a mean squared error of 0.322, the best of all participating systems.
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the meaning factory Formal Semantics for recognizing textual entailment and determining semantic similarity
International Conference on Computational Linguistics, 2014Co-Authors: Johannes Bjerva, Malvina Nissim, Johan Bos, Rob Van Der GootAbstract:Shared Task 1 of SemEval-2014 comprised two subtasks on the same dataset of sentence pairs: recognizing textual entailment and determining textual similarity. We used an existing system based on Formal Semantics and logical inference to participate in the first subtask, reaching an accuracy of 82%, ranking in the top 5 of more than twenty participating systems. For determining semantic similarity we took a supervised approach using a variety of features, the majority of which was produced by our system for recognizing textual entailment. In this subtask our system achieved a mean squared error of 0.322, the best of all participating systems.
Johannes Bjerva - One of the best experts on this subject based on the ideXlab platform.
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the meaning factory Formal Semantics for recognizing textual entailment and determining semantic similarity
International Conference on Computational Linguistics, 2014Co-Authors: Johannes Bjerva, Rob Van Der Goot, Malvina NissimAbstract:Shared Task 1 of SemEval-2014 comprised two subtasks on the same dataset of sentence pairs: recognizing textual entailment and determining textual similarity. We used an existing system based on Formal Semantics and logical inference to participate in the first subtask, reaching an accuracy of 82%, ranking in the top 5 of more than twenty participating systems. For determining semantic similarity we took a supervised approach using a variety of features, the majority of which was produced by our system for recognizing textual entailment. In this subtask our system achieved a mean squared error of 0.322, the best of all participating systems.
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the meaning factory Formal Semantics for recognizing textual entailment and determining semantic similarity
International Conference on Computational Linguistics, 2014Co-Authors: Johannes Bjerva, Malvina Nissim, Johan Bos, Rob Van Der GootAbstract:Shared Task 1 of SemEval-2014 comprised two subtasks on the same dataset of sentence pairs: recognizing textual entailment and determining textual similarity. We used an existing system based on Formal Semantics and logical inference to participate in the first subtask, reaching an accuracy of 82%, ranking in the top 5 of more than twenty participating systems. For determining semantic similarity we took a supervised approach using a variety of features, the majority of which was produced by our system for recognizing textual entailment. In this subtask our system achieved a mean squared error of 0.322, the best of all participating systems.
Grigore Rosu - One of the best experts on this subject based on the ideXlab platform.
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a complete Formal Semantics of x86 64 user level instruction set architecture
Programming Language Design and Implementation, 2019Co-Authors: Sandeep Dasgupta, Daejun Park, Theodoros Kasampalis, Vikram Adve, Grigore RosuAbstract:We present the most complete and thoroughly tested Formal Semantics of x86-64 to date. Our Semantics faithfully Formalizes all the non-deprecated, sequential user-level instructions of the x86-64 Haswell instruction set architecture. This totals 3155 instruction variants, corresponding to 774 mnemonics. The Semantics is fully executable and has been tested against more than 7,000 instruction-level test cases and the GCC torture test suite. This extensive testing paid off, revealing bugs in both the x86-64 reference manual and other existing Semantics. We also illustrate potential applications of our Semantics in different Formal analyses, and discuss how it can be useful for processor verification.
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p4k a Formal Semantics of p4 and applications
arXiv: Networking and Internet Architecture, 2018Co-Authors: Ali Kheradmand, Grigore RosuAbstract:Programmable packet processors and P4 as a programming language for such devices have gained significant interest, because their flexibility enables rapid development of a diverse set of applications that work at line rate. However, this flexibility, combined with the complexity of devices and networks, increases the chance of introducing subtle bugs that are hard to discover manually. Worse, this is a domain where bugs can have catastrophic consequences, yet Formal analysis tools for P4 programs / networks are missing. We argue that Formal analysis tools must be based on a Formal Semantics of the target language, rather than on its inFormal specification. To this end, we provide an executable Formal Semantics of the P4 language in the K framework. Based on this Semantics, K provides an interpreter and various analysis tools including a symbolic model checker and a deductive program verifier for P4. This paper overviews our Formal K Semantics of P4, as well as several P4 language design issues that we found during our Formalization process. We also discuss some applications resulting from the tools provided by K for P4 programmers and network administrators as well as language designers and compiler developers, such as detection of unportable code, state space exploration of P4 programs and of networks, bug finding using symbolic execution, data plane verification, program verification, and translation validation.
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kjs a complete Formal Semantics of javascript
Programming Language Design and Implementation, 2015Co-Authors: Daejun Park, Andrei Stefănescu, Grigore RosuAbstract:This paper presents KJS, the most complete and throughly tested Formal Semantics of JavaScript to date. Being executable, KJS has been tested against the ECMAScript 5.1 conformance test suite, and passes all 2,782 core language tests. Among the existing implementations of JavaScript, only Chrome V8's passes all the tests, and no other Semantics passes more than 90%. In addition to a reference implementation for JavaScript, KJS also yields a simple coverage metric for a test suite: the set of semantic rules it exercises. Our Semantics revealed that the ECMAScript 5.1 conformance test suite fails to cover several semantic rules. Guided by the Semantics, we wrote tests to exercise those rules. The new tests revealed bugs both in production JavaScript engines (Chrome V8, Safari WebKit, Firefox SpiderMonkey) and in other Semantics. KJS is symbolically executable, thus it can be used for Formal analysis and verification of JavaScript programs. We verified non-trivial programs and found a known security vulnerability.
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kjs a complete Formal Semantics of javascript
Programming Language Design and Implementation, 2015Co-Authors: Daejun Park, Andrei Stefănescu, Grigore RosuAbstract:This paper presents KJS, the most complete and throughly tested Formal Semantics of JavaScript to date. Being executable, KJS has been tested against the ECMAScript 5.1 conformance test suite, and passes all 2,782 core language tests. Among the existing implementations of JavaScript, only Chrome V8's passes all the tests, and no other Semantics passes more than 90%. In addition to a reference implementation for JavaScript, KJS also yields a simple coverage metric for a test suite: the set of semantic rules it exercises. Our Semantics revealed that the ECMAScript 5.1 conformance test suite fails to cover several semantic rules. Guided by the Semantics, we wrote tests to exercise those rules. The new tests revealed bugs both in production JavaScript engines (Chrome V8, Safari WebKit, Firefox SpiderMonkey) and in other Semantics. KJS is symbolically executable, thus it can be used for Formal analysis and verification of JavaScript programs. We verified non-trivial programs and found a known security vulnerability.
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an executable Formal Semantics of c with applications
Symposium on Principles of Programming Languages, 2012Co-Authors: Chucky Ellison, Grigore RosuAbstract:This paper describes an executable Formal Semantics of C. Being executable, the Semantics has been thoroughly tested against the GCC torture test suite and successfully passes 99.2% of 776 test programs. It is the most complete and thoroughly tested Formal definition of C to date. The Semantics yields an interpreter, debugger, state space search tool, and model checker "for free". The Semantics is shown capable of automatically finding program errors, both statically and at runtime. It is also used to enumerate nondeterministic behavior.
Zhaohui Luo - One of the best experts on this subject based on the ideXlab platform.
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Formal Semantics in modern type theories is it model theoretic proof theoretic or both
Logical Aspects of Computational Linguistics, 2014Co-Authors: Zhaohui LuoAbstract:In this talk, we contend that, for NLs, the divide between model-theoretic Semantics and proof-theoretic Semantics has not been well-understood. In particular, the Formal Semantics based on modern type theories (MTTs) may be seen as both model-theoretic and proof-theoretic. To be more precise, it may be seen both ways in the sense that the NL Semantics can first be represented in an MTT in a model-theoretic way and then the semantic representations can be understood inferentially in a proof-theoretic way. Considered in this way, MTTs arguably have unique advantages when employed for Formal Semantics.
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Formal Semantics in modern type theories with coercive subtyping
Linguistics and Philosophy, 2012Co-Authors: Zhaohui LuoAbstract:In the Formal Semantics based on modern type theories, common nouns are interpreted as types, rather than as predicates of entities as in Montague’s Semantics. This brings about important advantages in linguistic interpretations but also leads to a limitation of expressive power because there are fewer operations on types as compared with those on predicates. The theory of coercive subtyping adequately extends the modern type theories and, as shown in this paper, plays a very useful role in making type theories more expressive for Formal Semantics. It not only gives a satisfactory solution to the basic problem of ‘multiple categorisation’ caused by interpreting common nouns as types, but provides a powerful Formal framework to model interesting linguistic phenomena such as copredication, whose Formal treatment has been found difficult in a Montagovian setting. In particular, we show how to Formally introduce dot-types in a type theory with coercive subtyping and study some type-theoretic constructs that provide useful representational tools for reference transfers and multiple word meanings in Formal lexical Semantics.
Rob Van Der Goot - One of the best experts on this subject based on the ideXlab platform.
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the meaning factory Formal Semantics for recognizing textual entailment and determining semantic similarity
International Conference on Computational Linguistics, 2014Co-Authors: Johannes Bjerva, Malvina Nissim, Johan Bos, Rob Van Der GootAbstract:Shared Task 1 of SemEval-2014 comprised two subtasks on the same dataset of sentence pairs: recognizing textual entailment and determining textual similarity. We used an existing system based on Formal Semantics and logical inference to participate in the first subtask, reaching an accuracy of 82%, ranking in the top 5 of more than twenty participating systems. For determining semantic similarity we took a supervised approach using a variety of features, the majority of which was produced by our system for recognizing textual entailment. In this subtask our system achieved a mean squared error of 0.322, the best of all participating systems.
