23,334 research outputs found
Natural language processing
Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems
New Methods, Current Trends and Software Infrastructure for NLP
The increasing use of `new methods' in NLP, which the NeMLaP conference
series exemplifies, occurs in the context of a wider shift in the nature and
concerns of the discipline. This paper begins with a short review of this
context and significant trends in the field. The review motivates and leads to
a set of requirements for support software of general utility for NLP research
and development workers. A freely-available system designed to meet these
requirements is described (called GATE - a General Architecture for Text
Engineering). Information Extraction (IE), in the sense defined by the Message
Understanding Conferences (ARPA \cite{Arp95}), is an NLP application in which
many of the new methods have found a home (Hobbs \cite{Hob93}; Jacobs ed.
\cite{Jac92}). An IE system based on GATE is also available for research
purposes, and this is described. Lastly we review related work.Comment: 12 pages, LaTeX, uses nemlap.sty (included
Neural Machine Translation Inspired Binary Code Similarity Comparison beyond Function Pairs
Binary code analysis allows analyzing binary code without having access to
the corresponding source code. A binary, after disassembly, is expressed in an
assembly language. This inspires us to approach binary analysis by leveraging
ideas and techniques from Natural Language Processing (NLP), a rich area
focused on processing text of various natural languages. We notice that binary
code analysis and NLP share a lot of analogical topics, such as semantics
extraction, summarization, and classification. This work utilizes these ideas
to address two important code similarity comparison problems. (I) Given a pair
of basic blocks for different instruction set architectures (ISAs), determining
whether their semantics is similar or not; and (II) given a piece of code of
interest, determining if it is contained in another piece of assembly code for
a different ISA. The solutions to these two problems have many applications,
such as cross-architecture vulnerability discovery and code plagiarism
detection. We implement a prototype system INNEREYE and perform a comprehensive
evaluation. A comparison between our approach and existing approaches to
Problem I shows that our system outperforms them in terms of accuracy,
efficiency and scalability. And the case studies utilizing the system
demonstrate that our solution to Problem II is effective. Moreover, this
research showcases how to apply ideas and techniques from NLP to large-scale
binary code analysis.Comment: Accepted by Network and Distributed Systems Security (NDSS) Symposium
201
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