The use of data-mining for the automatic formation of tactics

This paper discusses the usse of data-mining for the automatic formation of tactics. It was presented at the Workshop on Computer-Supported Mathematical Theory Development held at IJCAR in 2004. The aim of this project is to evaluate the applicability of data-mining techniques to the automatic formation of tactics from large corpuses of proofs. We data-mine information from large proof corpuses to find commonly occurring patterns. These patterns are then evolved into tactics using genetic programming techniques

Natural language semantics and compiler technology

This paper recommends an approach to the implementation of semantic representation languages (SRLs) which exploits a parallelism between SRLs and programming languages (PLs). The design requirements of SRLs for natural language are similar to those of PLs in their goals. First, in both cases we seek modules in which both the surface representation (print form) and the underlying data structures are important. This requirement highlights the need for general tools allowing the printing and reading of expressions (data structures). Second, these modules need to cooperate with foreign modules, so that the importance of interface technology (compilation) is paramount; and third, both compilers and semantic modules need "inferential" facilities for transforming (simplifying) complex expressions in order to ease subsequent processing. But the most important parallel is the need in both fields for tools which are useful in combination with a variety of concrete languages -- general purpose parsers, printers, simplifiers (transformation facilities) and compilers. This arises in PL technology from (among other things) the need for experimentation in language design, which is again parallel to the case of SRLs. Using a compiler-based approach, we have implemented NLL, a public domain software package for computational natural language semantics. Several interfaces exist both for grammar modules and for applications, using a variety of interface technologies, including especially compilation. We review here a variety of NLL, applications, focusing on COSMA, an NL interface to a distributed appointment manager

The Prevalence and Impact of Persistent Ambiguity in Software Requirements Specification Documents

Despite a large amount of research in methods and tools for avoiding and detecting requirements ambiguity, recent studies have indicated that requirements ambiguity seems to be resolved through multiple inspections and discussions that characterize the requirements engineering process. However, this process may not catch ambiguity types that are likely to result in subconscious disambiguation. People are likely unaware of and incapable of recognizing these ambiguity types; therefore, these types are likely to remain after multiple inspections. This kind of ambiguity is defined as persistent ambiguity and may cause expensive damage. The potential impact of persistent ambiguity was investigated. Initially, a comprehensive ambiguity model based on linguistic ambiguity and its application to requirements engineering was developed. The model was subsequently analyzed to determine the ambiguity types likely to result in subconscious disambiguation and therefore likely to persist. Three requirements specifications were inspected for instances of persistent ambiguity as defined in the model. Each chief requirements engineer verified whether the persistent ambiguities likely to have the greatest impact on each project were indeed interpreted ambiguously, and if so, what the impact was. For the three requirements specifications inspected, there is an average of one persistent ambiguity for every 15.38 pages; project one has the highest average of one persistent ambiguity for every 3.33 pages, project three has an average of one persistent ambiguity for every 31.25 pages, and project two has the lowest average of one persistent ambiguity for every 56 pages. For the three projects, none of the persistent ambiguities reviewed by each chief requirements engineer caused expensive damage because all of the requirements engineers seemed to subconsciously disambiguate the ambiguities in the same way. For the three projects analyzed and the ambiguities reviewed by each chief requirements engineer, the least expensive approach would have been to forego initially identifying persistent ambiguity in these three projects. The first main conclusion is that persistent ambiguity remained undetected by the teams of requirements engineers. The second main conclusion is that the process used by these particular requirements engineering teams for these particular projects is enough to prevent damage. The third main conclusion is that the identification of persistent ambiguity in requirements specifications is potentially an effective and efficient strategy for minimizing damage caused by ambiguity precisely because of its focus on ambiguity that remained undetected due to lack of awareness. Further study is necessary to determine what factors are involved in persistent ambiguity and its prevalence, as well as its potential impacts

Scalable, Efficient and Precise Natural Language Processing in the Semantic Web

The Internet of Things (IoT) is an emerging phenomenon in the public space. Users with accessibility needs could especially benefit from these “smart” devices if they were able to interact with them through speech. This thesis presents a Compositional Semantics and framework for developing extensible and expressive Natural Language Query Interfaces to the Semantic Web, addressing privacy and auditability needs in the process. This could be particularly useful in healthcare or legal applications, where confidentiality of information is a key concer

Trustworthy Formal Natural Language Specifications

Interactive proof assistants are computer programs carefully constructed to check a human-designed proof of a mathematical claim with high confidence in the implementation. However, this only validates truth of a formal claim, which may have been mistranslated from a claim made in natural language. This is especially problematic when using proof assistants to formally verify the correctness of software with respect to a natural language specification. The translation from informal to formal remains a challenging, time-consuming process that is difficult to audit for correctness. This paper shows that it is possible to build support for specifications written in expressive subsets of natural language, within existing proof assistants, consistent with the principles used to establish trust and auditability in proof assistants themselves. We implement a means to provide specifications in a modularly extensible formal subset of English, and have them automatically translated into formal claims, entirely within the Lean proof assistant. Our approach is extensible (placing no permanent restrictions on grammatical structure), modular (allowing information about new words to be distributed alongside libraries), and produces proof certificates explaining how each word was interpreted and how the sentence's structure was used to compute the meaning. We apply our prototype to the translation of various English descriptions of formal specifications from a popular textbook into Lean formalizations; all can be translated correctly with a modest lexicon with only minor modifications related to lexicon size.Comment: arXiv admin note: substantial text overlap with arXiv:2205.0781

From clinics to methods and back: a tale of amyloid-PET quantification

The in-vivo assessment of cerebral amyloid load is taking a leading role in the early differential diagnosis of neurodegenerative diseases. With the hopefully near introduction of disease-modifying drugs, we expect a paradigm shift in the current diagnostic pathway with an unprecedented surge in the request of exams and detailed analysis