Learning Language from a Large (Unannotated) Corpus

A novel approach to the fully automated, unsupervised extraction of dependency grammars and associated syntax-to-semantic-relationship mappings from large text corpora is described. The suggested approach builds on the authors' prior work with the Link Grammar, RelEx and OpenCog systems, as well as on a number of prior papers and approaches from the statistical language learning literature. If successful, this approach would enable the mining of all the information needed to power a natural language comprehension and generation system, directly from a large, unannotated corpus.Comment: 29 pages, 5 figures, research proposa

Automated generation of program translation and verification tools using annotated grammars

Automatically generating program translators from source and target language specifications is a non-trivial problem. In this paper we focus on the problem of automating the process of building translators between operations languages, a family of DSLs used to program satellite operations procedures. We exploit their similarities to semi-automatically build transformation tools between these DSLs. The input to our method is a collection of annotated context-free grammars. To simplify the overall translation process even more, we also propose an intermediate representation common to all operations languages. Finally, we discuss how to enrich our annotated grammars model with more advanced semantic annotations to provide a verification system for the translation process. We validate our approach by semi-automatically deriving translators between some real world operations languages, using the prototype tool which we implemented for that purpose

Research in the Language, Information and Computation Laboratory of the University of Pennsylvania

This report takes its name from the Computational Linguistics Feedback Forum (CLiFF), an informal discussion group for students and faculty. However the scope of the research covered in this report is broader than the title might suggest; this is the yearly report of the LINC Lab, the Language, Information and Computation Laboratory of the University of Pennsylvania. It may at first be hard to see the threads that bind together the work presented here, work by faculty, graduate students and postdocs in the Computer Science and Linguistics Departments, and the Institute for Research in Cognitive Science. It includes prototypical Natural Language fields such as: Combinatorial Categorial Grammars, Tree Adjoining Grammars, syntactic parsing and the syntax-semantics interface; but it extends to statistical methods, plan inference, instruction understanding, intonation, causal reasoning, free word order languages, geometric reasoning, medical informatics, connectionism, and language acquisition. Naturally, this introduction cannot spell out all the connections between these abstracts; we invite you to explore them on your own. In fact, with this issue it’s easier than ever to do so: this document is accessible on the “information superhighway”. Just call up http://www.cis.upenn.edu/~cliff-group/94/cliffnotes.html In addition, you can find many of the papers referenced in the CLiFF Notes on the net. Most can be obtained by following links from the authors’ abstracts in the web version of this report. The abstracts describe the researchers’ many areas of investigation, explain their shared concerns, and present some interesting work in Cognitive Science. We hope its new online format makes the CLiFF Notes a more useful and interesting guide to Computational Linguistics activity at Penn

A Falsification View of Success Typing

Dynamic languages are praised for their flexibility and expressiveness, but static analysis often yields many false positives and verification is cumbersome for lack of structure. Hence, unit testing is the prevalent incomplete method for validating programs in such languages. Falsification is an alternative approach that uncovers definite errors in programs. A falsifier computes a set of inputs that definitely crash a program. Success typing is a type-based approach to document programs in dynamic languages. We demonstrate that success typing is, in fact, an instance of falsification by mapping success (input) types into suitable logic formulae. Output types are represented by recursive types. We prove the correctness of our mapping (which establishes that success typing is falsification) and we report some experiences with a prototype implementation.Comment: extended versio