5,011 research outputs found
Abstract syntax as interlingua: Scaling up the grammatical framework from controlled languages to robust pipelines
Syntax is an interlingual representation used in compilers. Grammatical Framework (GF) applies the abstract syntax idea to natural languages. The development of GF started in 1998, first as a tool for controlled language implementations, where it has gained an established position in both academic and commercial projects. GF provides grammar resources for over 40 languages, enabling accurate generation and translation, as well as grammar engineering tools and components for mobile and Web applications. On the research side, the focus in the last ten years has been on scaling up GF to wide-coverage language processing. The concept of abstract syntax offers a unified view on many other approaches: Universal Dependencies, WordNets, FrameNets, Construction Grammars, and Abstract Meaning Representations. This makes it possible for GF to utilize data from the other approaches and to build robust pipelines. In return, GF can contribute to data-driven approaches by methods to transfer resources from one language to others, to augment data by rule-based generation, to check the consistency of hand-annotated corpora, and to pipe analyses into high-precision semantic back ends. This article gives an overview of the use of abstract syntax as interlingua through both established and emerging NLP applications involving GF
A preliminary bibliography on focus
[I]n its present form, the bibliography contains approximately 1100 entries. Bibliographical work is never complete, and the present one is still modest in a number of respects. It is not annotated, and it still contains a lot of mistakes and inconsistencies. It has nevertheless reached a stage which justifies considering the possibility of making it available to the public. The first step towards this is its pre-publication in the form of this working paper. […]
The bibliography is less complete for earlier years. For works before 1970, the bibliographies of Firbas and Golkova 1975 and Tyl 1970 may be consulted, which have not been included here
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Does a Neural Model Understand the De Re / De Dicto Distinction?
Neural network language models (NNLMs) are often casually said to understand language, but what linguistic structures do they really learn? We pose this question in the context of de re / de dicto ambiguities. Nouns and determiner phrases in intensional contexts, such as belief, desire, and modality, are subject to referential ambiguities. The phrase Lilo believes an alien is on the loose,\u27\u27 for example, has two interpretations: one ( de re ) in which she believes a specific entity which happens to be an alien is on the loose, and another ( de dicto ) in which she believes some unspecified alien is on the loose. In this paper we confront an NNLM with contexts producing de re / de dicto ambiguities. We use coreference resolution to investigate which interpretive possibilities the model captures. We find that while RoBERTa is sensitive to the fact that intensional predicates and indefinite determiners each change coreference possibilities, it does not grasp how the two interact with each other, and hence misses a deeper level of semantic structure. This inquiry is novel in its cross-disciplinary approach to philosophy, semantics and NLP, bringing formal semantic insight to an active research area testing the nature of NNLMs\u27 linguistic understanding
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