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- Angelov, Krasimir, 1978, et al.
(författare)
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Fast Statistical Parsing with Parallel Multiple Context-Free Grammars
- 2014
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Ingår i: EACL'14, 14th Conference of the European Chapter of the Association for Computational Linguistics.
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Konferensbidrag (refereegranskat)abstract
- We present an algorithm for incremental statistical parsing with Parallel Multiple Context-Free Grammars (PMCFG). This is an extension of the algorithm by Angelov (2009) to which we added statistical ranking. We show that the new algorithm is several times faster than other statistical PMCFG parsing algorithms on real-sized grammars. At the same time the algorithm is more general since it supports non-binarized and non-linear grammars. We also show that if we make the search heuristics non-admissible, the parsing speed improves even further, at the risk of returning sub-optimal solutions.
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| 5. |
- Burden, Håkan, 1976, et al.
(författare)
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Parsing linear context-free rewriting systems
- 2005
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Ingår i: IWPT'05, 9th International Workshop on Parsing Technologies.
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Konferensbidrag (refereegranskat)abstract
- We describe four different parsing algorithms for Linear Context-Free Rewriting Systems (Vijay-Shanker et al., 1987). The algorithms are described as deduction systems, and possible optimizations are discussed.
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| 6. |
- Ljunglöf, Peter, 1971
(författare)
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Formalizing the dialogue move engine
- 2000
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Ingår i: Götalog Workshop on Semantics and Pragmatics of Dialogue.
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Konferensbidrag (refereegranskat)abstract
- In this paper we present a calculus for reasoning mathematically about rule-based dialogue systems so called dialogue move engines developed in the TRINDI project. The calculus is similar to term rewriting systems and dynamic logic. It is defined using monads, which are used for describing programming languages, and in functional programming to capture computations with side-effects.
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| 7. |
- Bamutura, David, 1984, et al.
(författare)
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Towards a Resource Grammar for Runyankore and Rukiga
- 2019
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Ingår i: WiNLP 2019, the 3rd Workshop on Widening NLP, Florence, Italy, 28th July 2019.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Currently, there is a lack of computational grammar resources for many under-resourced languages which limits the ability to develop Natural Language Processing (NLP) tools and applications such as Multilingual Document Authoring, Computer-Assisted Language Learning (CALL) and Low-Coverage Machine Translation (MT) for these languages. In this paper, we present our attempt to formalise the grammar of two such languages: Runyankore and Rukiga. For this formalisation we use the Grammatical Framework (GF) and its Resource Grammar Library (GF-RGL).
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| 8. |
- Bamutura, David, 1984, et al.
(författare)
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Towards computational resource grammars for runyankore and rukiga
- 2020
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Ingår i: LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings. - : European Language Resources Association. ; , s. 2846-2854
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Konferensbidrag (refereegranskat)abstract
- In this paper, we present computational resource grammars of Runyankore and Rukiga (R&R) languages. Runyankore and Rukiga are two under-resourced Bantu Languages spoken by about 6 million people indigenous to South Western Uganda, East Africa. We used Grammatical Framework (GF), a multilingual grammar formalism and a special-purpose functional programming language to formalise the descriptive grammar of these languages. To the best of our knowledge, these computational resource grammars are the first attempt to the creation of language resources for R&R. In Future Work, we plan to use these grammars to bootstrap the generation of other linguistic resources such as multilingual corpora that make use of data-driven approaches to natural language processing feasible. In the meantime, they can be used to build Computer-Assisted Language Learning (CALL) applications for these languages among others.
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| 9. |
- Lange, Herbert, 1987, et al.
(författare)
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Learning domain-specific grammars from a small number of examples
- 2020
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Ingår i: ICAART 2020 - Proceedings of the 12th International Conference on Agents and Artificial Intelligence. - : SCITEPRESS - Science and Technology Publications. ; 1, s. 422-430
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Konferensbidrag (refereegranskat)abstract
- In this paper we investigate the problem of grammar inference from a different perspective. The common approach is to try to infer a grammar directly from example sentences, which either requires a large training set or suffers from bad accuracy. We instead view it as a problem of grammar restriction or sub-grammar extraction. We start from a large-scale resource grammar and a small number of examples, and find a subgrammar that still covers all the examples. To do this we formulate the problem as a constraint satisfaction problem, and use an existing constraint solver to find the optimal grammar. We have made experiments with English, Finnish, German, Swedish and Spanish, which show that 10–20 examples are often sufficient to learn an interesting domain grammar. Possible applications include computer-assisted language learning, domain-specific dialogue systems, computer games, Q/A-systems, and others.
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| 10. |
- Lange, Herbert, 1987, et al.
(författare)
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Learning Domain-Specific Grammars from a Small Number of Examples
- 2021
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Ingår i: Studies in Computational Intelligence. - Cham : Springer International Publishing. - 1860-9503 .- 1860-949X. - 9783030637873 ; 939, s. 105-138
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Konferensbidrag (refereegranskat)abstract
- In this chapter we investigate the problem of grammar learning from a perspective that diverges from previous approaches. These prevailing approaches to learning grammars usually attempt to infer a grammar directly from example corpora without any additional information. This either requires a large training set or suffers from bad accuracy. We instead view learning grammars as a problem of grammar restriction or subgrammar extraction. We start from a large-scale grammar (called a resource grammar) and a small number of example sentences, and find a subgrammar that still covers all the examples. To accomplish this, we formulate the problem as a constraint satisfaction problem, and use a constraint solver to find the optimal grammar. We created experiments with English, Finnish, German, Swedish, and Spanish, which show that 10–20 examples are often sufficient to learn an interesting grammar for a specific application. We also present two extensions to this basic method: we include negative examples and allow rules to be merged. The resulting grammars can more precisely cover specific linguistic phenomena. Our method, together with the extensions, can be used to provide a grammar learning system for specific applications. This system is easy-to-use, human-centric, and can be used by non-syntacticians. Based on this grammar learning method, we can build applications for computer-assisted language learning and interlingual communication, which rely heavily on the knowledge of language and domain experts who often lack the competence to develop required grammars themselves.
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