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A Comparison of Two N-Best Extraction Methods for Weighted Tree Automata
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Foundations of Language Processing)
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Foundations of Language Processing)ORCID iD: 0000-0001-7349-7693
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Foundations of Language Processing)
2018 (English)In: Implementation and Application of Automata: 23rd International Conference, CIAA 2018, Charlottetown, PE, Canada, July 30 – August 2, 2018, Proceedings, Springer, 2018, p. 197-108Conference paper, Published paper (Refereed)
Abstract [en]

We conduct a comparative study of two state-of-the-art al- gorithms for extracting the N best trees from a weighted tree automaton (wta). The algorithms are Best Trees, which uses a priority queue to structure the search space, and Filtered Runs, which is based on an algorithm by Huang and Chiang that extracts N best runs, implemented as part of the Tiburon wta toolkit. The experiments are run on four data sets, each consisting of a sequence of wtas of increasing sizes. Our conclusion is that Best Trees can be recommended when the input wtas exhibit a high or unpredictable degree of nondeterminism, whereas Filtered Runs is the better option when the input wtas are large but essentially deterministic.

Place, publisher, year, edition, pages
Springer, 2018. p. 197-108
Series
Lecture Notes in Computer Science
Keywords [en]
N-best list, tree automaton
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:umu:diva-149994DOI: 10.1007/978-3-319-94812-6_9ISI: 000469285600009ISBN: 978-3-319-94812-6 (electronic)ISBN: 978-3-319-94811-9 (print)OAI: oai:DiVA.org:umu-149994DiVA, id: diva2:1229456
Conference
23rd International Conference on Implementation and Applications of Automata (CIAA 2018), Charlottetown, Canada, July 30-August 2, 2018
Available from: 2018-06-30 Created: 2018-06-30 Last updated: 2019-06-20Bibliographically approved
In thesis
1. Towards semantic language processing
Open this publication in new window or tab >>Towards semantic language processing
2018 (English)Licentiate thesis, comprehensive summary (Other academic)
Alternative title[sv]
Mot semantisk språkbearbetning
Abstract [en]

The overall goal of the field of natural language processing is to facilitate the communication between humans and computers, and to help humans with natural language problems such as translation. In this thesis, we focus on semantic language processing. Modelling semantics – the meaning of natural language – requires both a structure to hold the semantic information and a device that can enforce rules on the structure to ensure well-formed semantics while not being too computationally heavy. The devices used in natural language processing are preferably weighted to allow for comparison of the alternative semantic interpretations outputted by a device.

The structure employed here is the abstract meaning representation (AMR). We show that AMRs representing well-formed semantics can be generated while leaving out AMRs that are not semantically well-formed. For this purpose, we use a type of graph grammar called contextual hyperedge replacement grammar (CHRG). Moreover, we argue that a more well-known subclass of CHRG – the hyperedge replacement grammar (HRG) – is not powerful enough for AMR generation. This is due to the limitation of HRG when it comes to handling co-references, which in its turn depends on the fact that HRGs only generate graphs of bounded treewidth.

Furthermore, we also address the N best problem, which is as follows: Given a weighted device, return the N best (here: smallest-weighted, or more intuitively, smallest-errored) structures. Our goal is to solve the N best problem for devices capable of expressing sophisticated forms of semantic representations such as CHRGs. Here, however, we merely take a first step consisting in developing methods for solving the N best problem for weighted tree automata and some types of weighted acyclic hypergraphs.

Place, publisher, year, edition, pages
Umeå: Department of Computing Science, Umeå University, 2018. p. 16
Series
Report / UMINF, ISSN 0348-0542 ; 18.12
Keywords
Weighted tree automata, abstract meaning representation, contextual hyperedge replacement grammar, hyperedge replacement grammar, semantic modelling, the N best problem
National Category
Computer Sciences
Research subject
Computer Science; datorlingvistik
Identifiers
urn:nbn:se:umu:diva-153738 (URN)978-91-7601-964-1 (ISBN)
Presentation
2018-12-07, MC413, Umeå, 10:00 (English)
Opponent
Supervisors
Available from: 2018-11-29 Created: 2018-11-28 Last updated: 2018-11-29Bibliographically approved

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Björklund, JohannaDrewes, FrankJonsson, Anna

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