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Finding the N Best Vertices in an Infinite Weighted Hypergraph
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap. (Foundations of Language Processing)
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap. (Foundations of Language Processing)ORCID-id: 0000-0001-7349-7693
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap. (Foundations of Language Processing)
2017 (Engelska)Ingår i: Theoretical Computer Science, ISSN 0304-3975, E-ISSN 1879-2294, Vol. 682, s. 78s. 30-41Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

We propose an algorithm for computing the N best vertices in a weighted acyclic hypergraph over a nice semiring. A semiring is nice if it is finitely-generated, idempotent, and has 1 as its minimal element. We then apply the algorithm to the problem of computing the N best trees with respect to a weighted tree automaton, and complement theoretical correctness and complexity arguments with experimental data. The algorithm has several practical applications in natural language processing, for example, to derive the N most likely parse trees with respect to a probabilistic context-free grammar. 

Ort, förlag, år, upplaga, sidor
Elsevier, 2017. Vol. 682, s. 78s. 30-41
Nyckelord [en]
Hypergraph, N-best problem, Idempotent semiring
Nationell ämneskategori
Datavetenskap (datalogi)
Forskningsämne
datalogi
Identifikatorer
URN: urn:nbn:se:umu:diva-132501DOI: 10.1016/j.tcs.2017.03.010ISI: 000405062100005OAI: oai:DiVA.org:umu-132501DiVA, id: diva2:1081961
Anmärkning

Special Issue: SI

Tillgänglig från: 2017-03-15 Skapad: 2017-03-15 Senast uppdaterad: 2018-11-29Bibliografiskt granskad
Ingår i avhandling
1. Towards semantic language processing
Öppna denna publikation i ny flik eller fönster >>Towards semantic language processing
2018 (Engelska)Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
Alternativ titel[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.

Ort, förlag, år, upplaga, sidor
Umeå: Department of Computing Science, Umeå University, 2018. s. 16
Serie
Report / UMINF, ISSN 0348-0542 ; 18.12
Nyckelord
Weighted tree automata, abstract meaning representation, contextual hyperedge replacement grammar, hyperedge replacement grammar, semantic modelling, the N best problem
Nationell ämneskategori
Datavetenskap (datalogi)
Forskningsämne
datalogi; datorlingvistik
Identifikatorer
urn:nbn:se:umu:diva-153738 (URN)978-91-7601-964-1 (ISBN)
Presentation
2018-12-07, MC413, Umeå, 10:00 (Engelska)
Opponent
Handledare
Tillgänglig från: 2018-11-29 Skapad: 2018-11-28 Senast uppdaterad: 2018-11-29Bibliografiskt granskad

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Björklund, JohannaDrewes, FrankJonsson, Anna

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