Ewa Muszyńska


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Realization of long sentences using chunking
Ewa Muszyńska | Ann Copestake
Proceedings of the 10th International Conference on Natural Language Generation

We propose sentence chunking as a way to reduce the time and memory costs of realization of long sentences. During chunking we divide the semantic representation of a sentence into smaller components which can be processed and recombined without loss of information. Our meaning representation of choice is the Dependency Minimal Recursion Semantics (DMRS). We show that realizing chunks of a sentence and combining the results of such realizations increases the coverage for long sentences, significantly reduces the resources required and does not affect the quality of the realization.


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Resources for building applications with Dependency Minimal Recursion Semantics
Ann Copestake | Guy Emerson | Michael Wayne Goodman | Matic Horvat | Alexander Kuhnle | Ewa Muszyńska
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

We describe resources aimed at increasing the usability of the semantic representations utilized within the DELPH-IN (Deep Linguistic Processing with HPSG) consortium. We concentrate in particular on the Dependency Minimal Recursion Semantics (DMRS) formalism, a graph-based representation designed for compositional semantic representation with deep grammars. Our main focus is on English, and specifically English Resource Semantics (ERS) as used in the English Resource Grammar. We first give an introduction to ERS and DMRS and a brief overview of some existing resources and then describe in detail a new repository which has been developed to simplify the use of ERS/DMRS. We explain a number of operations on DMRS graphs which our repository supports, with sketches of the algorithms, and illustrate how these operations can be exploited in application building. We believe that this work will aid researchers to exploit the rich and effective but complex DELPH-IN resources.

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Graph- and surface-level sentence chunking
Ewa Muszyńska
Proceedings of the ACL 2016 Student Research Workshop

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A Proposition-Based Abstractive Summariser
Yimai Fang | Haoyue Zhu | Ewa Muszyńska | Alexander Kuhnle | Simone Teufel
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Abstractive summarisation is not yet common amongst today’s deployed and research systems. Most existing systems either extract sentences or compress individual sentences. In this paper, we present a summariser that works by a different paradigm. It is a further development of an existing summariser that has an incremental, proposition-based content selection process but lacks a natural language (NL) generator for the final output. Using an NL generator, we can now produce the summary text to directly reflect the selected propositions. Our evaluation compares textual quality of our system to the earlier preliminary output method, and also uses ROUGE to compare to various summarisers that use the traditional method of sentence extraction, followed by compression. Our results suggest that cutting out the middle-man of sentence extraction can lead to better abstractive summaries.