DE eng

Search in the Catalogues and Directories

Page: 1 2 3 4 5...115
Hits 1 – 20 of 2.284

1
The Telegram Chronicles of Online Harm
In: Journal of Open Humanities Data; Vol 7 (2021); 8 ; 2059-481X (2021)
BASE
Show details
2
Competition, selection and communicative need in language change: an investigation using corpora, computational modelling and experimentation ...
Karjus, Andres. - : The University of Edinburgh, 2021
BASE
Show details
3
Natural Language Processing for Corpus Linguistics ...
Dunn, Jonathan. - : Code Ocean, 2021
BASE
Show details
4
Reference Corpus of Early New High German (1350–1650) ...
BASE
Show details
5
Reference Corpus of Early New High German (1350–1650) ...
BASE
Show details
6
Reference Corpus of Early New High German (1350–1650) ...
BASE
Show details
7
Reference Corpus of Early New High German (1350–1650) ...
BASE
Show details
8
Reference Corpus of Early New High German (1350–1650) ...
BASE
Show details
9
Analysis of an Extracted Discipline-Specific Computer Science Vocabulary List
BASE
Show details
10
Sujeito oculto às claras: uma abordagem descritivo-computacional / Omitted subjects revealed: a quantitative-descriptive approach
In: Revista de Estudos da Linguagem, Vol 29, Iss 2, Pp 1033-1058 (2021) (2021)
BASE
Show details
11
A Corpus Approach to Roman Law Based on Justinian’s Digest ...
Ribary, Marton; McGillivray, Barbara. - : Apollo - University of Cambridge Repository, 2020
BASE
Show details
12
The Quest for 'Falsehood', or a Survey of Tools for the Study of Greek-Syriac-Arabic Translations ...
BASE
Show details
13
The Quest for 'Falsehood', or a Survey of Tools for the Study of Greek-Syriac-Arabic Translations ...
BASE
Show details
14
Discovering and analysing lexical variation in social media text ...
Shoemark, Philippa Jane. - : The University of Edinburgh, 2020
BASE
Show details
15
A Corpus Approach to Roman Law Based on Justinian’s Digest
Ribary, Marton; McGillivray, Barbara. - : MDPI AG, 2020. : Informatics, 2020
BASE
Show details
16
Discovering and analysing lexical variation in social media text
Shoemark, Philippa Jane. - : The University of Edinburgh, 2020
BASE
Show details
17
Sentence Simplification for Text Processing
Evans, Richard. - : University of Wolverhampton, 2020
Abstract: A thesis submitted in partial fulfilment of the requirement of the University of Wolverhampton for the degree of Doctor of Philosophy. ; Propositional density and syntactic complexity are two features of sentences which affect the ability of humans and machines to process them effectively. In this thesis, I present a new approach to automatic sentence simplification which processes sentences containing compound clauses and complex noun phrases (NPs) and converts them into sequences of simple sentences which contain fewer of these constituents and have reduced per sentence propositional density and syntactic complexity. My overall approach is iterative and relies on both machine learning and handcrafted rules. It implements a small set of sentence transformation schemes, each of which takes one sentence containing compound clauses or complex NPs and converts it one or two simplified sentences containing fewer of these constituents (Chapter 5). The iterative algorithm applies the schemes repeatedly and is able to simplify sentences which contain arbitrary numbers of compound clauses and complex NPs. The transformation schemes rely on automatic detection of these constituents, which may take a variety of forms in input sentences. In the thesis, I present two new shallow syntactic analysis methods which facilitate the detection process. The first of these identifies various explicit signs of syntactic complexity in input sentences and classifies them according to their specific syntactic linking and bounding functions. I present the annotated resources used to train and evaluate this sign tagger (Chapter 2) and the machine learning method used to implement it (Chapter 3). The second syntactic analysis method exploits the sign tagger and identifies the spans of compound clauses and complex NPs in input sentences. In Chapter 4 of the thesis, I describe the development and evaluation of a machine learning approach performing this task. This chapter also presents a new annotated dataset supporting this activity. In the thesis, I present two implementations of my approach to sentence simplification. One of these exploits handcrafted rule activation patterns to detect different parts of input sentences which are relevant to the simplification process. The other implementation uses my machine learning method to identify compound clauses and complex NPs for this purpose. Intrinsic evaluation of the two implementations is presented in Chapter 6 together with a comparison of their performance with several baseline systems. The evaluation includes comparisons of system output with human-produced simplifications, automated estimations of the readability of system output, and surveys of human opinions on the grammaticality, accessibility, and meaning of automatically produced simplifications. Chapter 7 presents extrinsic evaluation of the sentence simplification method exploiting handcrafted rule activation patterns. The extrinsic evaluation involves three NLP tasks: multidocument summarisation, semantic role labelling, and information extraction. Finally, in Chapter 8, conclusions are drawn and directions for future research considered.
Keyword: computational linguistics; corpus annotation; extrinsic evaluation; natural language processing; sentence simplification; shallow syntactic analysis; text readability
URL: http://hdl.handle.net/2436/623377
BASE
Hide details
18
Automatic syntactic analysis of learner English ...
Huang, Yan. - : Apollo - University of Cambridge Repository, 2019
BASE
Show details
19
E-raamatute eeltöödeldud ja lemmatiseeritud failid ...
Uiboaed, Kristel. - : DataDOI, 2018
BASE
Show details
20
Detection of Longitudinal Development of Dementia in Literary Writing
In: http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1524651391474684 (2018)
BASE
Show details

Page: 1 2 3 4 5...115

Catalogues
0
0
0
0
0
0
0
Bibliographies
0
0
0
0
0
0
0
0
0
Linked Open Data catalogues
0
Online resources
0
0
0
0
Open access documents
2.284
0
0
0
0
© 2013 - 2024 Lin|gu|is|tik | Imprint | Privacy Policy | Datenschutzeinstellungen ändern