1 |
Pluricentric languages : automatic identification and linguistic variation ... : Plurizentrische Sprachen : automatische Spracherkennung und linguistische Variation ...
|
|
|
|
BASE
|
|
Show details
|
|
2 |
Digital Humanities, Computational Linguistics, And Natural Language Processing ...
|
|
|
|
BASE
|
|
Show details
|
|
3 |
Digital Humanities, Computational Linguistics, And Natural Language Processing ...
|
|
|
|
BASE
|
|
Show details
|
|
4 |
Language model driven analysis : simplifying text on an individual scale ... : Benutzerzentrierte Modelle - Versuch unbekannte Wörter zu finden ...
|
|
|
|
BASE
|
|
Show details
|
|
5 |
Data Cleaning for XML Electronic Dictionaries via Statistical Anomaly Detection ...
|
|
|
|
BASE
|
|
Show details
|
|
6 |
Pluricentric languages : automatic identification and linguistic variation ; Plurizentrische Sprachen : automatische Spracherkennung und linguistische Variation
|
|
|
|
BASE
|
|
Show details
|
|
7 |
DOCREP: Document Representation for Natural Language Processing
|
|
Dawborn, Timothy James. - : The University of Sydney, 2016. : Faculty of Engineering and Information Technologies, School of Information Technologies, 2016
|
|
BASE
|
|
Show details
|
|
8 |
Evaluating Parsers with Dependency Constraints
|
|
Ng, Dominick. - : The University of Sydney, 2016. : Faculty of Engineering and Information Technologies, School of Information Technologies, 2016
|
|
BASE
|
|
Show details
|
|
9 |
Data Cleaning for XML Electronic Dictionaries via Statistical Anomaly Detection
|
|
|
|
BASE
|
|
Show details
|
|
10 |
Compiling Specialised Comparable Corpora. Should we always trust (Semi-)automatic Compilation Tools?
|
|
|
|
In: Linguamática, Vol 8, Iss 1 (2016) (2016)
|
|
Abstract:
Decisions at the outset of compiling a comparable corpus are of crucial importance for how the corpus is to be built and analysed later on. Several variables and external criteria are usually followed when building a corpus but little is been said about textual distributional similarity in this context and the quality that it brings to research. In an attempt to fulfil this gap, this paper aims at presenting a simple but efficient methodology capable of measuring a corpus internal degree of relatedness. To do so, this methodology takes advantage of both available natural language processing technology and statistical methods in a successful attempt to access the relatedness degree between documents. Our findings prove that using a list of common entities and a set of distributional similarity measures is enough not only to describe and assess the degree of relatedness between the documents in a comparable corpus, but also to rank them according to their degree of relatedness within the corpus.
|
|
Keyword:
comparable corpora; computational linguistics; distributional similarity measures; Language and Literature; manual and semi-automatic compilation; natural language processing; P; P1-1091; Philology. Linguistics
|
|
URL: https://doaj.org/article/9e342607089a4defb60e6004f4fbeaf5
|
|
BASE
|
|
Hide details
|
|
|
|