The purpose of this research is to develop a methodology for the detection
and categorisation of named entities or proper names (PPNN), in the categories of
geographical place, person and organisation. The hypothesis is that the context of
occurrence of the entity –a context window of n words before the target– as well as
the components of the PN itself may provide good estimators of the type of PN. To
that end, we developed a supervised categorisation algorithm, with a training phase
in which the system receives a corpus already annotated by another NERC system.
In the case of these experiments, such system was the open-source suite of language
analysers FreeLing, annotating the corpus of the Spanish Wikipedia. During this
training phase, the system learns to associate the category of entity with words of
the context as well as those from the PN itself. We evaluate results with the CONLL-
2002 and also with a corpus of geopolitics from the journal Le Monde Diplomatique
in its Spanish edition, and compare the results with some well-known NERC systems
Web demo: http://www.tecling.com/pol
Comments within the same scripts are at the moment only in Spanish.
- config.pm: Configuration file. User must change its values before execution.
- poltrain.pl: Script used for training.
- pol.pl: Script used for the actual processing of new data.
- convertmodel.pl: Script used to convert the model produced by poltrain.pl to the model that pol.pl
needs to work.
To train POL for make a new model, you need have installed the Storable module for Perl.
Corpus and models: experiments have only been conducted in Spanish for the moment. Models for new languages will be added in the future.
- WikipediaFreeling.zip (2,6Gb !!!): This is the training corpus. A Spanish Wikipedia tagged with Freeling.
- Model.zip: An example model produced after training and conversion to use with pol.pl.
These models were created
with a x86_64 HP Proliant machine with GenuineIntel CPU 1064.000 MHz running Linux (Ubuntu 14.04). If you have a different kind of machine (e.g., with Windows), then you will probably need to create the models again by using poltrain.pl.
This research is supported by a grant from the Chilean
Government: Conicyt-Fondecyt 11140686, “Inducción
automática de taxonomías de sustantivos generales y especializados a partir de corpus textuales desde el enfoque de
la lingüística cuantitativa” (Automatic taxonomy induction from corpora for terminology and general vocabulary using quantitative measures). Lead researcher: Rogelio Nazar.
+ Nazar, R.; Arriagada, P. (2017). POL: un nuevo sistema para la detección y clasificación de nombres propios. Procesamiento del Lenguaje Natural, n. 58, pp. 13-20.
Related concepts: Named entities, proper names, text linguistics