Economic crisis and recovery: changes in second birth rates within occupational classes and educational groups

This study assesses the decline in second birth rates for men and women across different skill levels in transitional Russia. Changes within educational groups and occupational classes are observed over three distinct time periods: the Soviet era, economic crisis, and economic recovery. The most remarkable finding is the similarity in the extent second birth rates declined within educational groups and occupational classes during the economic crisis. Although further decline occurred in the recovery period, more variation emerged across groups.

Analysis and evaluation of techniques for the extraction of classes in the ontology learning process

This paper analyzes and evaluates, in the context of Ontology learning, some techniques to identify and extract candidate terms to classes of a taxonomy. Besides, this work points out some inconsistencies that may be occurring in the preprocessing of text corpus, and proposes techniques to obtain good terms candidate to classes of a taxonomy.

Using qualia information to identify lexical semantic classes in an unsupervised clustering task

Acquiring lexical information is a complex problem, typically approached by relying on a number of contexts to contribute information for classification. One of the first issues to address in this domain is the determination of such contexts. The work presented here proposes the use of automatically obtained FORMAL role descriptors as features used to draw nouns from the same lexical semantic class together in an unsupervised clustering task. We have dealt with three lexical semantic classes (HUMAN, LOCATION and EVENT) in English. The results obtained show that it is possible to discriminate between elements from different lexical semantic classes using only FORMAL role information, hence validating our initial hypothesis. Also, iterating our method accurately accounts for fine-grained distinctions within lexical classes, namely distinctions involving ambiguous expressions. Moreover, a filtering and bootstrapping strategy employed in extracting FORMAL role descriptors proved to minimize effects of sparse data and noise in our task.

Aprenentatge automàtic de classes sintàctiques verbals

Aquest treball descriu una metodologia per classificar els verbs en català segons el seu comportament sintàctic. L’objectiu és adquirir un nombre reduït de classes bàsiques amb una precisió alta fent servir pocs recursos. Obtenir informació sobre classe sintàctica és un procés llarg i costós, però útil per a moltes tasques de PLN. Presentem com obtenir aquesta informació fent servir només un corpus amb anotació de categoria morfològica. Hem explorat tant tècniques supervisades com no supervisades. Primer presentem els experiments que fan servir un mètode supervisat per distingir automàticament entre verbs transitius i intransitius. El nostre sistema té una taxa d’error del 4,65%. Pel que fa als mètodes no supervisats (clustering), presentem dos experiments. El primer pretén classificar els verbs en transitius, intransitius i verbs que alternen amb la partícula se. El segon experiment té per objectiu fer una subclassificació entre intransitius purs i preposicional. Els resultats són uns coeficients-F de 0.84 i 0.88, respectivament.