Голосование большинством

The Stanford-CS224N system is an ensemble of sim- ple classifiers. The first-tier systems are heteroge- neous, consisting primarily of naive-Bayes variants, but also including vector space, memory-based, and other classifier …...

This paper investigates the application of co- training and self-training to word sense disam- biguation. Optimal and empirical parameter se- lection methods for co-training and self-training are investigated, with various …...

This paper assesses the role of multi-label classification in modelling polysemy for lan- guage acquisition tasks. We focus on the ac- quisition of semantic classes for Catalan ad- jectives, and …...

In this paper we present our solution for ob- taining sentiment at message-level of short sentences. The system combines the use of polarity dictionaries and Conditional Random Fields to obtain …...

AbstractThe analysis of crowdsourced annotations in natural language processing is concerned with identifying (1) gold standard labels, (2) annotator accuracies and biases, and (3) item difficulties and error patterns. Traditionally, …...

AbstractThis paper describes the approach that was developed for SemEval 2021 Task 7 (Hahackathon: Incorporating Demographic Factors into Shared Humor Tasks) by the DUTH Team. We used and compared a …...

AbstractSuccessful Machine Learning based Named Entity Recognition models could fail on texts from some special domains, for instance, Chinese addresses and e-commerce titles, where requires adequate background knowledge. Such texts …...

AbstractIn conventional bilingual dictionary creation by using crowdsourcing, the main method is to ask multiple workers to translate the same words or sentences and take a majority vote. However, when …...

AbstractThis paper presents our participation to the “Human Value Detection shared task (Kiesel et al., 2023), as “Andronicus of Rhodes. We describe the approaches behind each entry in the official …...