Semi-Supervised Learning for Word Sense Disambiguation: Quality vs. Quantity
نویسندگان
چکیده
In this paper, we discuss the importance of the quality against the quantity of automatically extracted examples for word sense disambiguation (WSD). We first show that we can build a competitive WSD system with a memory-based classifier and a feature set reduced to easily and efficiently computable features. We then show that adding automatically annotated examples improves the performance of this system when the examples are carefully selected based on their
منابع مشابه
Word Sense Disambiguation by Semi-supervised Learning
In this paper we propose to use a semi-supervised learning algorithm to deal with word sense disambiguation problem. We evaluated a semi-supervised learning algorithm, local and global consistency algorithm, on widely used benchmark corpus for word sense disambiguation. This algorithm yields encouraging experimental results. It achieves better performance than orthodox supervised learning algor...
متن کاملTheme: A Study of Classifier Combination and Semi-Supervised Learning for Word Sense Disambiguation
1. Aims Word Sense Disambiguation (WSD) involves the association of a polysemous word in a text or discourse with a particular sense among numerous potential senses of that word. In my thesis, we present a study of classifier combination and semi-supervised learning for WSD, which aim to boost supervised WSD and improve accuracy of WSD. In addition, we also work on context representation and fe...
متن کاملReview: Semi-Supervised Learning Methods for Word Sense Disambiguation
Word sense disambiguation (WSD) is an open problem of natural language processing, which governs the process of identifying the appropriate sense of a word in a sentence, when the word has multiple meanings. Many approaches have been proposed to solve the problem, of which supervised learning approaches are the most successful. However supervised machine learning are limited by the difficulties...
متن کاملSelf-training and co-training in biomedical word sense disambiguation
Word sense disambiguation (WSD) is an intermediate task within information retrieval and information extraction, attempting to select the proper sense of ambiguous words. Due to the scarcity of training data, semi-supervised learning, which profits from seed annotated examples and a large set of unlabeled data, are worth researching. We present preliminary results of two semi-supervised learnin...
متن کاملInvestigating Problems of Semi-supervised Learning for Word Sense Disambiguation
Word Sense Disambiguation (WSD) is the problem of determining the right sense of a polysemous word in a given context. In this paper, we will investigate the use of unlabeled data for WSD within the framework of semi supervised learning, in which the original labeled dataset is iteratively extended by exploiting unlabeled data. This paper addresses two problems occurring in this approach: deter...
متن کامل