Word Sense Disambiguation Based on Graph and Knowledge Base

Author(s):  
Fanqing Meng
2009 ◽  
Vol 35 (2) ◽  
pp. 151-184 ◽  
Author(s):  
Tom O'Hara ◽  
Janyce Wiebe

This article describes how semantic role resources can be exploited for preposition disambiguation. The main resources include the semantic role annotations provided by the Penn Treebank and FrameNet tagged corpora. The resources also include the assertions contained in the Factotum knowledge base, as well as information from Cyc and Conceptual Graphs. A common inventory is derived from these in support of definition analysis, which is the motivation for this work. The disambiguation concentrates on relations indicated by prepositional phrases, and is framed as word-sense disambiguation for the preposition in question. A new type of feature for word-sense disambiguation is introduced, using WordNet hypernyms as collocations rather than just words. Various experiments over the Penn Treebank and FrameNet data are presented, including prepositions classified separately versus together, and illustrating the effects of filtering. Similar experimentation is done over the Factotum data, including a method for inferring likely preposition usage from corpora, as knowledge bases do not generally indicate how relationships are expressed in English (in contrast to the explicit annotations on this in the Penn Treebank and FrameNet). Other experiments are included with the FrameNet data mapped into the common relation inventory developed for definition analysis, illustrating how preposition disambiguation might be applied in lexical acquisition.


2021 ◽  
pp. 174-186
Author(s):  
Mozhgan Saeidi ◽  
Evangelos Milios ◽  
Norbert Zeh

Author(s):  
Edoardo Barba ◽  
Luigi Procopio ◽  
Niccolò Campolungo ◽  
Tommaso Pasini ◽  
Roberto Navigli

The knowledge acquisition bottleneck strongly affects the creation of multilingual sense-annotated data, hence limiting the power of supervised systems when applied to multilingual Word Sense Disambiguation. In this paper, we propose a semi-supervised approach based upon a novel label propagation scheme, which, by jointly leveraging contextualized word embeddings and the multilingual information enclosed in a knowledge base, projects sense labels from a high-resource language, i.e., English, to lower-resourced ones. Backed by several experiments, we provide empirical evidence that our automatically created datasets are of a higher quality than those generated by other competitors and lead a supervised model to achieve state-of-the-art performances in all multilingual Word Sense Disambiguation tasks. We make our datasets available for research purposes at https://github.com/SapienzaNLP/mulan.


2012 ◽  
Vol 23 (4) ◽  
pp. 776-785 ◽  
Author(s):  
Zhi-Zhuo YANG ◽  
He-Yan HUANG

Author(s):  
Manuel Ladron de Guevara ◽  
Christopher George ◽  
Akshat Gupta ◽  
Daragh Byrne ◽  
Ramesh Krishnamurti

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