Bootstrapping of Semantic Relation Extraction for a Morphologically Rich Language: Semi-Supervised Learning of Semantic Relations

This article focuses on the use of a bootstrapping approach for the extraction of semantic relations that exist between two different concepts in a Tamil text. The proposed system, bootstrapping approach to semantic UNL relation extraction (BASURE) extracts generic relations that exist between different components of a sentence by exploiting the morphological richness of Tamil. Tamil is essentially a partially free word order language which means that semantic relations that exist between the concepts can occur anywhere in the sentence not necessarily in a fixed order. Here, the authors use Universal Networking Language (UNL), an Interlingua framework, to represent the word-based features and aim to define UNL semantic relations that exist between any two constituents in a sentence. The morphological suffix, lexical category and UNL semantic constraints associated with a word are defined as tuples of the pattern used for bootstrapping. Most systems define the initial set of seed patterns manually. However, this article uses a rule-based approach to obtain word-based features that form tuples of the patterns. A bootstrapping approach is then applied to extract all possible instances from the corpus and to generate new patterns. Here, the authors also introduce the use of UNL ontology to discover the semantic similarity between semantic tuples of the pattern, hence, to learn new patterns from the text corpus in an iterative manner. The use of UNL Ontology makes this approach general and domain independent. The results obtained are evaluated and compared with existing approaches and it has been shown that this approach is generic, can extract all sentence based semantic UNL relations and significantly increases the performance of the generic semantic relation extraction system.