We present a new approach to extraction of hypernyms based on projection learning and word embeddings. In contrast to classification-based approaches, projection-based methods require no candidate hyponym-hypernym pairs. While it is natural to use both positive and negative training examples in supervised relation extraction, the impact of negative examples on hypernym prediction was not studied so far. In this paper, we show that explicit negative examples used for regularization of the model significantly improve performance compared to the state-of-the-art approach of (2014) on three datasets from different languages.
for hypernymy extraction rely on sentences where both hyponym and hypernym co-occur in characteristic contexts, e.g., such as and ''. proposed to use hand-crafted lexical-syntactic patterns to extract hypernyms from such contexts. introduced a method for learning patterns automatically based on a set of seed hyponym-hypernym pairs. Further examples of path-based approaches include @cite_18 and @cite_27 . The inherent limitation of the path-based methods leading to sparsity issues is that hyponym and hypernym have to co-occur in the same sentence.
@cite_27 @cite_18
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Definition extraction is the task of automatically identifying definitional sentences within texts. The task has proven useful in many research areas including ontology learning, relation extraction and question answering. However, current approaches -- mostly focused on lexicosyntactic patterns -- suffer from both low recall and precision, as definitional sentences occur in highly variable syntactic structures. In this paper, we propose Word-Class Lattices (WCLs), a generalization of word lattices that we use to model textual definitions. Lattices are learned from a dataset of definitions from Wikipedia. Our method is applied to the task of definition and hypernym extraction and compares favorably to other pattern generalization methods proposed in the literature. We compare two different types of extraction patterns for automatically deriving semantic information from text: lexical patterns, built from words and word class information, and dependency patterns with syntactic information obtained from a full parser. We are particularly interested in whether the richer linguistic information provided by a parser allows for a better performance of subsequent information extraction work. We evaluate automatic extraction of hypernym information from text and conclude that the application of dependency patterns does not lead to substantially higher precision and recall scores than using lexical patterns.