Context-Guided Self-supervised Relation Embeddings

A semantic relation between two given words a and b can be represented using two complementary sources of information: (a) the semantic representations of a and b (expressed as word embeddings) and, (b) the contextual information obtained from the co-occurrence contexts of the two words (expressed in the form of lexico-syntactic patterns). Pattern-based approach suffers from sparsity while methods rely only on word embeddings for the related pairs lack of relational information. Prior works on relation embeddings have pre-dominantly focused on either one type of those two resources exclusively, except for a notable few exceptions. In this paper, we proposed a self-supervised context-guided Relation Embedding method (CGRE) using the two sources of information. We evaluate the learnt method to create relation representations for word-pairs that do not co-occur. Experimental results on SemEval-2012 task2 dataset show that the proposed operator outperforms other methods in representing relations for unobserved word-pairs

Knowledge Base Population using Semantic Label Propagation

A crucial aspect of a knowledge base population system that extracts new facts from text corpora, is the generation of training data for its relation extractors. In this paper, we present a method that maximizes the effectiveness of newly trained relation extractors at a minimal annotation cost. Manual labeling can be significantly reduced by Distant Supervision, which is a method to construct training data automatically by aligning a large text corpus with an existing knowledge base of known facts. For example, all sentences mentioning both 'Barack Obama' and 'US' may serve as positive training instances for the relation born_in(subject,object). However, distant supervision typically results in a highly noisy training set: many training sentences do not really express the intended relation. We propose to combine distant supervision with minimal manual supervision in a technique called feature labeling, to eliminate noise from the large and noisy initial training set, resulting in a significant increase of precision. We further improve on this approach by introducing the Semantic Label Propagation method, which uses the similarity between low-dimensional representations of candidate training instances, to extend the training set in order to increase recall while maintaining high precision. Our proposed strategy for generating training data is studied and evaluated on an established test collection designed for knowledge base population tasks. The experimental results show that the Semantic Label Propagation strategy leads to substantial performance gains when compared to existing approaches, while requiring an almost negligible manual annotation effort.Comment: Submitted to Knowledge Based Systems, special issue on Knowledge Bases for Natural Language Processin