Relation Extraction with Relation Topics
نویسندگان
چکیده
This paper describes a novel approach to the semantic relation detection problem. Instead of relying only on the training instances for a new relation, we leverage the knowledge learned from previously trained relation detectors. Specifically, we detect a new semantic relation by projecting the new relation’s training instances onto a lower dimension topic space constructed from existing relation detectors through a three step process. First, we construct a large relation repository of more than 7,000 relations from Wikipedia. Second, we construct a set of non-redundant relation topics defined at multiple scales from the relation repository to characterize the existing relations. Similar to the topics defined over words, each relation topic is an interpretable multinomial distribution over the existing relations. Third, we integrate the relation topics in a kernel function, and use it together with SVM to construct detectors for new relations. The experimental results on Wikipedia and ACE data have confirmed that backgroundknowledge-based topics generated from the Wikipedia relation repository can significantly improve the performance over the state-of-theart relation detection approaches.
منابع مشابه
Support Vector Machine with Ensemble Tree Kernel for Relation Extraction
Relation extraction is one of the important research topics in the field of information extraction research. To solve the problem of semantic variation in traditional semisupervised relation extraction algorithm, this paper proposes a novel semisupervised relation extraction algorithm based on ensemble learning (LXRE). The new algorithm mainly uses two kinds of support vector machine classifier...
متن کاملExtraction of Semantic Relationships from Academic Papers using Syntactic Patterns
Integrating concept and citation networks on a specific research subject can help researchers focus their own work or use methods described in prior works. In this paper, we propose a method to extract semantic relations from concepts and citation in the descriptions of related work. Specifically, we examined (i) topic-paper relations between research topics and reference papers and (ii) method...
متن کاملماهیت حقوقی رابطه پزشک و بیمار و سبب ایجاد آن
A glimpse on the writings that have addressed the relation between the physician and the patient shows that how the physician’s responsibility has appropriated most topics. Whenever the stage before it i.e. the physician’s obligations towards the patient were noticed, the looks have been focusing on the very obligation and its subject that ultimately leads to the responsibility. ...
متن کاملJargon-Term Extraction by Chunking
NLP definitions of Terminology are usually application-dependent. IR terms are noun sequences that characterize topics. Terms can also be arguments for relations like abbreviation, definition or IS-A. In contrast, this paper explores techniques for extracting terms fitting a broader definition: noun sequences specific to topics and not well-known to naive adults. We describe a chunkingbased app...
متن کاملFinding Relevant Relations in Relevant Documents
This work studies the combination of a document retrieval and a relation extraction system for the purpose of identifying query-relevant relational facts. On the TREC Web collection, we assess extracted facts separately for correctness and relevance. Despite some TREC topics not being covered by the relation schema, we find that this approach reveals relevant facts, and in particular those not ...
متن کامل