Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Exploring various knowledge in relation extraction
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Beyond NomBank: a study of implicit arguments for nominal predicates
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Exploiting background knowledge for relation extraction
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Social relation extraction from texts using a support-vector-machine-based dependency trigram kernel
Information Processing and Management: an International Journal
| Hi-index | 0.00 |
Relation extraction in documents allows the detection of how entities being discussed in a document are related to one another (e.g. part-of). This paper presents an analysis of a relation extraction system based on prior work but applied to the J. D. Power and Associates Sentiment Corpus to examine how the system works on documents from a range of social media. The results are examined on three different subsets of the JDPA Corpus, showing that the system performs much worse on documents from certain sources. The proposed explanation is that the features used are more appropriate to text with strong editorial standards than the informal writing style of blogs.