2006 GraphBasedSemiSupervApproachForIE
- (Hassan et al., 2006) ⇒ Hany Hassan, Ahmed Hassan, Sara Noeman. (2006). “Graph Based Semi-Supervised Approach for Information Extraction.” In: Proceedings of the HLT/NAACL-2006 Workshop on Graph-based methods for NLP (TextGraph 2006).
Subject Headings: Relation Recognition from Text Algorithm, ACE Benchmark Task, Semi-Supervised Leaerning Algorithm, Text Graph.
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Abstract
Classification techniques deploy supervised labeled instances to train classifiers for various classification problems. However labeled instances are limited, expensive, and time consuming to obtain, due to the need of experienced human annotators. Meanwhile large amount of unlabeled data is usually easy to obtain. Semi-supervised learning addresses the problem of utilizing unlabeled data along with supervised labeled data, to build better classifiers. In this paper we introduce a semi-supervised approach based on mutual reinforcement in graphs to obtain more labeled data to enhance the classifier accuracy. The approach has been used to supplement a maximum entropy model for semi-supervised training of the ACE Relation Detection and Characterization (RDC) task. ACE RDC is considered a hard task in information extraction due to lack of large amounts of training data and inconsistencies in the available data. The proposed approach provides 10% relative improvement over the state of the art supervised baseline system.
Results and Discussion
We train several models like the one described in section 5.2 on different training data sets. In all experiments, we use both the LDC ACE training data and the labeled unsupervised data induced with the graph based approach we propose. We use the ACE evaluation procedure and ACE test corpus, provided by LDC, to evaluate all models.
We incrementally added labeled unsupervised data to the training data to determine the amount of data after which degradation in the system performance occurs. We sought this degradation point separately for each relation type. Figure 4 shows the effect of adding labeled unsupervised data on the ACE value for each relation separately. We notice from figure 4 and table 1 that relations with a small number of training instances had a higher gain in performance compared to relations with a large number of training instances. This implies that the proposed approach achieves significant improvement when the number of labeled training instances is small but representative.
References
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2006 GraphBasedSemiSupervApproachForIE | Hany Hassan Ahmed Hassan Sara Noeman | Graph Based Semi-Supervised Approach for Information Extraction | Proceedings of the HLT/NAACL-2006 Workshop on Graph-based methods for NLP | http://www.computing.dcu.ie/~hhasan/semi-hlt06.pdf | 2006 |