Semi-Supervised Information Extraction Algorithm

A Semi-Supervised Information Extraction Algorithm is a supervised IE algorithm that is a semi-supervised algorithm (that can solve a semi-supervised IE task).

• AKA: Semi-Supervised IE Algorithm.
• Example(s):
  • a Bootstrapped IE Algorithm.
  • a Self-Training IE Algorithm.
  • a Distant-Learning IE Algorithm.
• See: Information Extraction Algorithm, Fully-Supervised IE Algorithm, Semi-Supervised Entity Extraction Algorithm, Semi-Supervised Relation Extraction Algorithm.

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References

2009

• (Liang et al., 2009) ⇒ Percy Liang, Michael I. Jordan, and Dan Klein. (2009). “Learning Semantic Correspondences with Less Supervision.” In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP (ACL 2009).
• (Bellare & McCallum, 2009) ⇒ Kedar Bellare, and Andrew McCallum. (2009). “Generalized Expectation Criteria for Bootstrapping Extractors using Record-Text Alignment..” In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing (EMNLP 2009).

2007

• (Chang et al., 2007) ⇒ Ming-Wei Chang, Lev Ratinov, and Dan Roth. (2007). “Guiding Semi-Supervision with Constraint-Driven Learning.” In: Proceedings of the Annual Meeting of the ACL (ACL 2007).
  • CITED BY ~51 http://scholar.google.com/scholar?q=%22Guiding+semi-supervision+with+constraint-driven+learning%22+2007
  • ABSTRACT: Over the last few years, two of the main research directions in machine learning of natural language processing have been the study of semi-supervised learning algorithms as a way to train classifiers when the labeled data is scarce, and the study of ways to exploit knowledge and global information in structured learning tasks. In this paper, we suggest a method for incorporating domain knowledge in semi-supervised learning algorithms. Our novel framework unifies and can exploit several kinds of task specific constraints. The experimental results presented in the information extraction domain demonstrate that applying constraints helps the model to generate better feedback during learning, and hence the framework allows for high performance learning with significantly less training data than was possible before on these tasks.
• (Snyder & Barzilay, 2007) ⇒ Benjamin Snyder, and Regina Barzilay. (2007). “Database-text Alignment via Structured Multi-label Classification.” In: Proceedings of the 20th international joint conference on Artifical intelligence (IJCAI 2007).
  • CITED BY ~16 http://scholar.google.com/scholar?q=%22Database-text+alignment+via+structured+multilabel+classification%22+2007
  • ABSTRACT: This paper addresses the task of aligning a database with a corresponding text. The goal is to link individual database entries with sentences that verbalize the same information. By providing explicit semantics-to-text links, these alignments can aid the training of natural language generation and information extraction systems. Beyond these pragmatic benefits, the alignment problem is appealing from a modeling perspective: the mappings between database entries and text sentences exhibit rich structural dependencies, unique to this task. Thus, the key challenge is to make use of as many global dependencies as possible without sacrificing tractability. To this end, we cast text-database alignment as a structured multilabel classification task where each sentence is labeled with a subset of matching database entries. In contrast to existing multilabel classifiers, our approach operates over arbitrary global features of inputs and proposed labels. We compare our model with a baseline classifier that makes locally optimal decisions. Our results show that the proposed model yields a 15% relative reduction in error, and compares favorably with human performance.

2004

• (Cohen & Sarawagi, 2004) ⇒ William W. Cohen, and Sunita Sarawagi. (2004). “Exploiting Dictionaries in Named Entity Extraction: Combining semi-Markov extraction processes and data integration methods.” In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2004). doi:10.1145/1014052.1014065

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