Learning Information Extraction Rules for Semi-Structured and Free Text
Machine Learning - Special issue on natural language learning
Relational learning of pattern-match rules for information extraction
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Mining Association Rules in Multiple Relations
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Message Understanding Conference-6: a brief history
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
GATE: an architecture for development of robust HLT applications
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Introduction to the CoNLL-2003 shared task: language-independent named entity recognition
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Top-down induction of first-order logical decision trees
Artificial Intelligence
Towards efficient named-entity rule induction for customizability
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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Developing linguistically sound and data-compliant rules for named entity annotation is usually an intensive and time consuming process for any developer or linguist. In this work, we present the use of two Inductive Logic Programming (ILP) techniques to construct rules for extracting instances of various named entity classes thereby reducing the efforts of a linguist/developer. Using ILP for rule development not only reduces the amount of effort required but also provides an interactive framework wherein a linguist can incorporate his intuition about named entities such as in form of mode declarations for refinements (suitably exposed for ease of use by the linguist) and the background knowledge (in the form of linguistic resources). We have a small amount of tagged data - approximately 3884 sentences for Marathi and 22748 sentences in Hindi. The paucity of tagged data for Indian languages makes manual development of rules more challenging, However, the ability to fold in background knowledge and domain expertise in ILP techniques comes to our rescue and we have been able to develop rules that are mostly linguistically sound that yield results comparable to rules handcrafted by linguists. The ILP approach has two advantages over the approach of hand-crafting all rules: (i) the development time reduces by a factor of 240 when ILP is used instead of involving a linguist for the entire rule development and (ii) the ILP technique has the computational edge that it has a complete and consistent view of all significant patterns in the data at the level of abstraction specified through the mode declarations. The point (ii) enables the discovery of rules that could be missed by the linguist and also makes it possible to scale the rule development to a larger training dataset. The rules thus developed could be optionally edited by linguistic experts and consolidated either (a) through default ordering (as in TILDE[1]) or (b) with an ordering induced using [2] or (c) by using the rules as features in a statistical graphical model such a conditional random field (CRF) [3]. We report results using WARMR [4] and TILDE to learn rules for named entities of Indian languages namely Hindi and Marathi.