Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Learning to resolve natural language ambiguities: a unified approach
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
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
Machine Learning for Information Extraction in Informal Domains
Machine Learning - Special issue on information retrieval
Deep Read: a reading comprehension system
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Probabilistic reasoning for entity & relation recognition
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Relational learning via propositional algorithms: an information extraction case study
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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The entity and relation recognition, i.e. (1) assigning semantic classes (e.g., person, organization and location) to entities in a sentence, and (2) determining the relations (e.g., born-in and employee-of) held between the corresponding entities, is an important task in areas such as information extraction and question answering. Subtasks (1) and (2) are typically carried out sequentially, and this procedure is problematic: errors made during subtask (1) are propagated to subtask (2) with an accumulative effect; and in many cases information that becomes available only during subtask (2) (e.g., the class of an entity corresponds to the first argument of relation born-in (X, China)) would be helpful for subtask (1) (e.g., the class of the entity cannot be a location but a person). To address problems of this kind, this paper develops a novel method, which allows subtasks (1) and (2) to be linked more closely together. The procedure is separated to three stages. Firstly, employ two classifiers to perform subtasks (1) and (2) independently. Secondly, the semantic class of each entity is determined by taking into account the classes of all the entities in the sentence, as computed during the previous step. This is achieved using a special model dubbed "entity relation propagation diagram" and "entity relation propagation tree". Thirdly, each relation is then assigned a class by considering the semantic classes of the entities produced at the previous step. Our experimental results show that the method improves not only relation recognition but also entity recognition in some degree.