We introduce several ideas that improve the performance of supervised information extraction systems with a pipeline architecture, when they are customized for new domains. We show that: (a) a combination of a sequence tagger with a rule-based approach for entity mention extraction yields better performance for both entity and relation mention extraction; (b) improving the identification of syntactic heads of entity mentions helps relation extraction; and (c) a deterministic inference engine captures some of the joint domain structure, even when introduced as a postprocessing step to a pipeline system. All in all, our contributions yield a 20 % relative increase in F1 score in a domain significantly different from the domains used during the development of our information extraction system. 1

We introduce several ideas that improve the performance of supervised information extraction systems with a pipeline architecture, when they are customized for new domains. We show that: (a) a combination of a sequence tagger with a rule-based approach for entity mention extraction yields better performance for both entity and relation mention extraction; (b) improving the identification of syntactic heads of entity mentions helps relation extraction; and (c) a deterministic inference engine captures some of the joint domain structure, even when introduced as a postprocessing step to a pipeline system. All in all, our contributions yield a 20 % relative increase in F1 score in a domain significantly different from the domains used during the development of our information extraction system. 1

We consider the problem of NER in Arabic Wikipedia, a semi-supervised domain adaptation setting for which we have no labeled training data in the target domain. To facilitate evaluation, we obtain annotations for articles in four topical groups, allowing annotators to identify domain-specific entity types in addition to standard categories. Standard supervised learning on newswire text leads to poor target-domain recall. We train a sequence model and show that a simple modification to the online learner—a loss function encouraging it to “arrogantly ” favor recall over precision—substantially improves recall and F1. We then employ self-training on unlabeled target-domain data in order to adapt our model; enforcing the same recall-oriented bias in the self-training stage yields additional gains. 1

We introduce several ideas that improve the performance of supervised information extraction systems with a pipeline architecture, when they are customized for new domains. We show that: (a) a combination of a sequence tagger with a rule-based approach for entity mention extraction yields better performance for both entity and relation mention extraction; (b) improving the identification of syntactic heads of entity mentions helps relation extraction; and (c) a deterministic inference engine captures some of the joint domain structure, even when introduced as a postprocessing step to a pipeline system. All in all, our contributions yield a 20 % relative increase in F1 score in a domain significantly different from the domains used during the development of our information extraction system. 1

We propose a general cross-domain bootstrapping algorithm for domain adaptation in the task of named entity recognition. We first generalize the lexical features of the source domain model with word clusters generated from a joint corpus. We then select target domain instances based on multiple criteria during the bootstrapping process. Without using annotated data from the target domain and without explicitly encoding any target-domainspecific knowledge, we were able to improve the source model’s F-measure by 7 points on the target domain.

We consider the problem of NER in Arabic Wikipedia, a semisupervised domain adaptation setting for which we have no labeled training data in the target domain. To facilitate evaluation, we obtain annotations for articles in four topical groups, allowing annotators to identify domain-specific entity types in addition to standard categories. Standard supervised learning on newswire text leads to poor target-domain recall. We train a sequence model and show that a simple modification to the online learner—a loss function encouraging it to “arrogantly ” favor recall over precision— substantially improves recall and F1. We then adapt our model with self-training on unlabeled target-domain data; enforcing the same recall-oriented bias in the selftraining stage yields marginal gains. 1 1