A person-name parser involves the identification of constituent parts of a person's name. Due to multiple writing styles ("John Smith" versus "Smith, John"), extra information ("John Smith, PhD", "Rev. John Smith"), and country-specific last-name prefixes ("Jean van de Velde"), parsing fullname strings from user profiles on Web 2.0 applications is not straightforward. To the best of our knowledge, we are the first to address this problem systematically by proposing machine learning approaches for parsing noisy fullname strings.

For big data practitioners, data integration/entity resolution/record linkage is one of the key challenges we face from day to day. Entity resolution/record linkage with high precision and recall on a large graph with billions of nodes, and hundreds of times more edges poses significant scalability challenges. Similarity based graph partition is still the most scalable method available. This paper presents a probabilistic method to approximate the match likelihood of a pair of records by incorporating values of different attributes and their aggregates/statistics. The quality of the approximates depend on the accuracy of the estimates of the aggregated values. The paper adapts the GTM model described in [1] to obtain the estimates. We present experimental results based on real world commercial data sources to show that the estimates obtained via GTM model is better than the baseline. Our experimental results also showed that the approximate match likelihood can improve the recall of the similarity function.

Record linkage is the task of identifying which records in one or more data collections refer to the same entity, and address is one of the most commonly used fields in databases. Hence, segmentation of the raw addresses into a set of semantic fields is the primary step in this task. In this paper, we present a probabilistic address parsing system based on the Hidden Markov Model. We also introduce several novel approaches to build models for noisy real-world addresses, obtaining 95.6% F-measure. Furthermore, we demonstrate the viability and efficiency of this system for large-scale data by scaling it up to parse billions of addresses with Hadoop.

Relation extraction is the process of identifying instances of specified types of semantic relations in text; relation type extension involves extending a relation extraction system to recognize a new type of relation. We present LGCo-Testing, an active learning system for relation type extension based on local and global views of relation instances. Locally, we extract features from the sentence that contains the instance. Globally, we measure the distributional similarity between instances from a 2 billion token corpus. Evaluation on the ACE 2004 corpus shows that LGCo-Testing can reduce annotation cost by 97% while maintaining the performance level of supervised learning.