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Relation Extraction |
Relation extraction plays an important role in extracting structured information from unstructured sources such as raw text. One may want to find interactions between drugs to build a medical database, understand the scenes in images, or extract relationships among people to build an easily searchable knowledge base.
For example, let's assume we are interested in marriage relationships. We want to automatically figure out that "Michelle Obama" is the wife of "Barack Obama" from a corpus of raw text snippets such as "Barack Obama married Michelle Obama in...". A naive approach would be to search news articles for indicative phrases, like "married" or "XXX's spouse". This would yield some results, but human language is inherently ambiguous, and one cannot possibly come up with all phrases that indicate a marriage relationship. A natural next step would be to use machine learning techniques to extract the relations. If we have some labeled training data, such as examples of pairs of people that are in a marriage relationship, we could train a machine learning classifier to automatically learn the patterns for us. This sounds like a great idea, but there are several challenges:
- How do we disambiguate between words that refer to the same entity? For example, a sentence may refer to "Barack Obama" as "Barack" or "The president".
- How do we get training data for our machine learning model?
- How do we deal with conflicting or uncertain data?
Before starting to extract relations, it is a good idea to determine which words refer to the same "object" in the real world. These objects are called entities. For example, "Barack", "Obama" or "the president" may refer to the entity "Barack Obama". Let's say we extract relations about one of the words above. It would be helpful to combine them as being information about the same person. Figuring out which words, or mentions, refer to the same entity is a process called entity linking. There are various techniques to perform entity linking, ranging from simple string matching to more sophisticated machine learning approaches. In some domains we have a database of all known entities to link against, such as a dictionary of all countries. In other domains, we need to be open to discovering new entities.
Given enough training data, we can use machine learning algorithms to extract entities and relations we care about. There is one problem left: human language is inherently noisy. Words and phrases can be ambiguous, sentences are often ungrammatical, and spelling mistakes are frequent. Our training data may have errors in it as well, and we may have made mistakes in the entity linking step. This is where many machine learning approaches break down: they treat training or input data as "correct" and make predictions using this assumption.
DeepDive makes good use of uncertainty to improve predictions during the probabilistic inference step. For example, DeepDive may figure out that a certain mention of "Barack" is only 60% likely to actually refer to "Barack Obama", and use this fact to discount the impact of that mention on the final result for the entity "Barack Obama". DeepDive can also make use of domain knowledge and allow users to encode rules such as "If Barack is married to Michelle, then Michelle is married to Barack" to improve the predictions.