Relation classification is an important research arena in the field of natural language processing (NLP). In this paper, we present SDP-LSTM, a novel neural network to classify the relation of two entities in a sentence. Our neural architecture leverages the shortest dependency path (SDP) between two entities; multichannel recurrent neural networks, with long short term memory (LSTM) units, pick up heterogeneous information along the SDP. Our proposed model has several distinct features: (1) The shortest dependency paths retain most relevant information (to relation classification), while eliminating irrelevant words in the sentence. (2) The multichannel LSTM networks allow effective information integration from heterogeneous sources over the dependency paths. (3) A customized dropout strategy regularizes the neural network to alleviate overfitting. We test our model on the SemEval 2010 relation classification task, and achieve an @math -score of 83.7 , higher than competing methods in the literature.
The World Wide Web contains a significant amount of information expressed using natural language. While unstructured text is often difficult for machines to understand, the field of Information Extraction (IE) offers a way to map textual content into a structured knowledge base. The ability to amass vast quantities of information from Web pages has the potential to increase the power with which a modern search engine can answer complex queries. IE has traditionally focused on acquiring knowledge about particular relationships within a small collection of domain-specific text. Typically, a target relation is provided to the system as input along with extraction patterns or examples that have been specified by hand. Shifting to a new relation requires a person to create new patterns or examples. This manual labor scales linearly with the number of relations of interest. The task of extracting information from the Web presents several challenges for existing IE systems. The Web is large and heterogeneous; the number of potentially interesting relations is massive and their identity often unknown. To enable large-scale knowledge acquisition from the Web, this thesis presents Open Information Extraction, a novel extraction paradigm that automatically discovers thousands of relations from unstructured text and readily scales to the size and diversity of the Web. We present a new approach to relation extraction that requires only a handful of training examples. Given a few pairs of named entities known to exhibit or not exhibit a particular relation, bags of sentences containing the pairs are extracted from the web. We extend an existing relation extraction method to handle this weaker form of supervision, and present experimental results demonstrating that our approach can reliably extract relations from web documents. Modern models of relation extraction for tasks like ACE are based on supervised learning of relations from small hand-labeled corpora. We investigate an alternative paradigm that does not require labeled corpora, avoiding the domain dependence of ACE-style algorithms, and allowing the use of corpora of any size. Our experiments use Freebase, a large semantic database of several thousand relations, to provide distant supervision. For each pair of entities that appears in some Freebase relation, we find all sentences containing those entities in a large unlabeled corpus and extract textual features to train a relation classifier. Our algorithm combines the advantages of supervised IE (combining 400,000 noisy pattern features in a probabilistic classifier) and unsupervised IE (extracting large numbers of relations from large corpora of any domain). Our model is able to extract 10,000 instances of 102 relations at a precision of 67.6 . We also analyze feature performance, showing that syntactic parse features are particularly helpful for relations that are ambiguous or lexically distant in their expression.