How do they combine a deep learning model with a knowledge base?
Entities from a deep learning model are linked to the related entities from a knowledge base by a lookup.
Fine-grained Named Entity Recognition is a task whereby we detect and classify entity mentions to a large set of types. These types can span diverse domains such as finance, healthcare, and politics. We observe that when the type set spans several domains the accuracy of the entity detection becomes a limitation for supervised learning models. The primary reason being the lack of datasets where entity boundaries are properly annotated, whilst covering a large spectrum of entity types. Furthermore, many named entity systems suffer when considering the categorization of fine grained entity types. Our work attempts to address these issues, in part, by combining state-of-the-art deep learning models (ELMo) with an expansive knowledge base (Wikidata). Using our framework, we cross-validate our model on the 112 fine-grained entity types based on the hierarchy given from the Wiki(gold) dataset.
Named entity recognition (NER) BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 is the process by which we identify text spans which mention named entities, and to classify them into predefined categories such as person, location, organization etc. NER serves as the basis for a variety of natural language processing (NLP) applications such as relation extraction BIBREF4 , machine translation BIBREF5 , question answering BIBREF6 and knowledge base construction BIBREF7 . Although early NER systems have been successful in producing adequate recognition accuracy, they often require significant human effort in carefully designing rules or features. In recent years, deep learning methods been employed in NER systems, yielding state-of-the-art performance. However, the number of types detected are still not sufficient for certain domain-specific applications. For relation extraction, identifying fine-grained types has been shown to significantly increase the performance of the extractor BIBREF8 , BIBREF9 since this helps in filtering out candidate relation types which do not follow this type constraint. Furthermore, for question answering fine-grained Named Entity Recognition (FgNER) can provide additional information helping to match questions to its potential answers thus improving performance BIBREF10 . For example, Li and Roth BIBREF11 rank questions based on their expected answer types (i.e. will the answer be food, vehicle or disease). Typically, FgNER systems use over a hundred labels, arranged in a hierarchical structure. We find that available training data for FgNER typically contain noisy labels, and creating manually annotated training data for FgNER is a time-consuming process. Furthermore, human annotators will have to assign a subset of correct labels from hundreds of possible labels making this a somewhat arduous task. Currently, FgNER systems use distant supervision BIBREF12 to automatically generate training data. Distant supervision is a technique which maps each entity in the corpus to knowledge bases such as Freebase BIBREF13 , DBpedia BIBREF14 , YAGO BIBREF15 and helps with the generation of labeled data. This method will assign the same set of labels to all mentions of a particular entity in the corpus. For example, “Barack Obama” is a person, politician, lawyer, and author. If a knowledge base has these four matching labels, the distant supervision technique will assign all of them to every mention of “Barack Obama”. Therefore, the training data will also fail to distinguish between mentions of “Barack Obama” in all subsequent utterances. Ling et al. ling2012fine proposed the first system for FgNER, where they used 112 overlapping labels with a linear classifier perceptron for multi-label classification. Yosef et al. spaniol2012hyena used multiple binary SVM classifiers to assign entities to a set of 505 types. Gillick et al. gillick2014context introduced context dependent FgNER and proposed a set of heuristics for pruning labels that might not be relevant given the local context of the entity. Yogatama et al. yogatama2015embedding proposed an embedding based model where user-defined features and labels were embedded into a low dimensional feature space to facilitate information sharing among labels. Shimaoka et al. shimaoka2016attentive proposed an attentive neural network model which used long short-term memory (LSTMs) to encode the context of the entity, then used an attention mechanism to allow the model to focus on relevant expressions in the entity mention's context. To learn entity representations, we propose a scheme which is potentially more generalizable.