Named entity recognition often fails in idiosyncratic domains. That causes a problem for depending tasks, such as entity linking and relation extraction. We propose a generic and robust approach for high-recall named entity recognition. Our approach is easy to train and offers strong generalization over diverse domain-specific language, such as news documents (e.g. Reuters) or biomedical text (e.g. Medline). Our approach is based on deep contextual sequence learning and utilizes stacked bidirectional LSTM networks. Our model is trained with only few hundred labeled sentences and does not rely on further external knowledge. We report from our results F1 scores in the range of 84-94 on standard datasets.
Named entity linking is the task to match textual mentions of named entities to a knowledge base @cite_9 . This task requires a set of candidate mentions from sentences. As a result, the recall from the underlying NER system constitutes an upper bound for entity linking accuracy @cite_16 . Moreover, show that state-of-the-art systems are substantially limited by low recall'' and don't perform well especially on idiosyncratic data while highlight that terms with high novelty or high specificity cannot efficiently be linked by current systems.
@cite_9 @cite_16
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Named Entity Linking (nel) grounds entity mentions to their corresponding node in a Knowledge Base (kb). Recently, a number of systems have been proposed for linking entity mentions in text to Wikipedia pages. Such systems typically search for candidate entities and then disambiguate them, returning either the best candidate or nil. However, comparison has focused on disambiguation accuracy, making it difficult to determine how search impacts performance. Furthermore, important approaches from the literature have not been systematically compared on standard data sets. We reimplement three seminal nel systems and present a detailed evaluation of search strategies. Our experiments find that coreference and acronym handling lead to substantial improvement, and search strategies account for much of the variation between systems. This is an interesting finding, because these aspects of the problem have often been neglected in the literature, which has focused largely on complex candidate ranking algorithms. Extracting named entities in text and linking extracted names to a given knowledge base are fundamental tasks in applications for text understanding. Existing systems typically run a named entity recognition (NER) model to extract entity names first, then run an entity linking model to link extracted names to a knowledge base. NER and linking models are usually trained separately, and the mutual dependency between the two tasks is ignored. We propose JERL, Joint Entity Recognition and Linking, to jointly model NER and linking tasks and capture the mutual dependency between them. It allows the information from each task to improve the performance of the other. To the best of our knowledge, JERL is the first model to jointly optimize NER and linking tasks together completely. In experiments on the CoNLL’03 AIDA data set, JERL outperforms state-of-art NER and linking systems, and we find improvements of 0.4 absolute F1 for NER on CoNLL’03, and 0.36 absolute precision@1 for linking on AIDA. Entity disambiguation, also known as entity linking, is the task of mapping mentions in text to the corresponding entities in a given knowledge base, e.g. Wikipedia. Two key challenges are making use of mention's context to disambiguate (i.e. local objective), and promoting coherence of all the linked entities (i.e. global objective). In this paper, we propose a deep neural network model to effectively measure the semantic matching between mention's context and target entity. We are the first to employ the long short-term memory (LSTM) and attention mechanism for entity disambiguation. We also propose Pair-Linking, a simple but effective and significantly fast linking algorithm. Pair-Linking iteratively identifies and resolves pairs of mentions, starting from the most confident pair. It finishes linking all mentions in a document by scanning the pairs of mentions at most once. Our neural network model combined with Pair-Linking, named NeuPL, outperforms state-of-the-art systems over different types of documents including news, RSS, and tweets. Entity Linking (EL) has received considerable attention in recent years. Given many name mentions in a document, the goal of EL is to predict their referent entities in a knowledge base. Traditionally, there have been two distinct directions of EL research: one focusing on the effects of mention's context compatibility, assuming that "the referent entity of a mention is reflected by its context"; the other dealing with the effects of document's topic coherence, assuming that "a mention's referent entity should be coherent with the document's main topics". In this paper, we propose a generative model -- called entity-topic model, to effectively join the above two complementary directions together. By jointly modeling and exploiting the context compatibility, the topic coherence and the correlation between them, our model can accurately link all mentions in a document using both the local information (including the words and the mentions in a document) and the global knowledge (including the topic knowledge, the entity context knowledge and the entity name knowledge). Experimental results demonstrate the effectiveness of the proposed model.