[PDF] Top 20 Classifying Relations for Biomedical Named Entity Disambiguation

... new disambiguation method that, instead of classifying each individual occurrence of an entity, it classifies pair-wise re- lations between the entity mention in question and the “cue words” ... See full document

... First, we noticed that in 31.5% of the true errors (41/130) our model selected an entity that can be understood as a specific (6.5%) or general (25%) realization of the correct solution. For example, instead of ... See full document

... The solution graph contains all possible candi- dates for each NE mention in the document. Each candidate has an initial confidence, with some connected by association relations. The disam- biguation phase ranks ... See full document

... In fact, besides being the more frequent one, after a deeper analysis of the papers, we consider ML the main method in this population. It is directly or indirectly used in the great majority of the articles. Even inside ... See full document

... in biomedical literature, a vast amount of new infor- mation and research results have been published and many of them are available in the electronic form - for example, like the PubMed MedLine ...most ... See full document

... new named entities that are unseen in the training ...known relations among en- tities within a graph ...the entity linking performance given ambiguous en- tities unseen in training ... See full document

... relationships. Named entity extraction (NEE) is a subtask of IE that aims to locate phrases (men- tions) in the text that represent names of persons, organizations, or locations regardless of their ...type. ... See full document

... successful entity disambiguation algorithm would benefit from both the initial similarity between can- didate and entity, as well as the coherence among entities in the same ...every entity ... See full document

... The dataset for each scenario is split into a train- ing dataset and a testing dataset which are dis- joint in terms of the query names used in their examples. For instance, if a query for the name John Williams is ... See full document

... Named Entity Recognition plays an important role in locating and classifying atomic elements into predefined categories such as person names, locations, organizations, expression of times, temporal ... See full document

... of named entities (NE) mentioned in text (such as people, or- ganizations, and geographic locations) plays an im- portant role in various natural language processing and information retrieval ...of Named ... See full document

... In the context of databases, traditional entity dis- ambiguation methods rely on similarity functions over attributes associated to the entities (de Car- valho et al., 2012). Obviously, such an approach is ... See full document

... For Named Entity Recognition, Maximum Entropy and CRFs (Laf- ferty, 2001) are mostly used, but other linear mod- els like Perceptron, Naive Bayes and linear SVMs are employed (Figure ... See full document

... Our phenotype name recognition system was extremely dependent on MetaMap and also made mistakes in case the head of an NP was among a list of empty heads. We proposed solutions to overcome these problems and improve the ... See full document

... Such a dictionary is essential for all further pro- cessing we do over YAGO3 to enrich the Arabic knowledge base using the English one. It is worth noting here, that this dictionary is completely au- tomatically ... See full document

... We also use a small test set within 6 test names, which is released by the Second CIPS- SIGHAN. The results in Table 3 show that the proposed method gives an average precision of 74.41%. However, the recall value is not ... See full document

... as disambiguation performance on unseen data, with the precision ranging between 90% and 99% for a handful of words with two distinct homograph senses, like, ... See full document

... multiple named entities, every rule should be written before it is actually ...the named entities to words even when the words are not listed in the dictionary and the context is not described in the rule ... See full document

... Named Entity Disambiguation (NED) sys- tems perform well on news articles and other texts covering a specific time inter- val. However, NED quality drops when inputs span long time periods like in ... See full document

... The purpose of PCE is to extract 〈person, category〉 tuples, in which category can be used as the fine-grained type of person, so the set of NE types can be expanded by automatically extracting from texts. When we extract ... See full document