[PDF] Top 20 Monitoring named entity recognition: the League Table

... Table 1 gives an overview of the first results in the League Table. All results are sorted according to the F-measure that has been determined through the align- ment of the annotated corpus against ... See full document

... The second batch of documents was a group of sentences in which a sentence context is not suffi- cient to designate a NE, so we need to know more about the particular NE, e.g. we need to look for its co-references in the ... See full document

... A solution to the cluster granularity problem proposed in many papers (e.g. Miller et al. (2004)) is the combination of multiple granularities, for example through hierarchical clustering. Surpris- ingly, for training on ... See full document

... results in Table 5 show significant improvements for the CoNLL dataset but performance drops for GermEval. Combining contemporary sources with historic target corpora yields to consistent benefits. Performance on ... See full document

... Results shown in Table 5 for four tags are not good as compared to other tags because these tags need more time to collect accurate data. If we consider accuracy of all the thirteen tags then accuracy is 74.09%. ... See full document

... each entity class as well as for the overall corpus in Table ...this table, Density (f2v) and Density (w2v) rep- resent the density criteria when using feature and word vectors, ...the table, ... See full document

... Learning hierarchical mentions explicitly using our model (HNNER) achieves better performance than using a set of projected subcategories inde- pendently, (HNNER+ SUB ) in Table 4. The pro- posed approach is still ... See full document

... Figure 4 describes our architecture to generate a word embedding for a word from its characters. A character lookup table initialized at random contains an embedding for every character. The character embeddings ... See full document

... During the writing of the paper we discovered a mistake in the experimental setup: We had included the names from the full corpus (the training and test data), instead of just the training data. This leaks information ... See full document

... Turkish. Table 3 presents the initial performance evaluation results of EMM’s NER system for Turkish while Table 4 presents the corresponding results after the system is modified with the below extensions, ... See full document

... We choose a basic discrete CRF model as our baseline tagger. As shown in Figure 3(a), discrete word features are first extracted as binary vectors (black and white circles) and then fed into a CRF layer. Taking those ... See full document

... of named entities in a text and classify- ing them with predefined types such as person, lo- cation and ...of Table 1, in which the mention “Leeds” can refer to “Leeds”, the city, and “Leeds United ...which ... See full document

... task, with the CoNLL score generally stricter than MUC7 (Nadeau and Sekine, 2007). As shown in Table 2, the scores achieved by the SVM-HMM are higher overall and for all classes but one, with the Jordan-RNN as the ... See full document

... nested named enti- ties of the same ...each named entity in a ...nested entity detection with linear time ...flat named entity recognition (Lample et ... See full document

... Different languages contain complementary cues about entities, which can be used to im- prove Named Entity Recognition (NER) sys- tems. We propose a method that formu- lates the problem of exploring ... See full document

... Statistical models have proved to be quite ef- fective. Such models typically treat named-entity recognition as a sequence tagging problem, where each word is tagged with its entity type if it ... See full document

... and named entities jointly resulted in improved per- formance on both. When looking at these numbers, it is important to keep in mind that the sizes of the training and test sets are significantly smaller than the ... See full document

... In Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and Proceedings of the 17th International Conference on Compu- tational Linguistics, pages 848-[r] ... See full document

... The crawler is a web-bot or spider which browses the web in an automated manner. It starts with a list of Uniform Resource Locators (URL) that it is to visit, called the seeds. As the crawler visits these URL’s it ... See full document

... general named entity ...of named entity recognition systems is against publicly available corpora, for instance the GENIA corpus, focusing on genes, proteins and there ...algorithmic ... See full document