[PDF] Top 20 Cross Lingual Named Entity Recognition via Wikification

... For the low-resource languages, we compare our direct transfer model with the expectation learning model proposed in Zhang et al. (2016). This model is not a direct transfer model, but it does not use any training data ... See full document

... the entity types, namely, an answer is counted as correct only if the FreeBase ID and the entity type are both correct, we built two simple 5-class classifiers to classify each mention into the five ... See full document

... to cross-lingual NER (also classified as transductive transfer learning (Pan and Yang, 2010) and closely related to domain ...for cross-lingual dependency parser ...However, cross- ... See full document

... cludes cross-lingual transfer methods that ex- ploit resources from existing high-resource languages for zero-shot or few-shot ...nor cross- lingual transfer has been explored for Danish NER, ... See full document

... Low-resource named entity recognition is still an open problem in ...predict named entities for both high-resource languages and low-resource languages ... See full document

... multi- lingual and multi-domain LRs that were initially collected and annotated in different settings, and on our efforts for their successful harmonisation, so that they are rendered homogenous and, thus, ...of ... See full document

... and De Meulder, 2003). Scores for individual en- tity types are obtained by averaging results of 10- fold cross-validation on the full dataset. When splitting the data, document bounds are taken into account so ... See full document

... with 36 different kinds of biological entities, and with parts of speech. Previous NER work using this corpus has employed 10-fold cross-validation for evaluation. We wanted to explore different model variations ... See full document

... Cross-lingual entity linking (XEL) grounds named entities in a source language to an English Knowledge Base (KB), such as ...bilingual entity maps, multilingual embeddings) that are ... See full document

... We proposed doing joint learning of NER and EL, in order to improve their performance. Results show that our model achieves results competitive with the state-of-the-art. Moreover, we verified that the models trained ... See full document

... Abstract—Named entity recognition (NER) is a subtask of information extraction that seeks to locate and classify elements in text into predefined categories such as names of persons, locations, ... See full document

... Our study fills an empirical gap by considering historical datasets and performing careful compar- isons of multiple models under exactly the same conditions. We have investigated the relative perfor- mance of an BiLSTM ... See full document

... For classification, we have compared the proposed SVM-HMM with a CRF and a deep learning ap- proach based on the Jordan-RNN (Mesnil et al., 2013). For the SVM-HMM we have used structural SVM from (Joachims, 2008) with a ... See full document

... Wikipedia cross-lingual links to English Wikipedia, and of those English Wikipedia titles, 185,531 have entries in ...for cross- lingual ...Wikipedia cross-lingual ...Wikipedia ... See full document

... For our baseline NER system we use a Bi- LSTM architecture that takes word and charac- ter embeddings as input. The same architecture is used both for the source and the target languages to allow for transfer of weights ... See full document

... Interestingly, we see improvement over the baseline even with only 500 entries. This improve- ment continues until 125K entries. It is important to note that only a small number of dictionary en- tries – words that ... See full document

... obtaining cross-lingual em- beddings have been proposed in the ...inter- lingual representations, by training mappings to the common language, English, we are able to map words in different languages ... See full document

... However, cross-lingual transfer on compa- rable corpora is more difficult than on parallel cor- pora, due to the difficulty in finding high-quality word translation ... See full document

... Named Entity Recognition (NER) is an important NLP task that identifies the boundary and type of named entities ...model. Cross-lingual transfer models, which train on ... See full document

... There are naturally a number of ways this project could be replicated in a more sophisticated way to yield a yet more sophisticated understand- ing and therewith likely further gains in perfor- mance. For one, the ... See full document