Turkish Wikipedia Named-Entity Recognition and Text Categorization (TWNERTC) dataset is a collection of automatically categorized and annotated sentences obtained from Wikipedia. We constructed large-scale gazetteers by using a graph crawler algorithm to extract relevant entity and domain information from a semantic knowledge base, Freebase. The constructed gazetteers contains approximately 300K entities with thousands of fine-grained entity types under 77 different domains. Since automated processes are prone to ambiguity, we also introduce two new content specific noise reduction methodologies. Moreover, we map fine-grained entity types to the equivalent four coarse-grained types: person, loc, org, misc. Eventually, we construct six different dataset versions and evaluate the quality of annotations by comparing ground truths from human annotators. We make these datasets publicly available to support studies on Turkish named-entity recognition (NER) and text categorization (TC).
As research on NER has been pushing the limits of automated systems performing named-entity recognition, the need for annotated datasets and benchmarks is also increasing. Knowledge bases are important for NLP researches, since they provide a structured schema of topics that can be used to annotate entities with fine-grained types and or categorize raw texts into related domains. @cite_24 published benchmark evaluations that compare three datasets that use semantic information from KBs: DBpedia Spotlight @cite_12 , KORE50 @cite_8 @cite_19 and the Wikilinks Corpus @cite_9 . These datasets are in English and constructed with the aim of evaluating the performance of NER systems. The authors present the statistics of each dataset and baseline performances of various algorithms. There are other methodologies which leverages KBs to named entity extraction and linking; however, most of them are not available to public @cite_8 @cite_16 .
@cite_8 @cite_9 @cite_24 @cite_19 @cite_16 @cite_12
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We automatically create enormous, free and multilingual silver-standard training annotations for named entity recognition (ner) by exploiting the text and structure of Wikipedia. Most ner systems rely on statistical models of annotated data to identify and classify names of people, locations and organisations in text. This dependence on expensive annotation is the knowledge bottleneck our work overcomes. We first classify each Wikipedia article into named entity (ne) types, training and evaluating on 7200 manually-labelled Wikipedia articles across nine languages. Our cross-lingual approach achieves up to 95 accuracy. We transform the links between articles into ne annotations by projecting the target [email protected]?s classifications onto the anchor text. This approach yields reasonable annotations, but does not immediately compete with existing gold-standard data. By inferring additional links and heuristically tweaking the Wikipedia corpora, we better align our automatic annotations to gold standards. We annotate millions of words in nine languages, evaluating English, German, Spanish, Dutch and Russian Wikipedia-trained models against conll shared task data and other gold-standard corpora. Our approach outperforms other approaches to automatic ne annotation (Richman and Schone, 2008 [61], , 2008 [46]) competes with gold-standard training when tested on an evaluation corpus from a different source; and performs 10 better than newswire-trained models on manually-annotated Wikipedia text. Most state-of-the-art named entity recognition (NER) systems rely on handcrafted features and on the output of other NLP tasks such as part-of-speech (POS) tagging and text chunking. In this work we propose a language-independent NER system that uses automatically learned features only. Our approach is based on the CharWNN deep neural network, which uses word-level and character-level representations (embeddings) to perform sequential classification. We perform an extensive number of experiments using two annotated corpora in two different languages: HAREM I corpus, which contains texts in Portuguese; and the SPA CoNLL-2002 corpus, which contains texts in Spanish. Our experimental results shade light on the contribution of neural character embeddings for NER. Moreover, we demonstrate that the same neural network which has been successfully applied to POS tagging can also achieve state-of-the-art results for language-independet NER, using the same hyperparameters, and without any handcrafted features. For the HAREM I corpus, CharWNN outperforms the state-of-the-art system by 7.9 points in the F1-score for the total scenario (ten NE classes), and by 7.2 points in the F1 for the selective scenario (five NE classes). As developers of a highly multilingual named entity recognition (NER) system, we face an evaluation resource bottleneck problem: we need evaluation data in many languages, the annotation should not be too time-consuming, and the evaluation results across languages should be comparable. We solve the problem by automatically annotating the English version of a multi-parallel corpus and by projecting the annotations into all the other language versions. For the translation of English entities, we use a phrase-based statistical machine translation system as well as a lookup of known names from a multilingual name database. For the projection, we incrementally apply different methods: perfect string matching, perfect consonant signature matching and edit distance similarity. The resulting annotated parallel corpus will be made available for reuse. 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.