Named Entity Resources - Overview and Outlook

Recognition of real-world entities is crucial for most NLP applications. Since its introduction some twenty years ago, named entity processing has undergone a significant evolution with, among others, the definition of new tasks (e.g. entity linking) and the emergence of new types of data (e.g. speech transcriptions, micro-blogging). These pose certainly new challenges which affect not only methods and algorithms but especially linguistic resources. Where do we stand with respect to named entity resources? This paper aims at providing a systematic overview of named entity resources, accounting for qualities such as multilingualism, dynamicity and interoperability, and to identify shortfalls in order to guide future developments

KIND: an Italian Multi-Domain Dataset for Named Entity Recognition

In this paper we present KIND, an Italian dataset for Named-entity recognition. It contains more than one million tokens with annotation covering three classes: person, location, and organization. The dataset (around 600K tokens) mostly contains manual gold annotations in three different domains (news, literature, and political discourses) and a semi-automatically annotated part. The multi-domain feature is the main strength of the present work, offering a resource which covers different styles and language uses, as well as the largest Italian NER dataset with manual gold annotations. It represents an important resource for the training of NER systems in Italian. Texts and annotations are freely downloadable from the Github repository

Old Content and Modern Tools : Searching Named Entities in a Finnish OCRed Historical Newspaper Collection 1771–1910

Named Entity Recognition (NER), search, classification and tagging of names and name-like informational elements in texts, has become a standard information extraction procedure for textual data. NER has been applied to many types of texts and different types of entities: newspapers, fiction, historical records, persons, locations, chemical compounds, protein families, animals etc. In general, the performance of a NER system is genre- and domain-dependent and also used entity categories vary [Nadeau and Sekine 2007]. The most general set of named entities is usually some version of a tripartite categorization of locations, persons, and organizations. In this paper we report trials and evaluation of NER with data from a digitized Finnish historical newspaper collection (Digi). Experiments, results, and discussion of this research serve development of the web collection of historical Finnish newspapers. Digi collection contains 1,960,921 pages of newspaper material from 1771–1910 in both Finnish and Swedish. We use only material of Finnish documents in our evaluation. The OCRed newspaper collection has lots of OCR errors; its estimated word level correctness is about 70–75 % [Kettunen and Pääkkönen 2016]. Our principal NE tagger is a rule-based tagger of Finnish, FiNER, provided by the FIN-CLARIN consortium. We also show results of limited category semantic tagging with tools of the Semantic Computing Research Group (SeCo) of the Aalto University. Three other tools are also evaluated briefly. This paper reports the first large scale results of NER in a historical Finnish OCRed newspaper collection. Results of this research supplement NER results of other languages with similar noisy data. As the results are also achieved with a small and morphologically rich language, they illuminate the relatively well-researched area of Named Entity Recognition from a new perspective.Peer reviewe