Tagging Named Entities in 19th Century and Modern Finnish Newspaper Material with a Finnish Semantic Tagger

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 during the last two decades. 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 a NER system’s performance is genre and domain dependent. Also used entity categories vary a lot (Nadeau and Sekine, 2007). The most general set of named entities is usually some version of three part categorization of locations, persons and corporations. In this paper we report evaluation results of NER with two different data: digitized Finnish historical newspaper collection Digi and modern Finnish technology news, Digitoday. Historical newspaper collection Digi contains 1,960,921 pages of newspaper material from years 1771–1910 both in 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%, and its NER evaluation collection consists of 75 931 words (Kettunen and Pääkkönen, 2016; Kettunen et al., 2016). Digitoday’s annotated collection consists of 240 articles in six different sections of the newspaper. Our new evaluated tool for NER tagging is non-conventional: it is a rule-based semantic tagger of Finnish, the FST (Löfberg et al., 2005), and its results are compared to those of a standard rule-based NE tagger, FiNER. The FST achieves up to 55–61 F-score with locations and F-score of 51–52 with persons with the historical newspaper data, and its performance is comparative to FiNER with locations. With the modern Finnish technology news of Digitoday FiNER achieves F-scores of up to 79 with locations at best. Person names show worst performance; their F-score varies from 33 to 66. The FST performs equally well as FiNER with Digitoday’s location names, but is worse with persons. With corporations, FST is at its worst, while FiNER performs reasonably well. Overall our results show that a general semantic tool like the FST is able to perform in a restricted semantic task of name recognition almost as well as a dedicated NE tagger. As NER is a popular task in information extraction and retrieval, our results show that NE tagging does not need to be only a task of dedicated NE taggers, but it can be performed equally well with more general multipurpose semantic tools.Peer reviewe

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

Modern Tools for Old Content - in Search of Named Entities in a Finnish OCRed Historical Newspaper Collection 1771-1910

Named entity recognition (NER), search, classification and tagging of names and name like frequent 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 a NER system’s performance is genre and domain dependent and also used entity categories vary [1]. The most general set of named entities is usually some version of three partite categorization of locations, persons and organizations. In this paper we report first trials and evaluation of NER with data out of a digitized Finnish historical newspaper collection Digi. Digi collection contains 1,960,921 pages of newspaper material from years 1771– 1910 both in 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 74–75 % [2]. Our principal NER tagger is a rule-based tagger of Finnish, FiNER, provided by the FIN-CLARIN consortium. We show also results of limited category semantic tagging with tools of the Semantic Computing Research Group (SeCo) of the Aalto University. FiNER is able to achieve up to 60.0 F-score with named entities in the evaluation data. Seco’s tools achieve 30.0–60.0 F-score with locations and persons. Performance of FiNER and SeCo’s tools with the data shows that at best about half of named entities can be recognized even in a quite erroneous OCRed textNamed entity recognition (NER), search, classification and tagging of names and name like frequent 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 a NER system’s performance is genre and domain dependent and also used entity categories vary [1]. The most general set of named entities is usually some version of three partite categorization of locations, persons and organizations. In this paper we report first trials and evaluation of NER with data out of a digitized Finnish historical newspaper collection Digi. Digi collection contains 1,960,921 pages of newspaper material from years 1771– 1910 both in 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 74–75 % [2]. Our principal NER tagger is a rule-based tagger of Finnish, FiNER, provided by the FIN-CLARIN consortium. We show also results of limited category semantic tagging with tools of the Semantic Computing Research Group (SeCo) of the Aalto University. FiNER is able to achieve up to 60.0 F-score with named entities in the evaluation data. Seco’s tools achieve 30.0–60.0 F-score with locations and persons. Performance of FiNER and SeCo’s tools with the data shows that at best about half of named entities can be recognized even in a quite erroneous OCRed text.Peer reviewe

Creating large semantic lexical resources for the Finnish language

Finnish belongs into the Finno-Ugric language family, and it is spoken by the vast majority of the people living in Finland. The motivation for this thesis is to contribute to the development of a semantic tagger for Finnish. This tool is a parallel of the English Semantic Tagger which has been developed at the University Centre for Computer Corpus Research on Language (UCREL) at Lancaster University since the beginning of the 1990s and which has over the years proven to be a very powerful tool in automatic semantic analysis of English spoken and written data. The English Semantic Tagger has various successful applications in the fields of natural language processing and corpus linguistics, and new application areas emerge all the time. The semantic lexical resources that I have created in this thesis provide the knowledge base for the Finnish Semantic Tagger. My main contributions are the lexical resources themselves, along with a set of methods and guidelines for their creation and expansion as a general language resource and as tailored for domain-specific applications. Furthermore, I propose and carry out several methods for evaluating semantic lexical resources. In addition to the English Semantic Tagger, which was developed first, and the Finnish Semantic Tagger second, equivalent semantic taggers have now been developed for Czech, Chinese, Dutch, French, Italian, Malay, Portuguese, Russian, Spanish, Urdu, and Welsh. All these semantic taggers taken together form a program framework called the UCREL Semantic Analysis System (USAS) which enables the development of not only monolingual but also various types of multilingual applications. Large-scale semantic lexical resources designed for Finnish using semantic fields as the organizing principle have not been attempted previously. Thus, the Finnish semantic lexicons created in this thesis are a unique and novel resource. The lexical coverage on the test corpora containing general modern standard Finnish, which has been the focus of the lexicon development, ranges from 94.58% to 97.91%. However, the results are also very promising in the analysis of domain-specific text (95.36%), older Finnish text (92.11–93.05%), and Internet discussions (91.97–94.14%). The results of the evaluation of lexical coverage are comparable to the results obtained with the English equivalents and thus indicate that the Finnish semantic lexical resources indeed cover the majority of core Finnish vocabulary

Named Entity Recognition for early-modern textual sources: a review of capabilities and challenges with strategies for the future

Purpose: By mapping-out the capabilities, challenges and limitations of named-entity recognition (NER), this article aims to synthesise the state of the art of NER in the context of the early modern research field and to inform discussions about the kind of resources, methods and directions that may be pursued to enrich the application of the technique going forward. // Design/methodology/approach: Through an extensive literature review, this article maps out the current capabilities, challenges and limitations of NER and establishes the state of the art of the technique in the context of the early modern, digitally augmented research field. It also presents a new case study of NER research undertaken by Enlightenment Architectures: Sir Hans Sloane's Catalogues of his Collections (2016–2021), a Leverhulme funded research project and collaboration between the British Museum and University College London, with contributing expertise from the British Library and the Natural History Museum. // Findings: Currently, it is not possible to benchmark the capabilities of NER as applied to documents of the early modern period. The authors also draw attention to the situated nature of authority files, and current conceptualisations of NER, leading them to the conclusion that more robust reporting and critical analysis of NER approaches and findings is required. // Research limitations/implications: This article examines NER as applied to early modern textual sources, which are mostly studied by Humanists. As addressed in this article, detailed reporting of NER processes and outcomes is not necessarily valued by the disciplines of the Humanities, with the result that it can be difficult to locate relevant data and metrics in project outputs. The authors have tried to mitigate this by contacting projects discussed in this paper directly, to further verify the details they report here. // Practical implications: The authors suggest that a forum is needed where tools are evaluated according to community standards. Within the wider NER community, the MUC and ConLL corpora are used for such experimental set-ups and are accompanied by a conference series, and may be seen as a useful model for this. The ultimate nature of such a forum must be discussed with the whole research community of the early modern domain. // Social implications: NER is an algorithmic intervention that transforms data according to certain rules-, patterns- or training data and ultimately affects how the authors interpret the results. The creation, use and promotion of algorithmic technologies like NER is not a neutral process, and neither is their output A more critical understanding of the role and impact of NER on early modern documents and research and focalization of some of the data- and human-centric aspects of NER routines that are currently overlooked are called for in this paper. // Originality/value: This article presents a state of the art snapshot of NER, its applications and potential, in the context of early modern research. It also seeks to inform discussions about the kinds of resources, methods and directions that may be pursued to enrich the application of NER going forward. It draws attention to the situated nature of authority files, and current conceptualisations of NER, and concludes that more robust reporting of NER approaches and findings are urgently required. The Appendix sets out a comprehensive summary of digital tools and resources surveyed in this article

Ensemble Named Entity Recognition (NER):Evaluating NER Tools in the Identification of Place Names in Historical Corpora

The field of Spatial Humanities has advanced substantially in the past years. The identification and extraction of toponyms and spatial information mentioned in historical text collections has allowed its use in innovative ways, making possible the application of spatial analysis and the mapping of these places with geographic information systems. For instance, automated place name identification is possible with Named Entity Recognition (NER) systems. Statistical NER methods based on supervised learning, in particular, are highly successful with modern datasets. However, there are still major challenges to address when dealing with historical corpora. These challenges include language changes over time, spelling variations, transliterations, OCR errors, and sources written in multiple languages among others. In this article, considering a task of place name recognition over two collections of historical correspondence, we report an evaluation of five NER systems and an approach that combines these through a voting system. We found that although individual performance of each NER system was corpus dependent, the ensemble combination was able to achieve consistent measures of precision and recall, outperforming the individual NER systems. In addition, the results showed that these NER systems are not strongly dependent on preprocessing and translation to Modern English

LL(O)D and NLP perspectives on semantic change for humanities research

CC BY 4.0This paper presents an overview of the LL(O)D and NLP methods, tools and data for detecting and representing semantic change, with its main application in humanities research. The paper’s aim is to provide the starting point for the construction of a workflow and set of multilingual diachronic ontologies within the humanities use case of the COST Action Nexus Linguarum, European network for Web-centred linguistic data science, CA18209. The survey focuses on the essential aspects needed to understand the current trends and to build applications in this area of study