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

Spanish named entity recognition in the biomedical domain

Named Entity Recognition in the clinical domain and in languages different from English has the difficulty of the absence of complete dictionaries, the informality of texts, the polysemy of terms, the lack of accordance in the boundaries of an entity, the scarcity of corpora and of other resources available. We present a Named Entity Recognition method for poorly resourced languages. The method was tested with Spanish radiology reports and compared with a conditional random fields system.Peer ReviewedPostprint (author's final draft

Information Retrieval Systems Adapted to the Biomedical Domain

The terminology used in Biomedicine shows lexical peculiarities that have required the elaboration of terminological resources and information retrieval systems with specific functionalities. The main characteristics are the high rates of synonymy and homonymy, due to phenomena such as the proliferation of polysemic acronyms and their interaction with common language. Information retrieval systems in the biomedical domain use techniques oriented to the treatment of these lexical peculiarities. In this paper we review some of the techniques used in this domain, such as the application of Natural Language Processing (BioNLP), the incorporation of lexical-semantic resources, and the application of Named Entity Recognition (BioNER). Finally, we present the evaluation methods adopted to assess the suitability of these techniques for retrieving biomedical resources.Comment: 6 pages, 4 table

Text Summarization by Sentence Extraction and Syntactic Pruning

Nous présentons une méthode hybride pour le résumé de texte, en combinant l'extraction de phrases et l'élagage syntaxique des phrases extraites. L'élagage syntaxique est effectué sur la base d’une analyse complète des phrases selon un parseur de dépendances, analyse réalisée par la grammaire développée au sein d'un logiciel commercial de correction grammaticale, le Correcteur 101. Des sous-arbres de l'analyse syntaxique sont supprimés quand ils sont identifiés par les relations ciblées. L'analyse est réalisée sur un corpus de divers textes. Le taux de réduction des phrases extraites est d’en moyenne environ 74%, tout en conservant la grammaticalité ou la lisibilité dans une proportion de plus de 64%. Étant donné ces premiers résultats sur un ensemble limité de relations syntaxiques, cela laisse entrevoir des possibilités pour une application de résumé automatique de texte.CRSN

A Method for Proper Noun Extraction in Kurdish

This paper suggests a method for proper noun identification in Kurdish texts. Kurdish proper nouns are not capitalized and they also assume other part-of-speech roles, which leads to a broad ambiguity that should be addressed in Kurdish proper noun recognition applications. Kurdish is also among less-resourced languages. We developed an application based on an architecture which includes a number of name lists, a set of rules, and a set of processes that recognizes Kurdish person names. This can help the study of Information Retrieval (IR) in Kurdish to advance and can also be used in Kurdish machine translation. We conducted several experiments which showed that the precision of the method is more than 95%, the recall is between 40% to 80%, and the F-measure is close to 60% to more than 80%. The reason for the low recall precision was because our name lists were not exhaustive enough to cover the vast majority of the Kurdish names