06491 Abstracts Collection -- Digital Historical Corpora- Architecture, Annotation, and Retrieval

From 03.12.06 to 08.12.06, the Dagstuhl Seminar 06491 ``Digital Historical Corpora - Architecture, Annotation, and Retrieval\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if availabl

A Large-Scale Comparison of Historical Text Normalization Systems

There is no consensus on the state-of-the-art approach to historical text normalization. Many techniques have been proposed, including rule-based methods, distance metrics, character-based statistical machine translation, and neural encoder--decoder models, but studies have used different datasets, different evaluation methods, and have come to different conclusions. This paper presents the largest study of historical text normalization done so far. We critically survey the existing literature and report experiments on eight languages, comparing systems spanning all categories of proposed normalization techniques, analysing the effect of training data quantity, and using different evaluation methods. The datasets and scripts are made publicly available.Comment: Accepted at NAACL 201

Cross-Lingual and Cross-Chronological Information Access to Multilingual Historical Documents

In this chapter, we present our work in realizing information access across different languages and periods. Nowadays, digital collections of historical documents have to handle materials written in many different languages in different time periods. Even in a particular language, there are significant differences over time in terms of grammar, vocabulary and script. Our goal is to develop a method to access digital collections in a wide range of periods from ancient to modern. We introduce an information extraction method for digitized ancient Mongolian historical manuscripts for reducing labour-intensive analysis. The proposed method performs computerized analysis on Mongolian historical documents. Named entities such as personal names and place names are extracted by employing support vector machine. The extracted named entities are utilized to create a digital edition that reflects an ancient Mongolian historical manuscript written in traditional Mongolian script. The Text Encoding Initiative guidelines are adopted to encode the named entities, transcriptions and interpretations of ancient words. A web-based prototype system is developed for utilizing digital editions of ancient Mongolian historical manuscripts as scholarly tools. The proposed prototype has the capability to display and search traditional Mongolian text and its transliteration in Latin letters along with the highlighted named entities and the scanned images of the source manuscript

Computational analysis of medieval manuscripts: a new tool for analysis and mapping of medieval documents to modern orthography

Medieval manuscripts or other written documents from that period contain valuable information about people, religion, and politics of the medieval period, making the study of medieval documents a necessary pre-requisite to gaining in-depth knowledge of medieval history. Although tool-less study of such documents is possible and has been ongoing for centuries, much subtle information remains locked such manuscripts unless it gets revealed by effective means of computational analysis. Automatic analysis of medieval manuscripts is a non-trivial task mainly due to non-conforming styles, spelling peculiarities, or lack of relational structures (hyper-links), which could be used to answer meaningful queries. Natural Language Processing (NLP) tools and algorithms are used to carry out computational analysis of text data. However due to high percentage of spelling variations in medieval manuscripts, NLP tools and algorithms cannot be applied directly for computational analysis. If the spelling variations are mapped to standard dictionary words, then application of standard NLP tools and algorithms becomes possible. In this paper we describe a web-based software tool CAMM (Computational Analysis of Medieval Manuscripts) that maps medieval spelling variations to a modern German dictionary. Here we describe the steps taken to acquire, reformat, and analyze data, produce putative mappings as well as the steps taken to evaluate the findings. At the time of the writing of this paper, CAMM provides access to 11275 manuscripts organized into 54 collections containing a total of 242446 distinctly spelled words. CAMM accurately corrects spelling of 55% percent of the verifiable words.Thanks to Georg Vogeler for his valuable suggestions about the algorithms. Thanks also to Jochen Graf and the Monasterium consortium for having given us access to the medieval dataset and for sharing valuable information about the existing EditMOM tools. Thanks to the Athabasca University, for providing a server to launch this tool, and thanks to theWeb Unit of the Computing Services Department at Athabasca for keeping the link alive.http://www.jucs.org/;internal&action=noaction&Parameter=1208164030958am201

Annotation and representation of a diachronic corpus of Spanish

In this article we describe two different strategies for the automatic tagging of a Spanish diachronic corpus involving the adaptation of existing NLP tools developed for modern Spanish. In the initial approach we follow a state-of-the-art strategy, which consists on standardizing the spelling and the lexicon. This approach boosts POS-tagging accuracy to 90, which represents a raw improvement of over 20% with respect to the results obtained without any pre-processing. In order to enable non-expert users in NLP to use this new resource, the corpus has been integrated into IAC (Corpora Interface Access). We discuss the shortcomings of the initial approach and propose a new one, which does not consist in adapting the source texts to the tagger, but rather in modifying the tagger for the direct treatment of the old variants.This second strategy addresses some important shortcomings in the previous approach and is likely to be useful not only in the creation of diachronic linguistic resources but also for the treatment of dialectal or non-standard variants of synchronic languages as well.Peer ReviewedPostprint (published version

LocLinkVis: A Geographic Information Retrieval-Based System for Large-Scale Exploratory Search

In this paper we present LocLinkVis (Locate-Link-Visualize); a system which supports exploratory information access to a document collection based on geo-referencing and visualization. It uses a gazetteer which contains representations of places ranging from countries to buildings, and that is used to recognize toponyms, disambiguate them into places, and to visualize the resulting spatial footprints.Comment: SEM'1

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