Deep Learning for Period Classification of Historical Texts

In this study, we address the interesting task of classifying historical texts by their assumed period of writing. This task is useful in digital humanity studies where many texts have unidentified publication dates. For years, the typical approach for temporal text classification was supervised using machine-learning algorithms. These algorithms require careful feature engineering and considerable domain expertise to design a feature extractor to transform the raw text into a feature vector from which the classifier could learn to classify any unseen valid input. Recently, deep learning has produced extremely promising results for various tasks in natural language processing (NLP). The primary advantage of deep learning is that human engineers did not design the feature layers, but the features were extrapolated from data with a general-purpose learning procedure. We investigated deep learning models for period classification of historical texts. We compared three common models: paragraph vectors, convolutional neural networks (CNN), and recurrent neural networks (RNN). We demonstrate that the CNN and RNN models outperformed the paragraph vector model and supervised machine-learning algorithms. In addition, we constructed word embeddings for each time period and analyzed semantic changes of word meanings over time

CLARIN. The infrastructure for language resources

CLARIN, the "Common Language Resources and Technology Infrastructure", has established itself as a major player in the field of research infrastructures for the humanities. This volume provides a comprehensive overview of the organization, its members, its goals and its functioning, as well as of the tools and resources hosted by the infrastructure. The many contributors representing various fields, from computer science to law to psychology, analyse a wide range of topics, such as the technology behind the CLARIN infrastructure, the use of CLARIN resources in diverse research projects, the achievements of selected national CLARIN consortia, and the challenges that CLARIN has faced and will face in the future. The book will be published in 2022, 10 years after the establishment of CLARIN as a European Research Infrastructure Consortium by the European Commission (Decision 2012/136/EU)

CLARIN

The book provides a comprehensive overview of the Common Language Resources and Technology Infrastructure – CLARIN – for the humanities. It covers a broad range of CLARIN language resources and services, its underlying technological infrastructure, the achievements of national consortia, and challenges that CLARIN will tackle in the future. The book is published 10 years after establishing CLARIN as an Europ. Research Infrastructure Consortium

CLARIN

The book provides a comprehensive overview of the Common Language Resources and Technology Infrastructure – CLARIN – for the humanities. It covers a broad range of CLARIN language resources and services, its underlying technological infrastructure, the achievements of national consortia, and challenges that CLARIN will tackle in the future. The book is published 10 years after establishing CLARIN as an Europ. Research Infrastructure Consortium

Trawling and trolling for terrorists in the digital Gulf of Bothnia : Cross-lingual text mining for the emergence of terrorism in Swedish and Finnish newspapers, 1780—1926

In pursuing the historical emergence of the discourse on terrorism, this study trawls the “digital Gulf of Bothnia” in the form of a corpus of combined Swedish and Finnish digitized newspaper texts. Through a cross-lingual exploration of the uses of the concept of terrorism in historical Swedish and Finnish news, we examine meanings anchored in the two culturally close but still decidedly different national political contexts. The study is an outcome of an integrative interdisciplinary effort.Peer reviewe

Extended Overview of HIPE-2022: Named Entity Recognition and Linking in Multilingual Historical Documents

This paper presents an overview of the second edition of HIPE (Identifying Historical People, Places and other Entities), a shared task on named entity recognition and linking in multilingual historical documents. Following the success of the first CLEF-HIPE-2020 evaluation lab, HIPE-2022 confronts systems with the challenges of dealing with more languages, learning domain-specific entities, and adapting to diverse annotation tag sets. This shared task is part of the ongoing efforts of the natural language processing and digital humanities communities to adapt and develop appropriate technologies to efficiently retrieve and explore information from historical texts. On such material, however, named entity processing techniques face the challenges of domain heterogeneity, input noisiness, dynamics of language, and lack of resources. In this context, the main objective of HIPE-2022, run as an evaluation lab of the CLEF 2022 conference, is to gain new insights into the transferability of named entity processing approaches across languages, time periods, document types, and annotation tag sets. Tasks, corpora, and results of participating teams are presented. Compared to the condensed overview [1], this paper contains more refined statistics on the datasets, a break down of the results per type of entity, and a discussion of the ‘challenges’ proposed in the shared task

Analysing Finnish Multi-Word Expressions with Word Embeddings

Sanayhdistelmät ovat useamman sanan kombinaatioita, jotka ovat jollakin tavalla jähmeitä ja/tai idiomaattisia. Tutkimuksessa tarkastellaan suomen kielen verbaalisia idiomeja sanaupotusmenetelmän (word2vec) avulla. Työn aineistona käytetään Gutenberg-projektista haettuja suomenkielisiä kirjoja. Työssä tutkitaan pääosin erityisesti idiomeja, joissa esiintyy suomen kielen sana ‘silmä’. Niiden idiomaattisuutta mitataan komposiittisuuden (kuinka hyvin sanayhdistelmän merkitys vastaa sen komponenttien merkitysten kombinaatiota) ja jähmeyttä leksikaalisen korvaustestin avulla. Vastaavat testit tehdään myös sanojen sisäisen rakenteen huomioonottavan fastText-algoritmin avulla. Työssä on myös luotu Gutenberg-korpuksen perusteella pienehkö luokiteltu lausejoukko, jota lajitellaan neuroverkkopohjaisen luokittelijan avulla. Tämä lisäksi työssä tunnustellaan eri ominaisuuksien kuten sijamuodon vaikutusta idiomin merkitykseen. Mittausmenetelmien tulokset ovat yleisesti ottaen varsin kirjavia. fastText-algoritmin suorituskyky on yleisesti ottaen hieman parempi kuin perusmenetelmän; sen lisäksi sanaupotusten laatu on parempi. Leksikaalinen korvaustesti antaa parhaimmat tulokset, kun vain lähin naapuri otetaan huomioon. Sijamuodon todettiin olevan varsin tärkeä idiomin merkityksen määrittämiseen. Mittauksien heikot tulokset voivat johtua monesta tekijästä, kuten siitä, että idiomien semanttisen läpinäkyvyyden aste voi vaihdella. Sanaupotusmenetelmä ei myöskään normaalisti ota huomioon sitä, että myös sanayhdistelmillä voi olla useita merkityksiä (kirjaimellinen ja idiomaattinen/kuvaannollinen). Suomen kielen rikas morfologia asettaa menetelmälle myös ylimääräisiä haasteita. Tuloksena voidaan sanoa, että sanaupotusmenetelmä on jokseenkin hyödyllinen suomen kielen idiomien tutkimiseen. Testattujen mittausmenetelmien käyttökelpoisuus yksin käytettynä on rajallinen, mutta ne saattaisivat toimia paremmin osana laajempaa tutkimusmekanismia

A Survey of Corpora for Germanic Low-Resource Languages and Dialects

Despite much progress in recent years, the vast majority of work in natural language processing (NLP) is on standard languages with many speakers. In this work, we instead focus on low-resource languages and in particular non-standardized low-resource languages. Even within branches of major language families, often considered well-researched, little is known about the extent and type of available resources and what the major NLP challenges are for these language varieties. The first step to address this situation is a systematic survey of available corpora (most importantly, annotated corpora, which are particularly valuable for NLP research). Focusing on Germanic low-resource language varieties, we provide such a survey in this paper. Except for geolocation (origin of speaker or document), we find that manually annotated linguistic resources are sparse and, if they exist, mostly cover morphosyntax. Despite this lack of resources, we observe that interest in this area is increasing: there is active development and a growing research community. To facilitate research, we make our overview of over 80 corpora publicly available

A Survey of Corpora for Germanic Low-Resource Languages and Dialects

Despite much progress in recent years, the vast majority of work in natural language processing (NLP) is on standard languages with many speakers. In this work, we instead focus on low-resource languages and in particular non-standardized low-resource languages. Even within branches of major language families, often considered well-researched, little is known about the extent and type of available resources and what the major NLP challenges are for these language varieties. The first step to address this situation is a systematic survey of available corpora (most importantly, annotated corpora, which are particularly valuable for NLP research). Focusing on Germanic low-resource language varieties, we provide such a survey in this paper. Except for geolocation (origin of speaker or document), we find that manually annotated linguistic resources are sparse and, if they exist, mostly cover morphosyntax. Despite this lack of resources, we observe that interest in this area is increasing: there is active development and a growing research community. To facilitate research, we make our overview of over 80 corpora publicly available. We share a companion website of this overview at https://github.com/mainlp/germanic-lrl-corpora .Comment: NoDaLiDa 202