Tracing Shifting Conceptual Vocabularies Through Time

This paper presents work in progress on an algorithm to track and identify changes in the vocabulary used to describe particular concepts over time, with emphasis on treating concepts as distinct from changes in word meaning. We apply the algorithm to word vectors generated from Google Books n-grams from 1800-1990 and evaluate the induced networks with respect to their flexibility (robustness to changes in vocabulary) and stability (they should not leap from topic to topic). We also describe work in progress using the British National Biography Linked Open Data Serials to construct a “ground truth” evaluation dataset for algorithms which aim to detect shifts in the vocabulary used to describe concepts. Finally, we discuss limitations of the proposed method, ways in which the method could be improved in the future, and other considerations.Cambridge Centre for Digital Knowledge, University of Cambridg

The Pico Project: Looking ahead

Text mining methods are examined and assessed in order to find exact sources of Pico's theses ascribed to Medieval authors in his "Conclusiones nongentae"

Using word vector models to trace conceptual change over time and space in historical newspapers, 1840–1914

Linking large digitized newspaper corpora in different languages that have become available in national and state libraries opens up new possibilities for the computational analysis of patterns of information flow across national and linguistic boundaries. The significant contribution this article presents is to demonstrate how word vector models can be used to explore the way concepts have shifted in meaning over time, as they migrated across space, by comparing newspapers from different countries published between 1840 and 1914. We define a concept, rather pragmatically, as a key term or core idea that has been used in historical discourse: an abstraction or mental representation that has served as a building block for thoughts and beliefs. We use historical newspapers in English, Finnish, German and Swedish from collections in the UK, US, Germany, and Finland, as well as the Europeana collection. As use cases, we analyze how the different conceptual constructs of “nation” and “illness” emerged and changed between 1840 and 1920. Conceptual change over time is simulated by creating a series of overlapping word vector models, each spanning ten years. Historical vocabularies are retrieved on the basis of vector space proximity. Conceptual change across space is simulated by comparing the historical change of vocabularies in newspaper collections from different nations in several languages. This computational approach to conceptual history opens up new ways to identify patterns in public discourse over longer periods of time and across borders.</p

Using word vector models to trace conceptual change over time and space in historical newspapers, 1840–1914

Linking large digitized newspaper corpora in different languages that have become available in national and state libraries opens up new possibilities for the computational analysis of patterns of information flow across national and linguistic boundaries. The significant contribution this article presents is to demonstrate how word vector models can be used to explore the way concepts have shifted in meaning over time, as they migrated across space, by comparing newspapers from different countries published between 1840 and 1914. We define a concept, rather pragmatically, as a key term or core idea that has been used in historical discourse: an abstraction or mental representation that has served as a building block for thoughts and beliefs. We use historical newspapers in English, Finnish, German and Swedish from collections in the UK, US, Germany, and Finland, as well as the Europeana collection. As use cases, we analyze how the different conceptual constructs of “nation” and “illness” emerged and changed between 1840 and 1920. Conceptual change over time is simulated by creating a series of overlapping word vector models, each spanning ten years. Historical vocabularies are retrieved on the basis of vector space proximity. Conceptual change across space is simulated by comparing the historical change of vocabularies in newspaper collections from different nations in several languages. This computational approach to conceptual history opens up new ways to identify patterns in public discourse over longer periods of time and across borders

Computational approaches to semantic change

Semantic change&nbsp;— how the meanings of words change over time&nbsp;— has preoccupied scholars since well before modern linguistics emerged in the late 19th and early 20th century, ushering in a new methodological turn in the study of language change. Compared to changes in sound and grammar, semantic change is the least&nbsp; understood. Ever since, the study of semantic change has progressed steadily, accumulating a vast store of knowledge for over a century, encompassing many languages and language families. Historical linguists also early on realized the potential of computers as research tools, with papers at the very first international conferences in computational linguistics in the 1960s. Such computational studies still tended to be small-scale, method-oriented, and qualitative. However, recent years have witnessed a sea-change in this regard. Big-data empirical quantitative investigations are now coming to the forefront, enabled by enormous advances in storage capability and processing power. Diachronic corpora have grown beyond imagination, defying exploration by traditional manual qualitative methods, and language technology has become increasingly data-driven and semantics-oriented. These developments present a golden opportunity for the empirical study of semantic change over both long and short time spans. A major challenge presently is to integrate the hard-earned&nbsp; knowledge and expertise of traditional historical linguistics with&nbsp; cutting-edge methodology explored primarily in computational linguistics. The idea for the present volume came out of a concrete response to this challenge.&nbsp; The 1st International Workshop on Computational Approaches to Historical Language Change (LChange'19), at ACL 2019, brought together scholars from both fields. This volume offers a survey of this exciting new direction in the study of semantic change, a discussion of the many remaining challenges that we face in pursuing it, and considerably updated and extended versions of a selection of the contributions to the LChange'19 workshop, addressing both more theoretical problems —&nbsp; e.g., discovery of "laws of semantic change"&nbsp;— and practical applications, such as information retrieval in longitudinal text archives

Computational approaches to semantic change

Semantic change&nbsp;— how the meanings of words change over time&nbsp;— has preoccupied scholars since well before modern linguistics emerged in the late 19th and early 20th century, ushering in a new methodological turn in the study of language change. Compared to changes in sound and grammar, semantic change is the least&nbsp; understood. Ever since, the study of semantic change has progressed steadily, accumulating a vast store of knowledge for over a century, encompassing many languages and language families. Historical linguists also early on realized the potential of computers as research tools, with papers at the very first international conferences in computational linguistics in the 1960s. Such computational studies still tended to be small-scale, method-oriented, and qualitative. However, recent years have witnessed a sea-change in this regard. Big-data empirical quantitative investigations are now coming to the forefront, enabled by enormous advances in storage capability and processing power. Diachronic corpora have grown beyond imagination, defying exploration by traditional manual qualitative methods, and language technology has become increasingly data-driven and semantics-oriented. These developments present a golden opportunity for the empirical study of semantic change over both long and short time spans. A major challenge presently is to integrate the hard-earned&nbsp; knowledge and expertise of traditional historical linguistics with&nbsp; cutting-edge methodology explored primarily in computational linguistics. The idea for the present volume came out of a concrete response to this challenge.&nbsp; The 1st International Workshop on Computational Approaches to Historical Language Change (LChange'19), at ACL 2019, brought together scholars from both fields. This volume offers a survey of this exciting new direction in the study of semantic change, a discussion of the many remaining challenges that we face in pursuing it, and considerably updated and extended versions of a selection of the contributions to the LChange'19 workshop, addressing both more theoretical problems —&nbsp; e.g., discovery of "laws of semantic change"&nbsp;— and practical applications, such as information retrieval in longitudinal text archives

Computational approaches to semantic change

Semantic change&nbsp;— how the meanings of words change over time&nbsp;— has preoccupied scholars since well before modern linguistics emerged in the late 19th and early 20th century, ushering in a new methodological turn in the study of language change. Compared to changes in sound and grammar, semantic change is the least&nbsp; understood. Ever since, the study of semantic change has progressed steadily, accumulating a vast store of knowledge for over a century, encompassing many languages and language families. Historical linguists also early on realized the potential of computers as research tools, with papers at the very first international conferences in computational linguistics in the 1960s. Such computational studies still tended to be small-scale, method-oriented, and qualitative. However, recent years have witnessed a sea-change in this regard. Big-data empirical quantitative investigations are now coming to the forefront, enabled by enormous advances in storage capability and processing power. Diachronic corpora have grown beyond imagination, defying exploration by traditional manual qualitative methods, and language technology has become increasingly data-driven and semantics-oriented. These developments present a golden opportunity for the empirical study of semantic change over both long and short time spans. A major challenge presently is to integrate the hard-earned&nbsp; knowledge and expertise of traditional historical linguistics with&nbsp; cutting-edge methodology explored primarily in computational linguistics. The idea for the present volume came out of a concrete response to this challenge.&nbsp; The 1st International Workshop on Computational Approaches to Historical Language Change (LChange'19), at ACL 2019, brought together scholars from both fields. This volume offers a survey of this exciting new direction in the study of semantic change, a discussion of the many remaining challenges that we face in pursuing it, and considerably updated and extended versions of a selection of the contributions to the LChange'19 workshop, addressing both more theoretical problems —&nbsp; e.g., discovery of "laws of semantic change"&nbsp;— and practical applications, such as information retrieval in longitudinal text archives

Computational approaches to semantic change

Semantic change&nbsp;— how the meanings of words change over time&nbsp;— has preoccupied scholars since well before modern linguistics emerged in the late 19th and early 20th century, ushering in a new methodological turn in the study of language change. Compared to changes in sound and grammar, semantic change is the least&nbsp; understood. Ever since, the study of semantic change has progressed steadily, accumulating a vast store of knowledge for over a century, encompassing many languages and language families. Historical linguists also early on realized the potential of computers as research tools, with papers at the very first international conferences in computational linguistics in the 1960s. Such computational studies still tended to be small-scale, method-oriented, and qualitative. However, recent years have witnessed a sea-change in this regard. Big-data empirical quantitative investigations are now coming to the forefront, enabled by enormous advances in storage capability and processing power. Diachronic corpora have grown beyond imagination, defying exploration by traditional manual qualitative methods, and language technology has become increasingly data-driven and semantics-oriented. These developments present a golden opportunity for the empirical study of semantic change over both long and short time spans. A major challenge presently is to integrate the hard-earned&nbsp; knowledge and expertise of traditional historical linguistics with&nbsp; cutting-edge methodology explored primarily in computational linguistics. The idea for the present volume came out of a concrete response to this challenge.&nbsp; The 1st International Workshop on Computational Approaches to Historical Language Change (LChange'19), at ACL 2019, brought together scholars from both fields. This volume offers a survey of this exciting new direction in the study of semantic change, a discussion of the many remaining challenges that we face in pursuing it, and considerably updated and extended versions of a selection of the contributions to the LChange'19 workshop, addressing both more theoretical problems —&nbsp; e.g., discovery of "laws of semantic change"&nbsp;— and practical applications, such as information retrieval in longitudinal text archives

Computational approaches to semantic change

Semantic change&nbsp;— how the meanings of words change over time&nbsp;— has preoccupied scholars since well before modern linguistics emerged in the late 19th and early 20th century, ushering in a new methodological turn in the study of language change. Compared to changes in sound and grammar, semantic change is the least&nbsp; understood. Ever since, the study of semantic change has progressed steadily, accumulating a vast store of knowledge for over a century, encompassing many languages and language families. Historical linguists also early on realized the potential of computers as research tools, with papers at the very first international conferences in computational linguistics in the 1960s. Such computational studies still tended to be small-scale, method-oriented, and qualitative. However, recent years have witnessed a sea-change in this regard. Big-data empirical quantitative investigations are now coming to the forefront, enabled by enormous advances in storage capability and processing power. Diachronic corpora have grown beyond imagination, defying exploration by traditional manual qualitative methods, and language technology has become increasingly data-driven and semantics-oriented. These developments present a golden opportunity for the empirical study of semantic change over both long and short time spans. A major challenge presently is to integrate the hard-earned&nbsp; knowledge and expertise of traditional historical linguistics with&nbsp; cutting-edge methodology explored primarily in computational linguistics. The idea for the present volume came out of a concrete response to this challenge.&nbsp; The 1st International Workshop on Computational Approaches to Historical Language Change (LChange'19), at ACL 2019, brought together scholars from both fields. This volume offers a survey of this exciting new direction in the study of semantic change, a discussion of the many remaining challenges that we face in pursuing it, and considerably updated and extended versions of a selection of the contributions to the LChange'19 workshop, addressing both more theoretical problems —&nbsp; e.g., discovery of "laws of semantic change"&nbsp;— and practical applications, such as information retrieval in longitudinal text archives