Proceedings of the NoDaLiDa 2017 Workshop on Processing Historical Language

We present the results of text reuse de- tection, based on the corpus of scanned and OCR-recognized Finnish newspapers and journals from 1771 to 1910. Our study draws on BLAST, a software cre- ated for comparing and aligning biologi- cal sequences. We show different types of text reuse in this corpus, and also present a comparison to the software Passim, de- veloped at the Northeastern University in Boston, for text reuse detection.  </div

Words In Action: Interdisciplinary Approaches To Understanding Word Processing And Storage

Almost all levels of language knowledge and processing (from phonology, to syntax and semantics) are known to be affected by knowledge of word structure at varying degrees. A better understanding of the human strategies involved in learning and processing word structure thus lies at the heart of our comprehension of the basic mechanisms serving both language and cognition and is key to addressing some fundamental challenges for the study of the physiology of grammar. On the 12th and 13th of October 2009, in the Research Area of the Italian National Research Council (CNR) in Pisa, 26 scholars from Europe, Canada and the United States were convened to take part in the European Science Foundation Exploratory Workshop "Words in Action: Interdisciplinary Approaches To Understanding Word Processing And Storage". The workshop brought together experts of various scientific domains and different theoretical inclinations to advance the current awareness of theoretical, historical, psycholinguistic, computational and neurophysiological issues in morphological processing and learning, with a view to assessing levels of research convergence and exploring the potential for synergy and strategic co-operation

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

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