An Algorithmic Scheme for Statistical Thesaurus Construction in a Morphologically Rich Language

Liebeskind, Chaya; Dagan, Ido; Schler, Jonathan

doi:10.1080/08839514.2019.1583447

Cited by 6 publications

(5 citation statements)

References 29 publications

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“…We plan to investigate additional aggregation methods and explore the impact of the individual models on the combined system to improve our system results. We also plan to try our system on other languages of different families, such as Semitic languages (Liebeskind and Liebeskind, 2020) and use LSC models to construct diachronic thesaurus, which bridges the lexical gap between modern and ancient language (Zohar et al, 2013;Liebeskind and Dagan, 2015;Liebeskind et al, 2016;Liebeskind et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

JCT at SemEval-2020 Task 1: Combined Semantic Vector Spaces Models for Unsupervised Lexical Semantic Change Detection

Amar¹,

Liebeskind

2020

Proceedings of the Fourteenth Workshop on Semantic Evaluation

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In this paper, we present our contribution in SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection, where we systematically combine existing models for unsupervised capturing of lexical semantic change across time in text corpora of German, English, Latin and Swedish.In particular, we analyze the score distribution of existing models. Then we define a general classification threshold, adjust it independently to each of the models and measure the models' score certainty. Finally, using both the threshold and score certainty, we aggregate the models for the two sub-tasks: binary classification and ranking.

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Section: Discussionmentioning

confidence: 99%

JCT at SemEval-2020 Task 1: Combined Semantic Vector Spaces Models for Unsupervised Lexical Semantic Change Detection

Amar¹,

Liebeskind

2020

Proceedings of the Fourteenth Workshop on Semantic Evaluation

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“…Previous work reported that the available modern Hebrew morphological analyzing tools have poor performance on the Responsa corpus. For example, whereas the accuracy of a state-ofthe-art modern Hebrew tagger on modern Hebrew text was over 90%, on the Responsa corpus it was only about 60% [Liebeskind et al, 2012]. Therefore, HaCohen-Kerner et al [2010] used the raw text and we followed the same approach.…”

Section: Conventional Machine-learning Methodsmentioning

confidence: 99%

“…In the evaluation stage, they classified each related term into lemma based groups. Later, Liebeskind et al [2012] proposed a schematic methodology for generating a co-occurrencebased thesaurus for the Responsa project. They investigated three options for term representation: surface form, lemma, and multiple lemmas, supplemented with the clustering of term variants.…”

Section: The Responsa Corpus and Diachronic Tasksmentioning

confidence: 99%

Deep Learning for Period Classification of Historical Hebrew Texts

Liebeskind

Liebeskind²

2020

Journal of Data Mining &Amp; Digital Humanities

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In this study, we address the interesting task of classifying historical texts by their assumed period of writ-ing. 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-learningalgorithms. These algorithms require careful feature engineering and considerable domain expertise todesign a feature extractor to transform the raw text into a feature vector from which the classifier couldlearn to classify any unseen valid input. Recently, deep learning has produced extremely promising re-sults for various tasks in natural language processing (NLP). The primary advantage of deep learning isthat human engineers did not design the feature layers, but the features were extrapolated from data witha general-purpose learning procedure. We investigated deep learning models for period classification ofhistorical texts. We compared three common models: paragraph vectors, convolutional neural networks (CNN) and recurrent neural networks (RNN), and conventional machine-learning methods. We demon-strate that the CNN and RNN models outperformed the paragraph vector model and the conventionalsupervised machine-learning algorithms. In addition, we constructed word embeddings for each timeperiod and analyzed semantic changes of word meanings over time.

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“…Previously, most of the methods for lexical semantic change detection built co-occurrence matrices [70,84,109]. While in some cases, high-dimensional sparse matrices were used, in other cases, the dimensions of the matrices were reduced mainly using singular value decomposition (SVD) [156].…”

Section: Word Embeddingsmentioning

confidence: 99%

LL(O)D and NLP perspectives on semantic change for humanities research

Armaselu

Apostol

Khan

et al. 2022

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This paper presents an overview of the LL(O)D and NLP methods, tools and data for detecting and representing semantic change, with its main application in humanities research. The paper’s aim is to provide the starting point for the construction of a workflow and set of multilingual diachronic ontologies within the humanities use case of the COST Action Nexus Linguarum, European network for Web-centred linguistic data science, CA18209. The survey focuses on the essential aspects needed to understand the current trends and to build applications in this area of study.

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An Algorithmic Scheme for Statistical Thesaurus Construction in a Morphologically Rich Language

Cited by 6 publications

References 29 publications

JCT at SemEval-2020 Task 1: Combined Semantic Vector Spaces Models for Unsupervised Lexical Semantic Change Detection

JCT at SemEval-2020 Task 1: Combined Semantic Vector Spaces Models for Unsupervised Lexical Semantic Change Detection

Deep Learning for Period Classification of Historical Hebrew Texts

LL(O)D and NLP perspectives on semantic change for humanities research

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