Algebraic Structures in Natural Language

Algebraic Structures in Natural Language addresses a central problem in cognitive science concerning the learning procedures through which humans acquire and represent natural language. Until recently algebraic systems have dominated the study of natural language in formal and computational linguistics, AI, and the psychology of language, with linguistic knowledge seen as encoded in formal grammars, model theories, proof theories and other rule-driven devices. Recent work on deep learning has produced an increasingly powerful set of general learning mechanisms which do not apply rule-based algebraic models of representation. The success of deep learning in NLP has led some researchers to question the role of algebraic models in the study of human language acquisition and linguistic representation. Psychologists and cognitive scientists have also been exploring explanations of language evolution and language acquisition that rely on probabilistic methods, social interaction and information theory, rather than on formal models of grammar induction.

This book addresses the learning procedures through which humans acquire natural language, and the way in which they represent its properties. It brings together leading researchers from computational linguistics, psychology, behavioral science and mathematical linguistics to consider the significance of non-algebraic methods for the study of natural language. The text represents a wide spectrum of views, from the claim that algebraic systems are largely irrelevant to the contrary position that non-algebraic learning methods are engineering devices for efficiently identifying the patterns that underlying grammars and semantic models generate for natural language input. There are interesting and important perspectives that fall at intermediate points between these opposing approaches, and they may combine elements of both. It will appeal to researchers and advanced students in each of these fields, as well as to anyone who wants to learn more about the relationship between computational models and natural language.

《自然語言中的代數結構》探討了認知科學中一個核心問題，即人類獲取和表徵自然語言的學習過程。在最近的研究中，代數系統在形式和計算語言學、人工智能和語言心理學中主導了對自然語言的研究，語言知識被視為編碼在形式語法、模型理論、證明理論和其他基於規則的設備中。深度學習的最新研究產生了一套越來越強大的通用學習機制，不適用基於規則的代數模型來表徵。深度學習在自然語言處理中的成功使一些研究人員質疑代數模型在人類語言獲取和語言表徵研究中的作用。心理學家和認知科學家也一直在探索語言演化和語言獲取的解釋，這些解釋依賴於概率方法、社會互動和信息理論，而不是基於形式語法歸納的模型。

本書探討了人類獲取自然語言的學習過程，以及他們如何表徵其特性。它匯集了計算語言學、心理學、行為科學和數學語言學的領先研究人員，考慮非代數方法對自然語言研究的重要性。本書代表了各種觀點，從代數系統在研究自然語言中幾乎無關緊要的主張，到非代數學習方法是有效識別語法和語義模型生成的模式的工程設備的相反觀點。在這些對立觀點之間，還有一些有趣且重要的中間觀點，它們可能結合了兩者的元素。本書將吸引這些領域的研究人員和高級學生，以及任何想要了解計算模型和自然語言之間關係的人。

Shalom Lappin is a Professor of Computational Linguistics at the University of Gothenburg, Professor of Natural Language Processing at Queen Mary University of London and Emeritus Professor of Computational Linguistics at King's College London. His research focuses on the application of machine learning and probabilistic models to the representation and the acquisition of linguistic knowledge.

Jean-Philippe Bernardy is a researcher at the University of Gothenburg. His main research interest is in interpretable linguistic models, in particular, those built from first principles of algebra, probability and geometry.

Shalom Lappin是哥德堡大學的計算語言學教授，倫敦玛丽女王大学的自然語言處理教授，以及倫敦國王學院的計算語言學名譽教授。他的研究專注於機器學習和概率模型在語言知識的表示和獲取方面的應用。

Jean-Philippe Bernardy是哥德堡大學的研究員。他主要研究興趣在於可解釋的語言模型，特別是那些建立在代數、概率和幾何的基礎上的模型。