A Practical Guide to Hybrid Natural Language Processing: Combining Neural Models and Knowledge Graphs for Nlp
暫譯: 混合自然語言處理實用指南：結合神經模型與知識圖譜進行 NLP

Gomez-Perez, Jose Manuel, Denaux, Ronald, Garcia-Silva, Andres

A Practical Guide to Hybrid Natural Language Processing: Combining Neural Models and Knowledge Graphs for Nlp

商品描述

This book provides readers with a practical guide to the principles of hybrid approaches to natural language processing (NLP) involving a combination of neural methods and knowledge graphs. To this end, it first introduces the main building blocks and then describes how they can be integrated to support the effective implementation of real-world NLP applications. To illustrate the ideas described, the book also includes a comprehensive set of experiments and exercises involving different algorithms over a selection of domains and corpora in various NLP tasks.

Throughout, the authors show how to leverage complementary representations stemming from the analysis of unstructured text corpora as well as the entities and relations described explicitly in a knowledge graph, how to integrate such representations, and how to use the resulting features to effectively solve NLP tasks in a range of domains. In addition, the book offers access to executable code with examples, exercises and real-world applications in key domains, like disinformation analysis and machine reading comprehension of scientific literature. All the examples and exercises proposed in the book are available as executable Jupyter notebooks in a GitHub repository. They are all ready to be run on Google Colaboratory or, if preferred, in a local environment.

A valuable resource for anyone interested in the interplay between neural and knowledge-based approaches to NLP, this book is a useful guide for readers with a background in structured knowledge representations as well as those whose main approach to AI is fundamentally based on logic. Further, it will appeal to those whose main background is in the areas of machine and deep learning who are looking for ways to leverage structured knowledge bases to optimize results along the NLP downstream.


商品描述(中文翻譯)

這本書為讀者提供了一個實用的指南，介紹結合神經方法和知識圖譜的混合自然語言處理（NLP）方法的原則。為此，書中首先介紹了主要的構建塊，然後描述了如何將它們整合以支持現實世界NLP應用的有效實施。為了說明所描述的概念，書中還包括了一組全面的實驗和練習，涉及不同算法在各種NLP任務中的多個領域和語料庫。

在整個過程中，作者展示了如何利用來自非結構化文本語料庫分析的互補表示，以及在知識圖譜中明確描述的實體和關係，如何整合這些表示，並如何使用所產生的特徵有效解決各種領域的NLP任務。此外，這本書提供了可執行代碼的訪問，包括示例、練習和在關鍵領域的現實應用，如虛假信息分析和科學文獻的機器閱讀理解。書中提出的所有示例和練習都可以在GitHub存儲庫中作為可執行的Jupyter筆記本獲得。它們都可以在Google Colaboratory上運行，或者如果需要，也可以在本地環境中運行。

這本書對於任何對神經方法和基於知識的方法在NLP中的相互作用感興趣的人來說，都是一個有價值的資源，對於具有結構化知識表示背景的讀者以及主要基於邏輯的AI方法的讀者來說，都是一本有用的指南。此外，它也會吸引那些主要背景在機器學習和深度學習領域的人，這些人希望尋找利用結構化知識庫來優化NLP下游結果的方法。

作者簡介

Jose Manuel Gomez-Perez leads the Cogito Research Lab at Expert System in Madrid, Spain, where he focuses on the combination of neural and knowledge-based approaches to enable reading comprehension in machines. His work lies at the intersection of several areas of artificial intelligence, including natural language processing, knowledge graphs and deep learning. He also consults for organizations like the European Space Agency and is the co-founder of ROHub.org, a platform for the intelligent management of scientific information. A former Marie Curie fellow, José Manuel holds a Ph.D. in Computer Science and Artificial Intelligence from Universidad Politécnica de Madrid. He regularly publishes in top scientific conferences and journals and his views have appeared in magazines like Nature and Scientific American, as well as newspapers like El País.

Ronald Denaux is a senior researcher scientist at Expert System. Ronald obtained his MSc in Computer Science from the Technical University Eindhoven, The Netherlands. After a couple of years working in industry as a software developer for a large IT company in The Netherlands, Ronald decided to go back to academia. He obtained a Ph.D., again in Computer Science, from the University of Leeds, UK. Ronald's research interests have revolved around making semantic web technologies more usable for end users, which has required research into the areas of ontology authoring and reasoning, natural language interfaces, dialogue systems, intelligent user interfaces and user modelling.

Andres Garcia-Silva is a senior research scientist at Expert System, where he works on a variety of fields related to knowledge management and artificial intelligence including semantic technologies, natural language processing, information extraction and retrieval, and machine learning. Andrés holds a Ph.D. and a Master degree in Artificial Intelligence from Universidad Politécnica de Madrid. He has worked as a visiting researcher at the University of Southampton, the Free University of Berlin, and the University of Southern California. Andrés regularly publishes and reviews papers for conferences and workshops in the semantic web research community.

作者簡介(中文翻譯)

何塞·曼努埃爾·戈麥斯-佩雷斯在西班牙馬德里的Expert System領導Cogito研究實驗室，專注於結合神經網絡和知識基礎的方法，以實現機器的閱讀理解。他的工作位於多個人工智慧領域的交集，包括自然語言處理、知識圖譜和深度學習。他還為歐洲太空總署等組織提供諮詢，並且是ROHub.org的共同創辦人，這是一個用於智能管理科學信息的平台。作為前馬里·居里研究員，何塞·曼努埃爾擁有馬德里理工大學的計算機科學和人工智慧博士學位。他定期在頂尖科學會議和期刊上發表文章，並且他的觀點曾出現在《自然》和《科學美國人》等雜誌，以及《國家報》等報紙上。

羅納德·德諾是Expert System的高級研究科學家。羅納德在荷蘭埃因霍溫科技大學獲得計算機科學碩士學位。在荷蘭的一家大型IT公司擔任軟體開發人員幾年後，羅納德決定重返學術界。他在英國利茲大學再次獲得計算機科學博士學位。羅納德的研究興趣圍繞著使語義網技術對最終用戶更具可用性，這需要對本體創作和推理、自然語言介面、對話系統、智能用戶介面和用戶建模等領域進行研究。

安德烈斯·加西亞-西爾瓦是Expert System的高級研究科學家，專注於與知識管理和人工智慧相關的多個領域，包括語義技術、自然語言處理、信息提取和檢索，以及機器學習。安德烈斯擁有馬德里理工大學的人工智慧博士學位和碩士學位。他曾在南安普敦大學、柏林自由大學和南加州大學擔任訪問研究員。安德烈斯定期為語義網研究社群的會議和研討會發表和審稿。

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