A time-sensitive historical thesaurus-based semantic tagger for deep semantic annotation

Piao, S., Dallachy, F. , Baron, A., Demmen, J., Wattam, S., Durkin, P., McCracken, J., Rayson, P. and Alexander, M. (2017) A time-sensitive historical thesaurus-based semantic tagger for deep semantic annotation. Computer Speech and Language, 46, 113 - 135. (doi: 10.1016/j.csl.2017.04.010)

Abstract

Automatic extraction and analysis of meaning-related information from natural language data has been an important issue in a number of research areas, such as natural language processing (NLP), text mining, corpus linguistics, and data science. An important aspect of such information extraction and analysis is the semantic annotation of language data using a semantic tagger. In practice, various semantic annotation tools have been designed to carry out different levels of semantic annotation, such as topics of documents, semantic role labeling, named entities or events. Currently, the majority of existing semantic annotation tools identify and tag partial core semantic information in language data, but they tend to be applicable only for modern language corpora. While such semantic analyzers have proven useful for various purposes, a semantic annotation tool that is capable of annotating deep semantic senses of all lexical units, or all-words tagging, is still desirable for a deep, comprehensive semantic analysis of language data. With large-scale digitization efforts underway, delivering historical corpora with texts dating from the last 400 years, a particularly challenging aspect is the need to adapt the annotation in the face of significant word meaning change over time. In this paper, we report on the development of a new semantic tagger (the Historical Thesaurus Semantic Tagger), and discuss challenging issues we faced in this work. This new semantic tagger is built on existing {NLP} tools and incorporates a large-scale historical English thesaurus linked to the Oxford English Dictionary. Employing contextual disambiguation algorithms, this tool is capable of annotating lexical units with a historically-valid highly fine-grained semantic categorization scheme that contains about 225,000 semantic concepts and 4,033 thematic semantic categories. In terms of novelty, it is adapted for processing historical English data, with rich information about historical usage of words and a spelling variant normalizer for historical forms of English. Furthermore, it is able to make use of knowledge about the publication date of a text to adapt its output. In our evaluation, the system achieved encouraging accuracies ranging from 77.12 to 91.08 on individual test texts. Applying time-sensitive methods improved results by as much as 3.54 and by 1.72 on average.

• Item Type: Articles
• Keywords: Language technology, semantics, historical thesaurus, linguistics, information retrieval, corpus linguistics, corpora.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Alexander, Professor Marc and Durkin, Dr Philip and Dallachy, Dr Fraser
• Authors: Piao, S., Dallachy, F., Baron, A., Demmen, J., Wattam, S., Durkin, P., McCracken, J., Rayson, P., and Alexander, M.
• Subjects: P Language and Literature > P Philology. Linguistics; P Language and Literature > PE English; Q Science > QA Mathematics > QA76 Computer software
• College/School: College of Arts & Humanities > School of Critical Studies > English Language and Linguistics
• Journal Name: Computer Speech and Language
• Journal Abbr.: CSL
• Publisher: Elsevier
• ISSN: 0885-2308
• ISSN (Online): 1095-8363
• Published Online: 17 May 2017
• Copyright Holders: Copyright © 2017 The Authors
• First Published: First published in Computer Speech and Language 46: 113-135
• Publisher Policy: Reproduced under a Creative Commons License

Related URLs

• Project Website

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
64894	1	Semantic Annotation and Mark Up for Enhancing Lexical Searches (SAMUELS)	Marc Alexander	Arts & Humanities Research Council (AHRC)	AH/L010062/1	CRIT - ENGLISH LANGUAGE & LINGUISTICS