Semantic Annotation and Mark Up for Enhancing Lexical Searches (SAMUELS)

As humanities datasets get ever larger, researchers have a pressing need for more sophisticated techniques of analysis. The most significant issue in big data research into textual datasets is that our primary methodology for searching, aggregating and analysing them relies not on concepts or meanings, but rather on word forms. These forms are imperfect and evasive proxies for the meanings they refer to, and with 60% of word forms in English referring to more than one meaning, and some word forms referring to close to two hundred meanings, the irrelevant "noise" which appears when searching using word forms grows with the size of the texts being searched.

In big data contexts, this problem cripples research, making any sort of detailed analysis entirely intractable and requiring impossible amounts of manual intervention. In this project, we will deliver a system for automatically annotating words in texts with their precise meanings, enabling a step-change in the way we deal with large textual data. The system is based around the unparalleled Historical Thesaurus of English, which contains 797,000 words from across the history of English arranged into 236,000 hierarchical categories of meanings alongside each word's dates of known use. The annotation software will take a text and provide for each word it contains an XML annotation giving the word meaning's Historical Thesaurus category code. The system will automatically disambiguate word meanings using a range of state-of-the-art computational techniques alongside new context-dependent methods unlocked by the Thesaurus's dating codes and its uniquely detailed and fine-grained hierarchical structure.

Textual data tagged in this way can then be accurately searched and precisely investigated, with any results also able to be aggregated at a range of levels of precision, without the need for manual intervention. A major part of the project is also the development of new techniques for working with semantically-aggregated and disambiguated data. Project partners will conduct research on resources including the Hansard Corpus, consisting of over 2.3 billion words of text, the Oxford English Corpus, the world's largest stratified corpus of modern English, and the EEBO-TCP corpus of 40,000 early modern books. As part of our work on changing the nature of how we deal with data on this scale, we will mine these text collections for frequently-occurring or statistically unusual concepts, will take advantage of our ability to search large datasets for terms realised by ambiguous word forms (such as "union" in the particular context of industrial relations rather than any of the other 33 possible meanings of this word), and will examine the data as a whole from a distant-reading perspective in order to look for striking or significant patterns of meaning changes across time.

These research projects based on tagged data will also drive the development of our tools for using this data, with teams of researchers across the UK and abroad providing a range of different demands on the data, ensuring a variety of needs and use-cases are catered for in the development of the project. In this way, we are committed to producing a set of compelling, fruitful, and practical research outcomes using semantically-tagged data during the lifetime of the project, in order to demonstrate the value of our approach and to help ensure the work of the project is as widely utilised and exploited as possible.

By doing all of this, we will enable new and transformative techniques of exploring, searching and investigating large-scale cultural, literary, historical and linguistic phenomena in big humanities datasets; through this project, it will be possible to place meaning - rather than word forms - at the heart of digital humanities research into text.

Education
The modern teaching of history and of literature often operates thematically, with students introduced to specific themes across a range of time periods. Open access through the BYU site to our annotated data will provide ways for students and teachers to explore the literature in EEBO and the political and historical information in Hansard through semantic categories which relate to the broader themes they are studying. We will provide suggestions for use of the data in teaching at upper secondary and tertiary levels, and will particularly highlight and support educational uses of the aggregated data produced in the Hansard project, as well as the Time Machine project. We also see potential for SAMUELS in areas such as improving written style through increased awareness of semantic patterning, grading reading materials according to age and topic suitability, and as a reading aid for older or complex texts.

Writers
Professional writers frequently use the print and online Historical Thesaurus for information about word usage at various periods, for the sake of authenticity in their writing or of novel ways to express concepts. The Time Machine project, linking words in supplied texts to the period during which those words are known to have been used, will be a further key resource for writers, particularly those working on historical fiction. Those writing about topics such as politics, history, and the development of debating styles will find a useful resource in the Hansard project.

Third Sector
Our work on Hansard has already led to discussions with third sector agencies such as theyworkforyou.org, who aggregate and display information to the public about their MP's activities. We will offer these agencies access to the aggregated data produced in the Glasgow Hansard project to enable them to display data on the topics and concepts most often discussed by their elected representatives across time. The original Hansard data was provided to Glasgow by the UK Parliamentary Service, and our enhanced version will be offered to them for their own uses. Libraries such as the National Library of Wales have indicated the need for a resource such as SAMUELS to aid cataloguing and other topic-related activities.

Commercial Implications
While non-commercial and academic access to the annotation system's website will remain free of charge, Oxford University Press, the experienced contractual operator for commercialising the Historical Thesaurus dataset, will partner with Glasgow and Lancaster to seek opportunities in the private sector for a semantic annotation system. One key commercial opportunity is with large providers of historical texts, who would benefit from semantically-aware searches, in particular from the ability to search using concepts in areas where users are unlikely to know historical synonyms for modern terms. This group includes the large private sector providers of legal texts and historical case material (eg WestLaw, HeinOnline or LexisNexis) as well as newspapers' historical archives, and database developers such as Gale, Cengage, and ProQuest. The project therefore has the potential to benefit the economy by stimulating income and creating further commercial outputs across time, and we will work with our partners to achieve this where feasible.

Data Mining
Finally, we see potential impact in SAMUELS for groups which need to mine large data sources for socially or commercially valuable information, including researchers in such fields as health informatics, marketing sentiment analysis, and wide-scale cultural analysis. Our project, with its advantages over plain lexical searches, will be an additional tool for these groups, permitting deeper and more productive mining of their data by aggregating a range of data sources in meaning-structured ways. Such work will also further expose the usefulness of the rich and unparalleled Historical Thesaurus lexical database for their research.