BL Labs 2017 Symposium: Samtla, Research Award Runner Up
Samtla (Search And Mining Tools for Labelling Archives) was developed to address a need in the humanities for research tools that help to search, browse, compare, and annotate documents stored in digital archives. The system was designed in collaboration with researchers at Southampton University, whose research involved locating shared vocabulary and phrases across an archive of Aramaic Magic Texts from Late Antiquity. The archive contained texts written in Aramaic, Mandaic, Syriac, and Hebrew languages. Due to the morphological complexity of these languages, where morphemes are attached to a root morpheme to mark gender and number, standard approaches and off-the-shelf software were not flexible enough for the task, as they tended to be designed to work with a specific archive or user group.
Samtla is designed to extract the same or similar information that may be expressed by authors in different ways, whether it is in the choice of vocabulary or the grammar. Traditionally search and text mining tools have been based on words, which limits their use to corpora containing languages were 'words' can be easily identified and extracted from text, e.g. languages with a whitespace character like English, French, German, etc. Word models tend to fail when the language is morphologically complex, like Aramaic, and Hebrew. Samtla addresses these issues by adopting a character-level approach stored in a statistical language model. This means that rather than extracting words, we extract character-sequences representing the morphology of the language, which we then use to match the search terms of the query and rank the documents according to the statistics of the language. Character-based models are language independent as there is no need to preprocess the document, and we can locate words and phrases with a lot of flexibility. As a result Samtla compensates for the variability in language use, spelling errors made by users when they search, and errors in the document as a result of the digitisation process (e.g. OCR errors).
The British Library have been very supportive of the work by openly providing access to their digital archives. The archives ranged in domain, topic, language, and scale, which enabled us to test Samtla‚Äôs flexibility to its limits. One of the biggest challenges we faced was indexing larger-scale archives of several gigabytes. Some archives also contained a scan of the original document together with metadata about the structure of the text. This provided a basis for developing new tools that brought researchers closer to the original object, which included highlighting the named entities over both the raw text, and the scanned image.
Currently we are focusing on developing approaches for leveraging the semantics underlying text data in order to help researchers find semantically related information. Semantic annotation is also useful for labelling text data with named entities, and sentiments. Our current aim is to develop approaches for annotating text data in any language or domain, which is challenging due to the fact that languages encode the semantics of a text in different ways.
As a first step we are offering labelled data to researchers, as part of a trial service, in order to help speed up the research process, or provide tagged data for machine learning approaches. If you are interested in participating in this trial, then more information can be found at www.samtla.com.
If this blog post has stimulated your interest in working with the British Library's digital collections, start a project and enter it for one of the BL Labs 2018 Awards! Join us on 12 November 2018 for the BL Labs annual Symposium at the British Library.
Posted by BL Labs on behalf of Dr Martyn Harris, Prof Dan Levene, Prof Mark Levene and Dr Dell Zhang.