12th GATE Summer School (17-21 June 2019)

12th GATE Training Course: open-source natural language processing with an emphasis on social media

For over a decade, the GATE team has provided an annual course in using our technology. The course content and track options have changed a bit over the years, but it always includes material to help novices get started with GATE as well as introductory and more advanced use of the JAPE language for matching patterns of document annotations.

The latest course also included machine learning, crowdsourcing, sentiment analysis, and an optional programming module (aimed mainly at Java programmers to help them embed GATE libraries, applications, and resources in web services and other "behind the scenes" processing).  We have also added examples and new tools in GATE to cover the increasing demand for getting data out of and back into spreadsheets, and updated our work on social media analysis, another growing field.

Information in "feral databases" (spreadsheets)

We also disseminated work from several current research projects.

Semantics in scientometrics

• From KNOWMAK and RISIS, we presented our work on using semantic technologies in scientometrics, by applying NLP and ontologies to document categorization in order to contribute to a searchable knowledge base that allows users to find aggregate and specific data about scientific publications, patents, and research projects by geography, category, etc.
• Much of our recent work on social media analysis, including opinion mining and abuse detection and measurement, has been done as part of the SoBigData project.
• The increasing range of tools for languages other than English links with our participation in the European Language Grid, which is also supported further development of GATE Cloud, our platform for text analytics as a service.

Conditional processing of multilingual documents

Processing German in GATE

The GATE software distributions, documentation, and training materials from our courses can all be downloaded from our website under open licences. Source code is also available from our github page.

Acknowledgements

The course included research funded by the European Union's Horizon 2020 research and innovation programme under grant agreements No. 726992 (KNOWMAK), No. 654024 (SoBigData), No. 824091 (RISIS), and No. 825627 (European Language Grid); by the Free Press Unlimited pilot project "Developing a database for the improved collection and systematisation of information on incidents of violations against journalists"; by EPSRC grant EP/I004327/1; by the British Academy under the  call "The Humanities and Social Sciences: Tackling the UK’s International Challenges"; and by Nesta.

Adapted TextRank for Term Extraction: A Generic Method of Improving Automatic Term Extraction Algorithms

This summer, we presented some of our latest work at SEMANTiCS 2018 in Vienna: "Adapted TextRank for Term Extraction: A Generic Method of Improving Automatic Term Extraction Algorithms".

Zhang, Z., Petrak, J. & Maynard, D. Adapted TextRank for Term Extraction: A Generic Method of Improving Automatic Term Extraction Algorithms. in SEMANTiCS 2018 – 14th International Conference on Semantic Systems 00, 0-000 (2018).

This work has been carried out in the context of the EU KNOWMAK project, where we're developing tools for multi-topic classification of text against an ontology, in order to attempt to map the state of European research output in key technologies.

Automatic Term Extraction (ATE) is a fundamental technique used in computational linguistics for recognising terms in text. Processing the collected terms in a text is a key step in understanding the content of the text.  There are many different ATE methods, but these all tend to work well only in a one specific domain.  In other words, there is no universal method which produces consistently good results, and so we have to choose an appropriate method for the domain being targeted.

In this work, we have developed a novel method for ATE which addresses two major limitations: the fact that no single ATE method consistently performs well across all domains, and the fact that the majority of ATE methods are unsupervised. Our generic method, AdaText, improves the accuracy of existing ATE methods, using existing lexical resources to support them, by revising the TextRank algorithm.

After being given a target text, AdaText:

1. Selects a subset of words based on their semantic relatedness to a set of seed words or phrases relevant to the domain, but not necessarily representative of the terms within the target text. 
2. It then applies an adapted TextRank algorithm to create a graph for these words, and computes a text-level TextRank score for each selected word. 
3. Finally, these scores are used to revise the score of a term candidate previously computed by an ATE method. 

This technique was trialled using a variety of parameters (such as the threshold of semantic similarity to select words, as described in step two) over two distinct datasets (GENIA and ACLv2, comprising Medline abstracts and abstracts from ACL respectively). We also tested it with a wide variety of state of the art ATE methods, including modified TFIDF, CValue, Basic, RAKE, Weirdness, LinkProbability, X², GlossEx and PositiveUnlabeled.

The figures show a sample of performances in different datasets and using different ATE techniques. The base performance of the ATE method is represented by the blachttps://gate.ac.uk/g8/page/show/2/sale/images/blog/Results-by-AdaText-compared-against-the-base-ATE-methods-y-axis-average-PK-for-all.pngk horizontal line. The horizontal axis represents the semantic similarity threshold used in step 1. The vertical axis shows average P@K for all five Ks considered.

This new generic combination approach can consistently improve the performance of the ATE method by 25 points, which is a significant increase. However, there is still room for improvement. In future work, we aim to optimise the selection of words from the TextRank graph, work on expanding TextRank to a graph of both words and phrases, and to explore how the size and source of the seed lexicon affects the performance of AdaText.