Corpus-Based Approaches to Igbo Diacritic Restoration

With natural language processing (NLP), researchers aim to get the computer to identify and understand the patterns in human languages. This is often difficult because a language embeds many dynamic and varied properties in its syntaxes, pragmatics and phonology, which needs to be captured and processed. The capacity of computers to process natural languages is increasing because NLP researchers are pushing its boundaries. But these research works focus more on well resourced languages such as English, Japanese, German, French, Russian, Mandarin Chinese etc. Over 95% of the world’s 7000 languages are low-resourced for NLP i.e. they have little or no data, tools, and techniques for NLP work. In this thesis, we present an overview of diacritic ambiguity and a review of previous diacritic disambiguation approaches on other languages. Focusing on Igbo language, we report the steps taken to develop a flexible framework for generating datasets for diacritic restoration. Three main approaches, the standard n-gram model, the classification models and the embedding models were proposed. The standard n-gram models use a sequence of previous words to the target stripped word as key predictors of the correct variants. For the classification models, a window of words on both sides of the target stripped word were use. The embedding models compare the similarity scores of the combined context word embeddings and the embeddings of each of the candidate variant vectors. The processes and techniques involved in projecting embeddings from a model trained with English texts to an Igbo embedding space and the creation of intrinsic evaluation tasks to validate the models were also discussed. A comparative analysis of the results indicate that all the approaches significantly improved on the baseline performance which uses the unigram model. The details of the processed involved in building the models as well as the possible directions for future work are discussed in this work

Extracting Imprecise Geographical and Temporal References from Journey Narratives

Previous approaches to understanding geographies in textual sources tend to focus on geoparsing to automatically identify place names and allocate them to coordinates. Such methods are highly quantitative and are limited to named places for which coordinates can be found, and have little concept of time. Yet, as narratives of journeys make abundantly clear, human experiences of geography are often subjective and more suited to qualitative representation. In these cases, “geography” is not limited to named places; rather, it incorporates the vague, imprecise, and ambiguous, with references to, for example, “the camp”, or “the hills in the distance”, and includes the relative locations using terms such as “near to”, “on the left”, “north of” or “a few hours’ journey from”. In this demo paper, we describe our research prototype to extract and analyse qualitative and quantitative references to place and time in two corpora of English Lake District travel writing and Holocaust survivor testimonies

Igbo-English Machine Translation:An Evaluation Benchmark

Although researchers and practitioners are pushing the boundaries and enhancing the capacities of NLP tools and methods, works on African languages are lagging. A lot of focus on well resourced languages such as English, Japanese, German, French, Russian, Mandarin Chinese etc. Over 97% of the world's 7000 languages, including African languages, are low resourced for NLP i.e. they have little or no data, tools, and techniques for NLP research. For instance, only 5 out of 2965, 0.19% authors of full text papers in the ACL Anthology extracted from the 5 major conferences in 2018 ACL, NAACL, EMNLP, COLING and CoNLL, are affiliated to African institutions. In this work, we discuss our effort toward building a standard machine translation benchmark dataset for Igbo, one of the 3 major Nigerian languages. Igbo is spoken by more than 50 million people globally with over 50% of the speakers are in southeastern Nigeria. Igbo is low resourced although there have been some efforts toward developing IgboNLP such as part of speech tagging and diacritic restoratio

Introducing the Welsh text summarisation dataset and baseline systems

Welsh is an official language in Wales and is spoken by an estimated 884,300 people (29.2% of the population of Wales). Despite this status and estimated increase in speaker numbers since the last (2011) census, Welsh remains a minority language undergoing revitalisation and promotion by Welsh Government and relevant stakeholders. As part of the effort to increase the availability of Welsh digital technology, this paper introduces the first Welsh summarisation dataset, which we provide freely for research purposes to help advance the work on Welsh summarisation. The dataset was created by Welsh speakers through manually summarising Welsh Wikipedia articles. In addition, the paper discusses the implementation and evaluation of different summarisation systems for Welsh. The summarisation systems and results will serve as benchmarks for the development of summarisers in other minority language contexts

Creation of an evaluation corpus and baseline evaluation scores for Welsh text summarisation

As part of the effort to increase the availability of Welsh digital technology, this paper introduces the first human vs metrics Welsh summarisation evaluation results and dataset, which we provide freely for research purposes to help advance the work on Welsh summarisation. The system summaries were created using an extractive graph-based Welsh summariser. The system summaries were evaluated by both human and a range of ROUGE metric variants (e.g. ROUGE 1, 2, L and SU4). The summaries and evaluation results will serve as benchmarks for the development of summarisers and evaluation metrics in other minority language contexts

Infrastructure for Semantic Annotation in the Genomics Domain

We describe a novel super-infrastructure for biomedical text mining which incorporates an end-to-end pipeline for the collection, annotation, storage, retrieval and analysis of biomedical and life sciences literature, combining NLP and corpus linguistics methods. The infrastructure permits extreme-scale research on the open access PubMed Central archive. It combines an updatable Gene Ontology Semantic Tagger (GOST) for entity identification and semantic markup in the literature, with a NLP pipeline scheduler (Buster) to collect and process the corpus, and a bespoke columnar corpus database (LexiDB) for indexing. The corpus database is distributed to permit fast indexing, and provides a simple web front-end with corpus linguistics methods for sub-corpus comparison and retrieval. GOST is also connected as a service in the Language Application (LAPPS) Grid, in which context it is interoperable with other NLP tools and data in the Grid and can be combined with them in more complex workflows. In a literature based discovery setting, we have created an annotated corpus of 9,776 papers with 5,481,543 words