Journal Articles

Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915

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Author: search.filters.author.Das PSubject: search.filters.subject.natural language processing

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    Detecting the geospatialness of prepositions from natural language text
    (Schloss Dagstuhl – Leibniz-Zentrum für Informatik, 2019-09-01) Radke M; Das P; Stock K; Jones CB; Timpf S; Schlieder C; Kattenbeck M; Ludwig B; Stewart K
    There is increasing interest in detecting the presence of geospatial locative expressions that include spatial relation terms such as near or within . Being able to do so provides a foundation for interpreting relative descriptions of location and for building corpora that facilitate the development of methods for spatial relation extraction and interpretation. Here we evaluate the use of a spatial role labelling procedure to distinguish geospatial uses of prepositions from other spatial and non-spatial uses and experiment with the use of additional machine learning features to improve the quality of detection of geospatial prepositions. An annotated corpus of nearly 2000 instances of preposition usage was created for training and testing the classifiers.
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    Detecting geospatial location descriptions in natural language text
    (Taylor and Francis Group, 2022) Stock K; Jones CB; Russell S; Radke M; Das P; Aflaki N
    References to geographic locations are common in text data sources including social media and web pages. They take different forms from simple place names to relative expressions that describe location through a spatial relationship to a reference object (e.g. the house beside the Waikato River). Often complex, multi-word phrases are employed (e.g. the road and railway cross at right angles; the road in line with the canal) where spatial relationships are communicated with various parts of speech including prepositions, verbs, adverbs and adjectives. We address the problem of automatically detecting relative geospatial location descriptions, which we define as those that include spatial relation terms referencing geographic objects, and distinguishing them from non-geographical descriptions of location (e.g. the book on the table). We experiment with several methods for automated classification of text expressions, using features for machine learning that include bag of words that detect distinctive words, word embeddings that encode meanings of words and manually identified language patterns that characterise geospatial expressions. Using three data sets created for this study, we find that ensemble and meta-classifier approaches, that variously combine predictions from several other classifiers with data features, provide the best F-measure of 0.90 for detecting geospatial expressions.