Journal Articles

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

Browse

Author: search.filters.author.Aflaki N

Search Results

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    Cross-corpora analysis of spatial language: The case of fictive motion
    (Schloss Dagstuhl – Leibniz-Zentrum für Informatik, 2019-09-01) Egorova E; Aflaki N; Marchis Fagundes CK; Stock K
    The way people describe where things are is one of the central questions of spatial information theory and has been the subject of considerable research. We investigate one particular type of location description, fictive motion (as in, The range runs along the coast). The use of this structure is known to highlight particular properties of the described entity, as well as to convey its configuration in physical space in an effective way. We annotated 496 fictive motion structures in seven corpora that represent different types of spatial discourse - news, travel blogs, texts describing outdoor pursuits and local history, as well as image and location descriptions. We analysed the results not only by examining the distribution of fictive motion structures across corpora, but also by exploring and comparing the semantic categories of verbs used in fictive motion. Our findings, first, add to our knowledge of location description strategies that go beyond prototypical locative phrases. They further reveal how the use of fictive motion varies across types of spatial discourse and reflects the nature of the described environment. Methodologically, we highlight the benefits of a cross-corpora analysis in the study of spatial language use across a variety of contexts.
  • Thumbnail Image
    Item
    Predicting Distance and Direction from Text Locality Descriptions for Biological Specimen Collections
    (Schloss Dagstuhl – Leibniz-Zentrum für Informatik, 2022-08-22) Liao R; Das PP; Jones CB; Aflaki N; Stock K; Ishikawa T; Fabrikant SI; Winter S
    A considerable proportion of records that describe biological specimens (flora, soil, invertebrates), and especially those that were collected decades ago, are not attached to corresponding geographical coordinates, but rather have their location described only through textual descriptions (e.g. North Canterbury, Selwyn River near bridge on Springston-Leeston Rd). Without geographical coordinates, millions of records stored in museum collections around the world cannot be mapped. We present a method for predicting the distance and direction associated with human language location descriptions which focuses on the interpretation of geospatial prepositions and the way in which they modify the location represented by an associated reference place name (e.g. near the Manawatu River). We study eight distance-oriented prepositions and eight direction-oriented prepositions and use machine learning regression to predict distance or direction, relative to the reference place name, from a collection of training data. The results show that, compared with a simple baseline, our model improved distance predictions by up to 60% and direction predictions by up to 31%.
  • Thumbnail Image
    Item
    The Koja Web Mapping Application for Context-sensitive Natural Language Spatial Querying
    (CEUR Team, 2023-01-01) Aflaki N; Stock K; Jones CB; Guesgen H; Fukuzawa Y; Morley J; Hu X; Hu Y; Resch B; Kersten J; Stock K
    The locations of objects are often described in natural language relative to some other object using vague and context-sensitive spatial relation terms (e.g. theatre near Trafalgar Square). Koja is a web map application that predicts the distance between a location and reference object based on the spatial relation term specified by the user and language and contextual features. That distance is used to retrieve objects of the specified type within a range of the distance. They are displayed through a map interface to make the process more intuitive and user-friendly.
  • Thumbnail Image
    Item
    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.