On metonymy recognition for geographic information retrieval

Leveling, Johannes; Hartrumpf, Sven

doi:10.1080/13658810701626244

Cited by 23 publications

(17 citation statements)

References 7 publications

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“…Confirming results from Leveling and Hartrumpf for annotated German newspaper texts [2], location names in the documents are often used in a non-geographic sense and locations can be referred to by means other than proper nouns. Examples of non-literal senses include metonymic use of location names (e.g.…”

Section: Topic and Document Annotationmentioning

confidence: 80%

Unnamed locations, underspecified regions, and other linguistic phenomena in geographic annotation of water-based locations

Leveling

2010

Proceedings of the 6th Workshop on Geographic Information Retrieval

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This short paper investigates how locations in or close to water masses in topics and documents (e.g. rivers, seas, oceans) are referred to. For this study, 13 topics from the GeoCLEF topics 2005-2008 aiming at documents on rivers, oceans, or sea names were selected and the corresponding relevant documents retrieved and manually annotated.Results of the geographic annotation indicate that i) topics aiming at locations close to water contain a wide variety of spatial relations (indicated by different prepositions), ii) unnamed locations can be generated on-the-fly by referring to movable objects (e.g. ships, planes) travelling along a path, iii) underspecified regions are referenced by proximity or distance or directional relations. In addition, several generic expressions (e.g. "in international waters") are frequently used, but refer to different underspecified regions.

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Section: Topic and Document Annotationmentioning

confidence: 80%

Unnamed locations, underspecified regions, and other linguistic phenomena in geographic annotation of water-based locations

Leveling

2010

Proceedings of the 6th Workshop on Geographic Information Retrieval

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“…On the textual level, TiMBL has been used for information extraction (Zavrel, Berck, and Lavrijssen, 2000;Zavrel and Daelemans, 2003;Ahn, 2006), text classification (Spitters, 2000), question classification (Cumbreras, López, and Santiago, 2006;Dridan and Baldwin, 2007), spam filtering (Androutsopoulos et al, 2000), named-entity recognition Hendrickx and Van den Bosch, 2003;De Meulder and Daelemans, 2003;Sporleder et al, 2006a;Leveling and Hartrumpf, 2007), and error detection in textual databases (Sporleder et al, 2006b). …”

Section: Nlp Applications Of Timblmentioning

confidence: 99%

Computational Approaches to Morphology

Keuleers

2018

Oxford Research Encyclopedia of Linguistics

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Computational psycholinguistics has a long history of investigation and modeling of morphological phenomena. Several computational models have been developed to deal with the processing and production of morphologically complex forms and with the relation between linguistic morphology and psychological word representations. Historically, most of this work has focused on modeling the production of inflected word forms, leading to the development of models based on connectionist principles and other data-driven models such as Memory-Based Language Processing (MBLP), Analogical Modeling of Language (AM), and Minimal Generalization Learning (MGL). In the context of inflectional morphology, these computational approaches have played an important role in the debate between single and dual mechanism theories of cognition. Taking a different angle, computational models based on distributional semantics have been proposed to account for several phenomena in morphological processing and composition. Finally, although several computational models of reading have been developed in psycholinguistics, none of them have satisfactorily addressed the recognition and reading aloud of morphologically complex forms.

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“…For wake-sleep algorithms, the energy equation and Gibbs sampling approach are used to calculate the descent gradient. The partial derivative is computed as follows [48,49]:…”

Section: B Transfer Parametersmentioning

confidence: 99%

Deep Belief Networks Based Toponym Recognition for Chinese Text

Wang

Zhang

et al. 2018

IJGI

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Abstract:In Geographical Information Systems, geo-coding is used for the task of mapping from implicitly geo-referenced data to explicitly geo-referenced coordinates. At present, an enormous amount of implicitly geo-referenced information is hidden in unstructured text, e.g., Wikipedia, social data and news. Toponym recognition is the foundation of mining this useful geo-referenced information by identifying words as toponyms in text. In this paper, we propose an adapted toponym recognition approach based on deep belief network (DBN) by exploring two key issues: word representation and model interpretation. A Skip-Gram model is used in the word representation process to represent words with contextual information that are ignored by current word representation models. We then determine the core hyper-parameters of the DBN model by illustrating the relationship between the performance and the hyper-parameters, e.g., vector dimensionality, DBN structures and probability thresholds. The experiments evaluate the performance of the Skip-Gram model implemented by the Word2Vec open-source tool, determine stable hyper-parameters and compare our approach with a conditional random field (CRF) based approach. The experimental results show that the DBN model outperforms the CRF model with smaller corpus. When the corpus size is large enough, their statistical metrics become approaching. However, their recognition results express differences and complementarity on different kinds of toponyms. More importantly, combining their results can directly improve the performance of toponym recognition relative to their individual performances. It seems that the scale of the corpus has an obvious effect on the performance of toponym recognition. Generally, there is no adequate tagged corpus on specific toponym recognition tasks, especially in the era of Big Data. In conclusion, we believe that the DBN-based approach is a promising and powerful method to extract geo-referenced information from text in the future.

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On metonymy recognition for geographic information retrieval

Cited by 23 publications

References 7 publications

Unnamed locations, underspecified regions, and other linguistic phenomena in geographic annotation of water-based locations

Unnamed locations, underspecified regions, and other linguistic phenomena in geographic annotation of water-based locations

Computational Approaches to Morphology

Deep Belief Networks Based Toponym Recognition for Chinese Text

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