Disambiguating fine-grained place names from descriptions by clustering (original) (raw)

2018, ArXiv

Everyday place descriptions often contain place names of fine-grained features, such as buildings or businesses, that are more difficult to disambiguate than names referring to larger places, for example cities or natural geographic features. Fine-grained places are often significantly more frequent and more similar to each other, and disambiguation heuristics developed for larger places, such as those based on population or containment relationships, are often not applicable in these cases. In this research, we address the disambiguation of fine-grained place names from everyday place descriptions. For this purpose, we evaluate the performance of different existing clustering-based approaches, since clustering approaches require no more knowledge other than the locations of ambiguous place names. We consider not only approaches developed specifically for place name disambiguation, but also clustering algorithms developed for general data mining that could potentially be leveraged. ...

Place disambiguation with co-occurrence models

CLEF 2006 Workshop, Working …, 2006

In this paper we describe the geographic information retrieval system developed by the Multimedia & Information Systems team for GeoCLEF 2006 and the results achieved. We detail our methods for generating and applying co-occurrence models for the purpose of place name disambiguation, our use of named entity recognition tools and text indexing applications. The presented system is split into two stages: a batch text & geographic indexer and a real time query engine. The query engine takes manually crafted queries where the text component is separated from the geographic component. Two monolingual runs were submitted for the GeoCLEF evaluation, the first constructed from the title and description, the second included the narrative also. We explain in detail our use of co-occurrence models for place name disambiguation using a model generated from Wikipedia. The paper concludes with a full description of future work and ways in which the system could be optimised.

Geo-referencing Place from Everyday Natural Language Descriptions

ArXiv, 2017

Natural language place descriptions in everyday communication provide a rich source of spatial knowledge about places. An important step to utilize such knowledge in information systems is geo-referencing all the places referred to in these descriptions. Current techniques for geo-referencing places from text documents are using place name recognition and disambiguation; however, place descriptions often contain place references that are not known by gazetteers, or that are expressed in other, more flexible ways. Hence, the approach for geo-referencing presented in this paper starts from a place graph that contains the place references as well as spatial relationships extracted from place descriptions. Spatial relationships are used to constrain the locations of places and allow the later best-matching process for geo-referencing. The novel geo-referencing process results in higher precision and recall compared to state-of-art toponym resolution approaches on several tested place de...

On metonymy recognition for geographic information retrieval

International Journal of Geographical Information Science, 2007

Metonymically used location names (toponyms) refer to other, related entities and thus possess a meaning different from their literal, geographic sense. Metonymic uses are to be treated differently to improve the performance of geographic information retrieval (GIR). Statistics on toponym senses show that 75.06% of all location names are used in their literal sense, 17.05% are used metonymically, and 7.89% have a mixed sense. This article presents a method for disambiguating location names in texts between literal and metonymic senses, based on shallow features.The evaluation of this method is two‐fold. First, we use a memory‐based learner (TiMBL) to train a classifier and determine standard evaluation measures such as F‐score and accuracy. The classifier achieved an F‐score of 0.842 and an accuracy of 0.846 for identifying toponym senses in a subset of the CoNLL (Conference on Natural Language Learning) data.Second, we perform retrieval experiments based on the GeoCLEF data (newspaper article corpus and queries) from 2005 and 2006. We compare searching location names in a database index containing both their literal and metonymic senses with searching in an index containing their literal senses only. Evaluation results indicate that removing metonymic senses from the index yields a higher mean average precision (MAP) for GIR. In total, we observed a significant gain in MAP: an increase from 0.0704 to 0.0715 MAP for the GeoCLEF 2005 data, and an increase from 0.1944 to 0.2100 MAP for the GeoCLEF 2006 data.

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