Improving Toponym Resolution with Better Candidate Generation, Transformer-based Reranking, and Two-Stage Resolution (2023)

Abstract: Geocoding is the task of converting location mentions in text into structured data that encodes the geospatial semantics. We propose a new architecture for geocoding, GeoNorm. GeoNorm first uses information retrieval techniques to generate a list of candidate entries from the geospatial ontology. Then it reranks the candidate entries using a transformer-based neural network that incorporates information from the ontology such as the entry’s population. This generate-and-rerank process is applied twice: first to resolve the less ambiguous countries, states, and counties, and second to resolve the remaining location mentions, using the identified countries, states, and counties as context. Our proposed toponym resolution framework achieves state-of-the-art performance on multiple datasets. Code and models are available at \url{https://github.com/clulab/geonorm}.

## introduction geospatial information extraction is a type of semantic extraction that plays a critical role in tasks such as geographical document classification and retrieval @xcite , historical event analysis based on location data @xcite , tracking the evolution and emergence of infectious diseases @xcite , and disaster response mechanisms @xcite . such information extraction can be challenging because different geographical locations can be referred to by the same place name (e.g., san jose in costa rica vs. san jose in california, usa), and different place names can refer to the same geographical location (e.g., leeuwarden and ljouwert are two names for the same city in the netherlands). it is thus critical to resolve these place names by linking them with their corresponding coordinates from a geospatial ontology or knowledge base. geocoding, also called toponym resolution or toponym disambiguation, is the subtask of geoparsing that disambiguates place names (known as toponyms) in text. given a textual mention of a location, a geocoder chooses the corresponding geospatial coordinates, geospatial polygon, or entry in a geospatial database. approaches to geocoding include generate-and-rank systems that first use information retrieval systems to generate candidate entries and then rerank them with handengineered heuristics and/or supervised classifiers (e.g., @xcite @xcite , vector-space systems that use deep neural networks to encode place names and database entries as vectors and measure their similarity (e.g., @xcite , and tile-classification systems that use deep neural networks to directly predict small tiles of the map rather than ontology entries (e.g., @xcite @xcite . the deep neural network tile-classification approaches have been the most successful, but they do not naturally produce an ontology entry, which contains semantic metadata needed by users. we propose a new architecture, geonorm, shown in figure 1 , which builds on all of these lines of research: it uses pre-trained deep neural networks for the improved robustness in matching place names, while leveraging a generate-then-rank architecture to produce ontology entries as output. it couples this generate-and-rank process with a two-stage approach that first resolves the less ambiguous countries, states, and counties, and then resolves the remaining location mentions, using the identified countries, states, and counties as context. our work makes the following contributions: ## related work the current work focuses on mention-level geocoding. related tasks include document-level geocoding and geotagging. document-level geocoding takes as input an entire text and produces as output a location from a geospatial ontology, as in geolocating twitter users or microblog posts @xcite @xcite @xcite @xcite and geographic document retrieval and classification @xcite . geotagging takes as input an entire text and produces as output a list of location phrases @xcite . mention-level geocoding, the focus of the current article, takes as input location phrases from a text and produces as output their corresponding locations in a geospatial ontology. this is related to the task of linking phrases to wikipedia, though geospatial ontologies do not have full text articles for each of their concepts, which are required for training many recent wikipedia linking approaches (e.g., @xcite . early systems for mention-level geocoding used hand-crafted rules and heuristics to predict geospatial labels for place names: edinburgh geoparser @xcite , @xcite , lieberman et al. (2010), lieberman and samet (2011), clavin (berico technologies, 2012 ), geotxt @xcite , and @xcite . the most common features and heuristics were based on string matching, population count, and type of place (city, country, etc.). later geocoding systems used heuristics of rule-based systems as features in supervised machine learning models, including logistic regression (wistr, @xcite , support vector machines @xcite , random forests @xcite , stacked light-gbms (dm_nlp, @xcite and other statistical learning methods @xcite . these systems typically applied a generatethen-rerank framework: the mention text is used to query an information retrieval index of the geospatial ontology and produce candidate ontology entries, then a supervised machine-learning model reranks the candidates using additional features. some deep learning models approach geocoding as a vector-space problem. both the mention text and ontology entries are converted into vectors, and vector similarity is used to select the most appropriate ontology entry for each mention @xcite . such approaches should allow more flexible matching of mentions to concepts, but we find that simple information retrieval techniques outperform these models. other deep learning models approach geocoding as a classification problem by dividing the earth's surface into an n ×n grid of tiles. place names and their features are mapped to one of these tiles using convolutional @xcite or recurrent neural networks @xcite . such approaches can flexibly match mentions to concepts and can also incorporate textual context, but do not naturally produce ontology entries, which contain semantic metadata needed by users. our proposed approach combines the tight ontology integration of the generate-and-rerank systems with the robust text and context encoding of the deep neural network classifiers. ## proposed methods we define the task of toponym resolution as follows. we are given an ontology or knowledge base with a set of entries @xmath0, e 2 , ..., e |e| }. each input is a text made up of sentences @xmath1, t 2 , . . . , t |t | } and a list of location mentions @xmath2, m 2 , ..., m |m | } in the text. the goal is to find a mapping function f (m i ) = e j that maps each location mention in the text to its corresponding entry in the ontology. we approach toponym resolution using a candidate generator followed by a candidate reranker. the candidate generator, g(m, e) → e m , takes a mention m and ontology e as input, and generates a list of candidate entries e m , where e m ⊆ e and |e m | ≪ |e|. as the candidate generator must search a large ontology and produce only a short list of candidates, the goal for g will be high recall and high runtime efficiency. the candidate reranker, r(m, e m ) → e m , takes a mention m and the list of candidate ontology entries e m , and sorts them by their relevance or importance to produce a new list, e m . as the candidate ranker needs to work only with a short list of candidates, the goal for r will be high precision, especially at rank 1, with less of a focus on runtime efficiency. ## results we first evaluate our context-free candidate generator, comparing it to recent context-free candidate generators. ## qualitative analysis ## limitations geonorm's candidate generator is based on information retrieval. this is efficient but not very flexible in string matching, and when the candidate generator fails to produce the correct candidate entry, the candidate reranker also necessarily fails. for example, as ## conclusion we propose a new toponym resolution architecture, geonorm, that combines the tight ontology integration of generate-and-rerank systems with the robust text encoding of deep neural networks. geonorm consists of an information retrieval-based candidate generator, a bert-based reranker that incorporates features important to toponym resolution such as population and type of location, and a novel two-stage resolution strategy that incorporates document-level context. we evaluate our proposed architecture against prior state-of-theart, using multiple evaluation metrics and multiple datasets. geonorm achieves new state-of-the-art performance on all datasets.