A paper entitled Things and Strings: Improving Place Name Disambiguation from Short Texts by Combining Entity Co-Occurrence with Topic Modeling by Yiting Ju, Benjamin Adams, Krzysztof Janowicz, Yingjie Hu, Bo Yan and Grant McKenzie was accepted at the 20th International Conference on Knowledge Engineering and Knowledge Management (EKAW 2016), which will takes place from November 19th, 2016 to November 24th, 2016 in Bologna, Italy.
Abstract: Place name disambiguation is the task of correctly identifying a place from a set of places sharing a common name. It contributes to tasks such as knowledge extraction, query answering, geographic information retrieval, and automatic tagging. Disambiguation quality relies on the ability to correctly identify and interpret contextual clues, complicating the task for short texts. Here we propose a novel approach to the disambiguation of place names from short texts that integrates two models: entity co-occurrence and topic modeling. The first model uses Linked Data to identify related entities to improve disambiguation quality. The second model uses topic modeling to differentiate places based on the terms used to describe them. We evaluate our approach using a corpus of short texts, determine the suitable weight between models, and demonstrate that a combined model outperforms benchmark systems such as DBpedia Spotlight and Open Calais in terms of F1-score and Mean Reciprocal Rank.
In this work we proposed a novel approach to tackle the challenging task of disambiguating place names from short texts. We combine a things-based perspective with a strings-based perspective, integrating two models, namely an entity-based co-occurrence model and a topic-based model.
Keywords: Place name disambiguation; natural language processing; LDA; Wikipedia; DBpedia; Linked Data