A Simple Text-based Relevant Location Prediction Method using Knowledge Base (2020)

Abstract: In this paper, we propose a simple method to predict salient locations from news article text using a knowledge base (KB). The proposed method uses a dictionary of locations created from the KB to identify occurrences of locations in the text and uses the hierarchical information between entities in the KB for assigning appropriate saliency scores to regions. It allows prediction at arbitrary region units and has only a few hyperparameters that need to be tuned. We show using manually annotated news articles that the proposed method improves the f-measure by > 0.12 compared to multiple baselines.

## introduction predicting relevant locations from news articles can result in numerous useful applications. for example, it enables the delivery of news related to a specific city that is of user interest, or facilitates the prediction of a disease outbreak in a specific region when used with event detection techniques. in this paper, we focus on predicting relevant locations from news articles. the goal of this task is to identify locations that are salient to the article, not those that simply appeared in the article. for example, consider the following excerpt: "the aoi festival is one of the three major festivals in kyoto. it originated as a series of rites to calm down angry gods. a visitor from australia said..." in this example, the phrase "kyoto" is highly relevant to the article, but "australia" is not. traditional methods to predict locations require specific data, such as a training dataset or phrase distribution, that match the application domain and the granularity of the prediction. however, it is costly to prepare such data for individual applications. we propose a knowledge base (kb)-based method that only requires a general-purpose kb instead of a labeled dataset for training. it propagates phrase-level importance to region entities following their relationship in the kb. it can theoretically be applied to predictions at an arbitrary level of granularity (e.g. countries, prefectures, cities) without dedicated training data. in this study, we focus on japanese news articles and report the performance of predictions at the japanese prefecture-level. we provide practical tips to tackle un-tokenized language like japanese. ## related works depending on the objective, geolocation prediction tasks from texts are roughly divided into two types. one type is for detecting and identifying mentions of points-of-interest (pois) in the text, well known by entity linking @xcite . this task focuses on extracting all mentions regardless of their saliency. the other type is for estimating the author's current location or home town from his or her posts @xcite . it is mainly performed to complement user profiles in services that deal with user-generated content (e.g. sns). in this case, in addition to text, various user metadata such as the relationship between authors is available @xcite . our work is similar to the former type in terms of purpose, but we focus on identifying salient regions rather than extracting all of the individual pois. there are two main approaches to predicting location. one is the dictionary-based approach @xcite @xcite , where dictionaries of location indicative words are created in advance and used for the prediction. in addition to explicit region names, the choice of which words to add to the dictionary is a hot topic of discussion @xcite . the other is the machine learning(ml)-based ap-proach @xcite . methods based on this approach usually perform well if sufficient training data is available. however, in practice, it is difficult to prepare data whose granularity matches the requirements of the application. in particular, estimating regions that are rarely found in the training data is one of the weaknesses of machine learning. ## conclusion in this paper, we presented a simple kb-based method to predict relevant locations from articles. the proposed method requires no training data or maintenance of a dictionary thanks to a freshly generated kb, and it can be used to make predictions at an arbitrary level of granularity, as long as the corresponding data is present in the kb. we demonstrated the effectiveness of this method at predicting salient japanese prefectures using manually annotated articles. in future work, we plan to make location predictions at the city-level and evaluate its performance.