The Details Exploration of Intangible Cultural Heritage From the Perspective of Cultural Tourism Industry: A Case Study of Hohhot City in China

the intangible cultural heritage is an important part of the cultural tourism industry marketing.As the cultural element ,the intangible cultural heritage can impel the main cultural tourism industry become more diversified and more extensively involved of public.Intangible cultural heritage can enhance the experience of tourists in culture . The intangible cultural heritage of Hohhot has an important value ,. keeping up with the times and has gone through long history, it is synchronic,all this can increase the value of tourism destination by explorating the innovation of intangible cultural heritage.With the help of real drama culture, the local brand value of intangible cultural heritage can be created. And it can realize the endorsement value of the people speaker.  This article explores the intangible cultural heritage from the perspective of cultural tourism in order to promote the tourism development in Hohhot.

GeoLM: Empowering Language Models for Geospatially Grounded Language Understanding

Humans subconsciously engage in geospatial reasoning when reading articles. We recognize place names and their spatial relations in text and mentally associate them with their physical locations on Earth. Although pretrained language models can mimic this cognitive process using linguistic context, they do not utilize valuable geospatial information in large, widely available geographical databases, e.g., OpenStreetMap. This paper introduces GeoLM, a geospatially grounded language model that enhances the understanding of geo-entities in natural language. GeoLM leverages geo-entity mentions as anchors to connect linguistic information in text corpora with geospatial information extracted from geographical databases. GeoLM connects the two types of context through contrastive learning and masked language modeling. It also incorporates a spatial coordinate embedding mechanism to encode distance and direction relations to capture geospatial context. In the experiment, we demonstrate that GeoLM exhibits promising capabilities in supporting toponym recognition, toponym linking, relation extraction, and geo-entity typing, which bridge the gap between natural language processing and geospatial sciences. The code is publicly available at https://github.com/knowledge-computing/geolm.Comment: Accepted to EMNLP23 mai