Automatic compilation of travel information from automatically identified travel blogs

Abstract

In this paper, we propose a method for compiling travel information automatically. For the compilation, we focus on travel blogs, which are defined as travel journals written by bloggers in diary form. We consider that travel blogs are a useful information source for obtaining travel information, because many bloggers' travel experiences are written in this form. Therefore, we identified travel blogs in a blog database and extracted travel information from them. We have confirmed the effectiveness of our method by experiment. For the identification of travel blogs, we obtained scores of 38.1% for Recall and 86.7% for Precision. In the extraction of travel information from travel blogs, we obtained 74.0% for Precision at the top 100 extracted local products, thereby confirming that travel blogs are a useful source of travel information.

Automatic Compilation of Travel Information from Texts: A Survey

“…Ishino et al [15] proposed a method to extract people's transportation information from automatically identified travel blogs written in Japanese [25]. They used machine learning to extract information, such as "departure place", "destination", or "transportation device", from travel blog entries.…”

“…Tsai and Chou [32] also proposed a method for extracting dish names from restaurant review blogs written in Chinese using a machine learning (CRF) technique. In the following, we explain the detail of the bootstrapping-based and machine learning-based information extraction approaches based on Nanba's work [25]. Nanba et al extracted pairs comprising a location name and a local product from travel blogs written in Japanese, which were identified using the method described in Section 2.1.…”

“…However, there are many more travel blogs in the blogosphere, beyond these portal sites. In an attempt to construct an exhaustive database of travel blogs, Nanba et al [25] identified travel blog entries written in Japanese in a blog database. 2 Blog entries that contain cue phrases, such as "travel", "sightseeing", or "tour", have a high degree of probability of being travel blogs.…”

“…For the machine learning method, they used CRF [20], in the same way as Nanba et al [25], which we mentioned in Section 2.2. The CRF-based method identifies the class of each entry.…”

A Comparison of Statistical and Data Mining Techniques for Enrichment Ontology with Instances

“…Nanba et al [8] applied NLP and used CRF as ML in order to identify travel blog, and extract travel information relating to the relationships between location names and local products. Carlson et al [9], Giuliano and Gliozo [10], Cimiano et al [11] and Etizioni et al [12] applied the NLP, IE and ML techniques to the ontology population.…”