  Tourism information is scattered around nowadays. To search for the
information, it is usually time consuming to browse through the results from
search engine, select and view the details of each accommodation. In this
paper, we present a methodology to extract particular information from full
text returned from the search engine to facilitate the users. Then, the users
can specifically look to the desired relevant information. The approach can be
used for the same task in other domains. The main steps are 1) building
training data and 2) building recognition model. First, the tourism data is
gathered and the vocabularies are built. The raw corpus is used to train for
creating vocabulary embedding. Also, it is used for creating annotated data.
The process of creating named entity annotation is presented. Then, the
recognition model of a given entity type can be built. From the experiments,
given hotel description, the model can extract the desired entity,i.e, name,
location, facility. The extracted data can further be stored as a structured
information, e.g., in the ontology format, for future querying and inference.
The model for automatic named entity identification, based on machine learning,
yields the error ranging 8%-25%.
