This paper proposes an integrated method for extracting the time information of events from Japanese news archives. We first utilize a new pattern-based method named TRE/ERT combined with a neural-based model to extract all temporal expressions possibly related with an event. Then, we apply a simple but efficient clustering and narrowing process to summarize these temporal expressions into a small time frame for events lasting shorter than a day, or time frames for the beginning and the end days of the events for events spanning multiple days. We conducted two experiments where the results show that when working with one-day events, our system has a precision high up to 57% and the rate that the actual date of the event falls in our extracted time frame reaches 100% as long as the event name is found in the archive. The results also show that our system works with multiple-day events, but needs further improvements to get better results.

In this paper, we explore and discuss a way to extract temporal information from natural language texts. The suggested method is divided into two parts: temporal expression recognition and temporal value inference. The former employs the conventional NER approach, using a BiLSTM-CRF architecture. The latter is implemented with a rule-based algorithm, which can be further developed in later work for better coverage of various temporal expressions. In terms of the corpus, we have selected 200 articles from one of the major Japanese newspaper companies to create an annotated corpus, classifying temporal expressions into five different types. As for the performance, we have achieved 0.866 in F-measure for the recognition of temporal expressions and 0.920 in accuracy for the inference of the absolute temporal values of the expressions. Combining the two modules and running them as an end-to-end system, we have attained 0.891 of F-measure.

In this paper, we report our experience in constructing a cooking recipe text corpus. We describe problems we found and explain how we managed them. One of the problems we faced in the construction of our recipe corpus is the difficulty of establishing a clear, stable, and complete guideline instructing annotators how to annotate. During the annotation, we found many unexpected cases for which the pre-defined guideline is not clear enough, and even cases for which the pre-defined guideline provides no guidance at all. As a result, we needed to update the guideline twice during the annotation, and also needed to revise annotations we have done before the updates. During that process, we have several trade-offs, and it is not easy to decide when and how often we should revise the annotations. It is even unclear whether we should revise them or should instead use the human resource for annotating more data. We show an experiment, whose result suggests that we should revise the old annotations. Another problem we had is the management of versions of the guideline, sets of annotations corresponding to them, and communication between participants.

There are many databases of game records available online. In order to retrieve a game state from such a database, users usually need to specify the target state in a domain-specific language, which may be difficult to learn for novice users. In this work, we propose a search system that allows users to retrieve game states from a game record database by using keywords. In our approach, we first train a neural network model for symbol grounding using a small number of pairs of a game state and a commentary on it. We then apply it to all the states in the database to associate each of them with characteristic terms and their scores. The enhanced database thus enables users to search for a state using keywords. To evaluate the performance of the proposed method, we conducted experiments of game state retrieval using game records of Shogi (Japanese chess) with commentaries. The results demonstrate that our approach gives significantly better results than full-text search and an LSTM language model.