A Method for Extraction of Future Reference Sentences Based on Semantic Role Labeling

Abstract

SUMMARYIn everyday life, people use past events and their own knowledge in predicting probable unfolding of events. To obtain the necessary knowledge for such predictions, newspapers and the Internet provide a general source of information. Newspapers contain various expressions describing past events, but also current and future events, and opinions. In our research we focused on automatically obtaining sentences that make reference to the future. Such sentences can contain expressions that not only explicitly refer to future events, but could also refer to past or current events. For example, if people read a news article that states "In the near future, there will be an upward trend in the price of gasoline," they may be likely to buy gasoline now. However, if the article says "The cost of gasoline has just risen 10 yen per liter," people will not rush to buy gasoline, because they accept this as reality and may expect the cost to decrease in the future. In the following study we firstly investigate future reference sentences in newspapers and Web news. Next, we propose a method for automatic extraction of such sentences by using semantic role labels, without typical approaches (temporal expressions, etc.). In a series of experiments, we extract semantic role patterns from future reference sentences and examine the validity of the extracted patterns in classification of future reference sentences. key words: natural language processing, future reference expressions, future prediction, information extraction

English Semantic Recognition Based on Intelligent Algorithm

“…In the current research, semantic annotation and recognition are of great help to disambiguation and role understanding [10]. Among them, semantic role annotation (SRL) [11] is an important content, which can realize shallow semantic analysis and identify the labels of argument, agent, patient, etc. in a sentence.…”