Temporal Relation Extraction Using Expectation Maximization

Proceedings of Recent Advances in Natural Language Processing, pages 218–225, Hissar, Bulgaria, 12-14 September 2011. Temporal Relation Extraction Using Expectation Maximization Seyed Abolghasem Mirroshandel Sharif University or Technology, Iran mirroshandel@ce.sharif.edu Gholamreza Ghassem-Sani Sharif University or Technology, Iran sani@sharif.edu Abstract The ability to accurately determine temporal relations between events is an important task for several natural language processing applications such as Question Answering, Summarization, and Information Extraction. Since current supervised methods require large corpora, which for many languages do not exist, we have focused our attention on approaches with less supervision as much as possible. This paper presents a fully generative model for temporal relation extraction based on the expectation maximization (EM) algorithm. Our experiments show that the performance of the proposed algorithm, regarding its little supervision, is considerable in temporal relation learning. 1 Introduction Lately, the increasing attention to the practical NLP applications such as question answering, information extraction, and summarization have resulted in a growing demand of temporal information processing (Tatu and Srikanth, 2008). In question answering, one may expect the system to answer questions such as “when an event occurred”, or “what is the chronological order of some desired events”. In text summarization, especially in the multi-document type, knowing the order of events is a useful source of correctly merging related information. Unlike problems such as part-of-speech tagging, morphological analysis, parsing, and named entity recognition which have been recently addressed with satisfactory results by combining statistical and symbolic methods (Mani et al., 2006), temporal relation extraction that requires deeper semantic analysis are yet to be worked on. One of recent efforts has disclosed that this task is a complicated task, even for human annotators (Mani et al., 2006). Based on the type of corpora that different temporal relation learning methods use, these methods are divided into three major categories: supervised, semi-supervised, and unsupervised. Supervised methods normally rely on the correct temporal relations of training sentences of a manually tagged corpus. Semi-supervised methods often rely on a partially tagged corpus and need less supervision. Finally, unsupervised methods rely only on raw sentences without any temporal relation annotation. It is obvious that producing the necessary training data (corpora) of supervised and to a less extent semi- supervised methods is a time consuming, hard, and expensive work. Besides, it is very difficult to adapt such methods for new tasks, languages, and/or domains. Consequently, it is in fact the corpus availability that directs the research in this area. For mentioned reasons, we have focused on unsupervised and weakly supervised temporal relation learning. This paper presents a novel usage of expectation maximization (EM) algorithm for temporal relation learning. The algorithm also employs Allen's interval algebra (Allen, 1984). Our experiments show that the performance of the proposed algorithm is acceptable with respect to little usage of tagged corpora which is used. The remainder of the paper is organized as follows: section 2 is about previous works on temporal relation extraction. Section 3 explains our proposed method. Section 4 briefly presents the characteristic of the …

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