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Automatic Temporal Relation Extraction of Persian Texts
, M.Sc. Thesis Sharif University of Technology ; GhassemSani, Gholamreza (Supervisor)
Abstract
Temporal relation extraction is one of the challenging research topics in natural language processing semantic level. The purpose of this kind of extraction is to find the temporal ordering between text events so that they can be used in various applications such as question answering and summarization systems.Most of early researches in temporal relation extractionaimed at finding a number of rules and templates for every single temporal relation in English texts. However, with the availability of temporal corpora in English and some other natural languages like Chinese, Korean, Italian, etc., the research trend in this field turned towards the use of machine learning methods. Accordingly,...
Towards unsupervised learning of temporal relations between events
, Article Journal of Artificial Intelligence Research ; Volume 45 , 2012 , Pages 125-163 ; 10769757 (ISSN) ; Ghassem Sani, G ; Sharif University of Technology
2012
Abstract
Automatic extraction of temporal relations between event pairs is an important task for several natural language processing applications such as Question Answering, Information Extraction, and Summarization. Since most existing methods are supervised and require large corpora, which for many languages do not exist, we have concentrated our efforts to reduce the need for annotated data as much as possible. This paper presents two different algorithms towards this goal. The first algorithm is a weakly supervised machine learning approach for classification of temporal relations between events. In the first stage, the algorithm learns a general classifier from an annotated corpus. Then,...
Automatic extraction of is-a relations in taxonomy learning
, Article 13th International Computer Society of Iran Computer Conference on Advances in Computer Science and Engineering, CSICC 2008, Kish Island, 9 March 2008 through 11 March 2008 ; Volume 6 CCIS , 2008 , Pages 17-24 ; 18650929 (ISSN); 3540899847 (ISBN); 9783540899846 (ISBN) ; Abolhassani, H ; Fatemi, H ; Sharif University of Technology
2008
Abstract
Taxonomy learning is a prerequisite step for ontology learning. In order to create a taxonomy, first of all, existing 'is-a' relations between words should be extracted. A known way to extract 'is-a' relations is finding lexicosyntactic patterns in large text corpus. Although this approach produces results with high precision but it suffers from low values of recall. Furthermore developing a comprehensive set of patterns is tedious and cumbersome. In this paper, firstly, we introduce an approach for developing lexico-syntactic patterns automatically using the snippets of search engine results and then, challenge the law recall of this approach using a combined model, which is based on...
Towards Unsupervised Temporal Relation Extraction Between Events
,
M.Sc. Thesis
Sharif University of Technology
;
Ghassem-Sani, Gholamreza
(Supervisor)
Abstract
Temporal relation classification is one of the contemporary demanding tasks in natural language processing. This task can be used in various applications such as question answering, summarization, and language specific information retrieval. Temporal relation classification methods can be categorized into three main groups of supervised, semi-supervised, and unsupervised (based on the type of the training data that they need). In this thesis, we have two main goals: first, improving accuracy of temporal relation learning, and second, decreasing supervision of algorithm as much as possible. For achieving these goals, three main steps are proposed. In the first step, we propose an improved...