Opinion mining is a well-known problem in natural language processing that has attracted increasing attention in recent years. Existing approaches are mainly limited to the identification of direct opinions and are mostly dedicated to explicit opinions. However, in some domains such as medical, the opinions about an entity are not usually expressed by opinion words directly, but they are expressed indirectly by describing the effect of that entity on other ones. Therefore, ignoring indirect opinions can lead to the loss of valuable information and noticeable decline in overall accuracy of opinion mining systems. In this paper, we first introduce the task of indirect opinion mining. Then, we present a novel approach to construct a knowledge base of indirect opinions, called OpinionKB, which aims to be a resource for automatically classifying people’s opinions about drugs. Using our approach, we have extracted 896 quadruples of indirect opinions at a precision of 88.08 percent. Furthermore, experiments on drug reviews demonstrate that our approach can achieve 85.25 percent precision in polarity detection task, and outperforms the state-of-the-art opinion mining methods. We also build a corpus of indirect opinions about drugs, which can be used as a basis for supervised indirect opinion mining. The proposed approach for corpus construction achieves the precision of 88.42 percent.
Polarity classification is the main subtask of sentiment analysis and opinion mining, well-known problems in natural language processing that have attracted increasing attention in recent years. Existing approaches mainly rely on the subjective part of text in which sentiment is expressed explicitly through specific words, called sentiment words. These approaches, however, are still far from being good in the polarity classification of patients' experiences since they are often expressed without any explicit expression of sentiment, but an undesirable or desirable effect of the experience implicitly indicates a positive or negative sentiment. This paper presents a method for polarity classification of patients' experiences of drugs using domain knowledge. We first build a knowledge base of polar facts about drugs, called FactNet, using extracted patterns from Linked Data sources and relation extraction techniques. Then, we extract generalized semantic patterns of polar facts and organize them into a hierarchy in order to overcome the missing knowledge issue. Finally, we apply the extracted knowledge, i.e., polar fact instances and generalized patterns, for the polarity classification task. Different from previous approaches for personal experience classification, the proposed method explores the potential benefits of polar facts in domain knowledge aiming to improve the polarity classification performance, especially in the case of indirect implicit experiences, i.e., experiences which express the effect of one entity on other ones without any sentiment words. Using our approach, we have extracted 9703 triplets of polar facts at a precision of 92.26 percent. In addition, experiments on drug reviews demonstrate that our approach can achieve 79.78 percent precision in polarity classification task, and outperforms the state-of-the-art sentiment analysis and opinion mining methods.
In this paper, a hybrid algorithm based on modified intelligent water drops algorithm and learning automata for solving Steiner tree problem is proposed. Since the Steiner tree problem is NP-hard, the aim of this paper is to design an algorithm to construct high quality Steiner trees in a short time which are suitable for real time multicast routing in networks. The global search and fast convergence ability of the intelligent water drops algorithm make it efficient to the problem. To achieve better results, we used learning automata for adjusting IWD parameters. IWD has several parameters. The appropriate selections of these parameters have large effects on the performance and convergence of the algorithm. Experimental results on the OR-library test cases show that the proposed algorithm outperforms traditional heuristic algorithms and other iteration based algorithms with faster convergence speed. General Terms
Abtract-In the Persian language, an Ezafe construction is a linking element which joins the head of a phrase to its modifiers. The Ezafe in its simplest form is pronounced as-e, but generally not indicated in writing. Determining the position of an Ezafe is advantageous for disambiguating the boundary of the syntactic phrases which is a fundamental task in most natural language processing applications. This paper introduces a framework for combining genetic algorithms with rule-based models that brings the advantages of both approaches and overcomes their problems. This framework was used for recognizing the position of Ezafe constructions in Persian written texts. At the first stage, the rulebased model was applied to tag some tokens of an input sentence. Then, in the second stage, the search capabilities of the genetic algorithm were used to assign the Ezafe tag to untagged tokens using the previously captured training information. The proposed framework was evaluated on Peykareh corpus and it achieved 95.26 percent accuracy. Test results show that this proposed approach outperformed other approaches for recognizing the position of Ezafe constructions.
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