Abolfazl Keighobadi Lamjiri

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The prediction of the future states in MultiAgent Systems has been a challenging problem since the begining of MAS. Robotic soccer is a MAS environment in which the predictions of the opponents strategy, or opponent modeling, plays an important role. In this paper, a novel case-based architecture is applied in the soccer coach that learns and predicts(More)
In this paper we analyze the contribution of semantic, syntactic and word similarity of document features in closed and open domain question answering. Semantic similarity is computed as the similarity of the action in the candidate sentence to the action asked in the question, measured using WordNet::Similarity on main verbs. The syntactic similarity(More)
In this paper, we describe our experiments on statistical word sense disambiguation (WSD) using two systems based on different approaches: Näıve Bayes on word tokens and Maximum Entropy on local syntactic and semantic features. In the first approach, we consider a context window and a sub-window within it around the word to disambiguate. Within the outside(More)
One of the main challenges in RoboCup is to maintain a high level of speed and accuracy in decision making and performing actions by the robot players. Although we might be able to use complicated hardware and software on the robots to achieve the desired accuracy, but such systems might not be applicable in real-time RoboCup environment due to their high(More)
In this paper, we describe the system we used for the TREC Question Answering Track. For factoid and list questions two different approaches were exploited: A redundancy-based approach using a modified version of aranea and a parse-tree based unifier. The modified version of aranea essentially uses Google snippets for extracting answers and then projects(More)
As opposed to factoid questions, questions posed in a closed domain are typically more open-ended. People can ask for specific properties, procedures or conditions and require longer and more complex answers. As a result, detailed understanding of the question and the corpus texts is required for answering such questions. In this paper, we present a(More)
We present a technique for ranking the candidate answers of questions that have a main content verb. This novel ranking method uses the question head (the most important noun phrase) as an anchor for selecting the target subtree in the parse tree of the candidate sentence. The semantic similarity of the action in the selected subtree to the action asked by(More)
This paper presents our experiments with a low-frequency approach to information retrieval for question answering over a small, closed domain corpus and a variety of question types. With a corpus of 255 questions categorized into simple, average and challenging, we compared the performance of our question answering system (QASCU) when used with two(More)
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