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Solving a Complex Language Game by Using Knowledge-Based Word Associations Discovery

机译:通过使用基于知识的单词联想发现解决复杂的语言游戏

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“The Guillotine” is a language game whose goal is to predict the unique word that is linked in some way to five words given as clues, generally unrelated to each other. The ability of the human player to find the solution depends on the richness of her cultural background. We designed an artificial player for that game, based on a large knowledge repository built by exploiting several sources available on the web, such as Wikipedia, that provide the system with the cultural and linguistic background needed to understand clues. The “brain” of the system is a spreading activation algorithm that starts processing clues, finds associations between them and words within the knowledge repository, and computes a list of candidate solutions. In this paper we focus on the problem of finding the most promising candidate solution to be provided as the final answer. We improved the spreading algorithm by means of two strategies for finding associations also between candidate solutions and clues. Those strategies allow bidirectional reasoning and select the candidate solution which is the most connected with the clues. Experiments show that the performance of the system is comparable to that of average human players.
机译:“断头台”是一种语言游戏,其目标是预测唯一的单词,该单词以某种方式链接到作为线索给出的五个单词,通常彼此之间不相关。人类玩家找到解决方案的能力取决于其文化背景的丰富性。我们基于大型知识库(通过利用诸如Wikipedia等网络上的多种可用资源而建立的)为该游戏设计了一个人工游戏者,该资源为系统提供了理解线索所需的文化和语言背景。该系统的“大脑”是一种扩展激活算法,该算法开始处理线索,找到线索与知识库中的单词之间的关联,并计算候选解决方案列表。在本文中,我们关注于寻找最有希望的候选解决方案作为最终答案的问题。我们通过两种寻找候选解和线索之间关联的策略改进了传播算法。这些策略允许双向推理并选择与线索最相关的候选解决方案。实验表明,该系统的性能可与普通玩家媲美。

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