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Cross-lingual semantic similarity of words as the similarity of their semantic word responses

机译:单词的跨语言语义相似度与其语义单词响应的相似度

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摘要

We propose a new approach to identifying semantically similar words across languages. The approach is based on an idea that two words in different languages are similar if they are likely to generate similar words (which includes both source and target language words) as their top semantic word responses. Semantic word responding is a concept from cognitive science which addresses detectingthe most likely words that humans output as free word associations given some cue word. The method consists of two main steps: (1) it utilizes a probabilistic multilingual topic model trained on comparable data to learn and quantify the semantic word responses, (2) it provides ranked lists of similar words according to the similarity of their semantic word response vectors. We evaluate our approach in the task of bilingual lexicon extraction (BLE) for a variety of language pairs. Weshow that in the cross-lingual settings withoutany language pair dependent knowledge the response-based method of similarity is more robust and outperforms current state-of-the art methods that directly operate in the semantic space of latent cross-lingual concepts/topics.
机译:我们提出了一种新的方法来识别跨语言的语义相似的单词。该方法基于这样的想法:如果两个语言可能会生成相似的单词(包括源语言和目标语言单词)作为其最高语义单词响应,则它们是相似的。语义词响应是来自认知科学的一个概念,致力于检测人类在给定某些提示词的情况下以自由词联想形式输出的最有可能的词。该方法包括两个主要步骤:(1)利用在可比较数据上训练的概率多语言主题模型来学习和量化语义词的响应,(2)根据语义词响应的相似性提供相似词的排名列表向量。我们在各种语言对的双语词典提取(BLE)任务中评估了我们的方法。我们显示,在没有任何语言对依赖知识的跨语言环境中,基于响应的相似性方法更加健壮,并且胜过直接在潜在跨语言概念/主题的语义空间中直接运行的最新技术。

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