基于语义相似度的话题关联检测方法

为有效识别任意两篇报道的相似性,提出了一种基于语义相似度的话题关联检测算法.该算法首先通过计算特征词之间的相对熵作为两篇报道中特征词之间的语义相似度;其次,通过计算平均语义相似度获得特征词和报道之间的关联度;最后,结合特征词在语料库中的TF-IF(term frequency-inverse document frequency)权重计算两篇报道之间的关联度,实现报道之间的关联度检测.本文提出的方法与现有的向量空间模型方法和仅依赖于平均点互信息的方法进行了比较,并通过TDT4中文语料进行测评,结果表明,基于语义相似度的关联检测方法能够更好地利用文本的语境信息,提高了现有检测系统的性能,其最小DET(detection error tradeoff)代价降低了3%. 

Topic Link Detection Method Based on Semantic Similarity

To effectively judge the similarity between the topics of any two of stories, a topic link detection method was proposed on the basis of semantic similarity. First, the relative entropy between the feature words in two stories was calculated to work as the semantic similarity. Furthermore, the relevance between the feature words and the other story was obtained by calculating the average semantic similarity. At last, the relevance degree between two stories was calculated by considering TF-IF(term frequency-nverse document frequency)weights of the feature words in the corpus and the semantic similarity simultaneously, completing the link detection of the story pairs. The proposed algorithm was compared with the VSM (vector space model) method and average point-wise mutual information. The experimental results for Chinese Corpus of TDT4 show that minimum DET(detection error tradeoff)cost of the proposed algorithm is reduced by about 3%, which demonstrates that the proposed algorithm can impose the context information effectively and improve the performance of the topic link detection system simultaneously.