NeRoSim: A System for Measuring and Interpreting Semantic Textual Similarity
NeRoSim: A System for Measuring and Interpreting Semantic Textual Similarity
复制标题
NeRoSim：测量和解释语义文本相似性的系统
DOI：
--
复制
发表时间：
2015
期刊：
影响因子：
--
通讯作者：
D. Gautam
中科院分区：
文献类型：
--
作者：
Rajendra Banjade;Nobal B. Niraula;Nabin Maharjan;V. Rus;D. Stefanescu;Mihai C. Lintean;D. Gautam
我们在本文中介绍了我们为SemEval 2015共享任务2（2a -英语语义文本相似性，STS和2c -可解释相似性）开发的系统以及提交的运行结果。对于英语STS子任务，我们使用回归模型结合了广泛的功能，包括从各种方法获得的语义相似性得分。我们的一次运行实现了句子相似性子任务的加权平均相关得分为0.784（即，英国STS），并在29支球队提交的74分中排名第十。对于可解释的相似性试验任务，我们采用了基于规则的方法，结合基于语义相似性特征的组块对齐标记和评分。我们的可解释文本相似性系统是前三名性能最好的系统之一。
We present in this paper our system developed for SemEval 2015 Shared Task 2 (2a - English Semantic Textual Similarity, STS, and 2c - Interpretable Similarity) and the results of the submitted runs. For the English STS subtask, we used regression models combining a wide array of features including semantic similarity scores obtained from various methods. One of our runs achieved weighted mean correlation score of 0.784 for sentence similarity subtask (i.e., English STS) and was ranked tenth among 74 runs submitted by 29 teams. For the interpretable similarity pilot task, we employed a rule-based approach blended with chunk alignment labeling and scoring based on semantic similarity features. Our system for interpretable text similarity was among the top three best performing systems.