Abstract
In this work, we introduce a supervised model for learning textual similarity, which can identify and score similarity between a set of candidate texts and a given query text. By combining dependency graph similarity and coverage features with lexical similarity measures using neural networks, we show that most relevant documents to a given text can be more accurately ranked and scored than if the lexical similarity measures were used in isolation. Additionally, we introduce an approximate dependency subgraph alignment approach allowing node gaps and mismatch, where a certain word in one dependency graph cannot be mapped to any word in the other graph. We apply our model to two different applications, namely re-ranking for improving document retrieval precision on a new dataset, and automatic short answer scoring on a standard dataset. Experimental results indicate that our approach is easily adaptable to different tasks and languages, and works well for long texts as well as short texts.
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Notes
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Learning rate = 0.5, momentum = 0.2.
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Learning rate = 0.3, momentum = 0.2.
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Kohail, S., Biemann, C. (2018). Matching, Re-Ranking and Scoring: Learning Textual Similarity by Incorporating Dependency Graph Alignment and Coverage Features. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2017. Lecture Notes in Computer Science(), vol 10761. Springer, Cham. https://doi.org/10.1007/978-3-319-77113-7_30
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