Using linguistic graph similarity to search for sentences in news articles
With the volume of daily news growing to sizes too big to handle for any individual human, there is a clear need for effective search algorithms. Since traditional bag-of-words approaches are inherently limited since they ignore much of the information that is embedded in the structure of the text, we propose a linguistic approach to search called Destiny in this paper. With Destiny, sentences, both from news items and the user queries, are represented as graphs where the nodes represent the words in the sentence and the edges represent the grammatical relations between the words. The proposed algorithm is evaluated against a TF-IDF baseline using a custom corpus of user-rated sentences. Destiny significantly outperforms TF-IDF in terms of Mean Average Precision, normalized Discounted Cumulative Gain, and Spearman's Rho.
|Keywords||Sub-graph isomorphism, Syntax dependencies, Text searching|
|Persistent URL||dx.doi.org/10.3233/978-1-61499-714-6-255, hdl.handle.net/1765/113077|
|Series||Frontiers in Artificial Intelligence and Applications|
Schouten, K.I.M, & Frasincar, F. (2016). Using linguistic graph similarity to search for sentences in news articles. Frontiers in Artificial Intelligence and Applications. doi:10.3233/978-1-61499-714-6-255