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Despite its simplicity, bag-of-n-grams sen- tence representation has beenfound to excel in some NLP tasks. However, it has not re- ceived much attentionin recent years and fur- ther analysis on its properties is necessary. Wepropose a framework to investigate the amount and type of information capturedin a general- purposed bag-of-n-grams sentence represen- tation. We first usesentence reconstruction as a tool to obtain bag-of-n-grams representa- tionthat contains general information of the sentence. We then run prediction tasks(sen- tence length, word content, phrase content and word order) using theobtained representation to look into the specific type of information capturedin the representation. Our analysis demonstrates that bag-of-n-gramsrepresenta- tion does contain sentence structure level in- formation. However,incorporating n-grams with higher order n empirically helps little withencoding more information in general, except for phrase content information.

Analysis of Bag-of-n-grams Representation's Properties Based on Textual Reconstruction