ICNet: Incorporating Indicator Words and Contexts to Identify Functional Description Information

Functional description information refers to the texts that describe the functionality or performance characteristics of a certain object. This type of information is of great potential value for the field of intelligence discovery. Thus automatically and accurately identifying this information from large amounts of texts on the web is very important. In this paper we reduce the functional description problem to a binary classification task deciding whether the input sentence is a functional description sentence or not. However, there exist lots of comment texts in the web data, which are semantically very similar to description texts, making our task quite difficult. Also, existing methods only provide general sentence representation models, which can't lead to targeted ways to solve our problem. Therefore, to address the problem, we not only exploit contexts, like many other previous work did, but also introduce indicator word information to learn rich representations. And in order to incorporate them both, we propose two models, namely ICNet(multi-tasks) and ICNet(ensemble). ICNet(multitasks) exploits them jointly in a integrated process of learning representations, while ICNet(ensemble) exploits them by two respective but concatenated sub-models. Experimental results on the collected real-world dataset indicate that both ICNet(multitasks) and ICNet(ensemble) achieve higher F1 scores compared with FaxtText, CNN, RNN, LSTM and Bi-LSTM, QuickThought models on this task.