Verbs play an important role in the understanding of natural language text. This paper studies the problem of abstracting the subject and object arguments of a verb into a set of noun concepts, known as the “argument concepts”. This set of concepts, whose size is parameterized, represents the finegrained semantics of a verb. For example, the object of “enjoy” can be abstracted into time, hobby and event, etc. We present a novel framework to automatically infer human readable and machine computable action concepts with high accuracy.
- The directory
SP
is the code for generating verb argument concepts based onSelectional Preference
by Philip Resnik. - The directory
Action
is the code for generating verb argument concepts based on algorithm proposed in our paper.
- The taxonomies used in both algorithm are
WordNet
from Princeton University andProbase
from MSRA. - The training corpus used in both algorithm is
google syntatic n-gram
.
In the preceeding of AAAI 2016, link: http://www.cs.sjtu.edu.cn/~kzhu/papers/kzhu-action.pdf
@inproceedings{gong2016representing,
title={Representing verbs as argument concepts},
author={Gong, Yu and Zhao, Kaiqi and Zhu, Kenny Qili},
booktitle={Thirtieth AAAI Conference on Artificial Intelligence},
year={2016}
}
The talk about this project in “Frontiers in Knowledge Graphs 2015” is at http://kw.fudan.edu.cn/resources/ppt/8-%E6%9C%B1%E5%85%B6%E7%AB%8B-Representing%20Verbs%20as%20Argument%20Concepts.pdf. You can rely on this slide to know more about our ideas and approaches.
A website demo of our project is at http://adapt.seiee.sjtu.edu.cn/~gongyu/action, where can browse the verbs' argument concepts in the browser.