gy910210 / Action-aaai2016

Representing Verbs as Argument Concepts

Representing Verbs as Argument Concepts

Abstract

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.

Code

1. The directory SP is the code for generating verb argument concepts based on Selectional Preference by Philip Resnik.
2. The directory Action is the code for generating verb argument concepts based on algorithm proposed in our paper.

Dependency

1. The taxonomies used in both algorithm are WordNet from Princeton University and Probase from MSRA.
2. The training corpus used in both algorithm is google syntatic n-gram.

Publication

In the preceeding of AAAI 2016, link: http://www.cs.sjtu.edu.cn/~kzhu/papers/kzhu-action.pdf

Citation

@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}
}

Slide

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.

Demo

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.