Discovering Power Laws in Entity Length

11/08/2018
by   Xiaoshi Zhong, et al.
2

This paper presents a discovery that the length of the entities follows a family of scale-free power law distributions. The concept of entity here broadly includes the named entity, entity mention, time expression, and domain-specific entity that are well investigated in natural language processing and related areas. The power law distributions in entity length have well-defined means and finite variances and possess the scale-free property. We explain the phenomenon of power laws in entity length by the principle of least effort in communication and the preferential mechanism.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/22/2022

SU-NLP at SemEval-2022 Task 11: Complex Named Entity Recognition with Entity Linking

This paper describes the system proposed by Sabancı University Natural L...
research
05/27/2020

Establishing a New State-of-the-Art for French Named Entity Recognition

The French TreeBank developed at the University Paris 7 is the main sour...
research
07/01/2011

Law of Connectivity in Machine Learning

We present in this paper our law that there is always a connection prese...
research
09/28/2021

Chekhov's Gun Recognition

Chekhov's gun is a dramatic principle stating that every element in a st...
research
09/03/2019

Modeling Named Entity Embedding Distribution into Hypersphere

This work models named entity distribution from a way of visualizing top...
research
05/23/2022

Information Propagation by Composited Labels in Natural Language Processing

In natural language processing (NLP), labeling on regions of text, such ...
research
09/01/2023

Exploring the law of text geographic information

Textual geographic information is indispensable and heavily relied upon ...

Please sign up or login with your details

Forgot password? Click here to reset