Biological Event Extraction is an important task towards the goal of extracting biomedical knowledge from the scientific publications by capturing biomedical entities and their complex relations from the texts. As a crucial step in event extraction, event trigger identification, assigning words with suitable trigger category, has recently attracted substantial attention. As triggers are scattered in large corpus, traditional linguistic parsers are hard to generate syntactic features from them. Thereby, trigger sparsity problem restricts the model's learning process and becomes one of the main hinder in trigger identification. In this paper, we employ Noise Contrastive Estimation with Multi-Layer Perceptron model for solving triggers' sparsity problem. Meanwhile, in the light of recent advance in word distributed representation, word-embedding feature generated by language model is utilized for semantic and syntactic information extraction. Finally, experimental study on commonly used MLEE dataset against baseline methods has demonstrated its promising result.