Disclosed is an English text sequence labelling method based on a neural network. The method comprises: extracting word information, character information and morphological features of a target sentence, and inputting the word information, the character information and the morphological features into a first BI-LSTM layer and a first dropout layer so as to obtain a first output matrix Om ×d 1; obtaining a second output matrix Om ×d 2 by means of a fourth matrix expression (I) corresponding to selective information; by means of a fifth matrix Sm × d corresponding to semantic information, obtaining a third output matrix Om ×d 3; by means of a sixth matrix Bm × d corresponding to binary information, obtaining a fourth output matrix Om ×d 4; performing linear addition on Om ×d 1, Om ×d 2, Om ×d 3 and Om ×d 4 to obtain a linear addition result O = ∑i=1 4Oωi i; inputting the linear addition result O into a second LSTM layer to obtain a fifth output matrix expression (II); and taking an expression (III) as an input sequence of conditional random fields (CRF) so as to output a label sequence Y = (y1, y2,..., ym) by means of the CRF, such that the accuracy of labelling can be effectively improved.
ENGLISH TEXT SEQUENCE LABELLING METHOD AND SYSTEM, AND COMPUTER DEVICE