Method and apparatus for sequence labeling on entity text, and non-transitory computer-readable recording medium

An apparatus for sequence labeling on an entity text, the apparatus comprising:

• a memory storing computer-executable instructions;
• and
• one or more processors configured to execute the computer-executable instructions such that the one or more processors are configured to
• determine a start position of an entity text within a target text, the target text being a text having an entity label to be recognized;
• generate a first matrix based on the start position of the entity text within the target text, the number of rows and the number of columns of the first matrix being equal to a sequence length of the target text, elements in the first matrix indicating focusable weights of each word with respect to other words in the target text, the focusable weight of a word in the target text that is within the entity text with respect to a first word being greater than the focusable weight of the word with respect to a second word, the first word including the word, and one or more words between the start position of the entity text and the word, and the second word being a word other than the first word in the target text;
• generate a named entity recognition model using the first matrix, the named entity recognition model being obtained by training using first training data, the first training data including word embeddings corresponding to one or more respective texts in a training text set, and the texts in the training text set being texts whose entity label has been labeled;
• and
• input the target text to the named entity recognition model, and output probability distribution of the entity label corresponding to the target text,
• wherein determining the start position of the entity text within the target text includes
• performing at least one of data format conversion and data noise removal on the target text to obtain a first text;
• segmenting words of the first text to obtain a first word sequence;
• segmenting word segments of the first word sequence to divide the first word sequence into a first word segment sequence;
• and
• inputting the first word segment sequence to a second training model to obtain the start position of the entity text.

A non-transitory computer-readable recording medium having computer-executable instructions for execution by one or more processors, wherein, the computer-executable instructions, when executed, cause the one or more processors to carry out a method for sequence labeling on an entity text, the method comprising:

• determining a start position of an entity text within a target text, the target text being a text having an entity label to be recognized;
• generating a first matrix based on the start position of the entity text within the target text, the number of rows and the number of columns of the first matrix being equal to a sequence length of the target text, elements in the first matrix indicating focusable weights of each word with respect to other words in the target text, the focusable weight of a word in the target text that is within the entity text with respect to a first word being greater than the focusable weight of the word with respect to a second word, the first word including the word, and one or more words between the start position of the entity text and the word, and the second word being a word other than the first word in the target text;
• generating a named entity recognition model using the first matrix, the named entity recognition model being obtained by training using first training data, the first training data including word embeddings corresponding to one or more respective texts in a training text set, and the texts in the training text set being texts whose entity label has been labeled;
• and
• inputting the target text to the named entity recognition model, and outputting probability distribution of the entity label corresponding to the target text,
• wherein determining the start position of the entity text within the target text includes
• performing at least one of data format conversion and data noise removal on the target text to obtain a first text;
• segmenting words of the first text to obtain a first word sequence;
• segmenting word segments of the first word sequence to divide the first word sequence into a first word segment sequence;
• and
• inputting the first word segment sequence to a second training model to obtain the start position of the entity text.