The invention relates to an anaphora resolution weak supervised learning method using a language model, and belongs to the technical field of information extraction in natural language processing. Themethod comprises the following steps: step 1, preprocessing a data set; 2, training an anaphora resolution model on a small number of labeled data sets; 3, training thelanguage model on the basis ofa multi-head self-attention mechanism on alarge-scale label-free data set; 4, performing weak supervised learning output based on an anaphora resolution model on unlabeled and labeled data, introducing losses of special design of the multi-head self-attention mechanism, dividing taps in the multi-head self-attention mechanism into special taps and common taps, and calculating different losses respectively. According to the method, the special taps have the capacity of outputting the similar distribution probability of the anaphora resolution model, the accuracy of the anaphora resolution system is improved, the obtained language model and the anaphora resolution model can expand the use field of an existing anaphora resolution model, and model parameters have better interpretability.
Anaphora resolution weak supervised learning method using language model