Event argument extraction, which aims to identify arguments of specific events and label their roles, is a challenging subtask of event extraction. Previous approaches solve this problem in a two-stage manner that first extracts named entities as argument candidates and then determines their roles. However, many nested entities may be missed or wrongly predicted during the argument candidate extraction procedure, which substantially affects the performance of the downstream classifier. In this paper, we propose a novel one-step question answering based framework, which performs argument candidate extraction and argument role classification simultaneously to mitigate the error propagation problem in conventional two-stage methods. Since the conventional question answering based framework cannot be applied directly to this task, we design a Question Answering based Sequence Labeling (QA-SL) model to tackle inexistent argument roles and multiple argument token spans. Moreover, considering the overwhelming number of parameters in question answering based neural network models and the relatively small size of event extraction corpus, we fine-tune the pre-trained model from BERT to mitigate the data scarcity problem. Extensive experiments demonstrate the benefits of the proposed method, leading to a competitive performance compared with state-of-the-art methods. To the best of our knowledge, this is the first work to cast event argument extraction as a question answering task.

Chinese knowledge base question answering (KBQA) is designed to answer the questions with the facts contained in a knowledge base. This task can be divided into two subtasks: topic entity extraction and relation selection. During the topic entity extraction stage, an entity extraction model is built to locate topic entities in questions. The Levenshtein Ratio entity linker is proposed to conduct effective entity linking. All the relevant subject-predicate-object (SPO) triples to topic entity are searched from the knowledge base as candidates. In relation selection, an attention-based multi-granularity interaction model (ABMGIM) is proposed. Two main contributions are as follows. First, a multi-granularity approach for text embedding is proposed. A nested character-level and word-level approach is used to concatenate the pre-trained embedding of a character with corresponding embedding on word-level. Second, we apply a hierarchical matching model for question representation in relation selection tasks, and attention mechanisms are imported for a fine-grained alignment between characters for relation selection. Experimental results show that our model achieves a competitive performance on the public dataset, which demonstrates its effectiveness.

Relation extraction is a vital task in natural language processing. It aims to identify the relationship between two specified entities in a sentence. Besides information contained in the sentence, additional information about the entities is verified to be helpful in relation extraction. Additional information such as entity type getting by NER (Named Entity Recognition) and description provided by knowledge base both have their limitations. Nevertheless, there exists another way to provide additional information which can overcome these limitations in Chinese relation extraction. As Chinese characters usually have explicit meanings and can carry more information than English letters. We suggest that characters that constitute the entities can provide additional information which is helpful for the relation extraction task, especially in large scale datasets. This assumption has never been verified before. The main obstacle is the lack of large-scale Chinese relation datasets. In this paper, first, we generate a large scale Chinese relation extraction dataset based on a Chinese encyclopedia. Second, we propose an attention-based model using the characters that compose the entities. The result on the generated dataset shows that these characters can provide useful information for the Chinese relation extraction task. By using this information, the attention mechanism we used can recognize the crucial part of the sentence that can express the relation. The proposed model outperforms other baseline models on our Chinese relation extraction dataset.