Relation classification (RC) is an important task in knowledge extraction from texts, while data-driven approaches, although achieving high performance, heavily rely on a large amount of annotated training data. Recently, many few-shot RC models have been proposed and yielded promising results in general domain datasets, but when adapting to a specific domain, such as medicine, the performance drops dramatically. In this paper, we propose a Knowledge-Enhanced Few-shot RC model for the Domain Adaptation task (KEFDA), which incorporates general and domain-specific knowledge graphs (KGs) to the RC model to improve its domain adaptability. With the help of concept-level KGs, the model can better understand the semantics of texts and easily summarize the global semantics of relation types from only a few instances. To be more important, as a kind of meta-information, the manner of utilizing KGs can be transferred from existing tasks to new tasks, even across domains. Specifically, we design a knowledge-enhanced prototypical network to conduct instance matching, and a relation-meta learning network for implicit relation matching. The two scoring functions are combined to infer the relation type of a new instance. Experimental results on the Domain Adaptation Challenge in the FewRel 2.0 benchmark demonstrate that our approach significantly outperforms the state-of-the-art models (by 6.63% on average).

Dataless text classification has attracted increasing attentions recently. It only needs very few seed words of each category to classify documents, which is much cheaper than supervised text classification that requires massive labeling efforts. However, most of existing models pay attention to long texts, but get unsatisfactory performance on short texts, which have become increasingly popular on the Internet. In this paper, we at first propose a novel model named Seeded Biterm Topic Model (SeedBTM) extending BTM to solve the problem of dataless short text classification with seed words. It takes advantage of both word co-occurrence information in the topic model and category-word similarity from widely used word embeddings as the prior topic-in-set knowledge. Moreover, with the same approach, we also propose Seeded Twitter Biterm Topic Model (SeedTBTM), which extends Twitter-BTM and utilizes additional user information to achieve higher classification accuracy. Experimental results on five real short-text datasets show that our models outperform the state-of-the-art methods, and especially perform well when the categories are overlapping and interrelated.