  Open world classification is a task in natural language processing with key
practical relevance and impact. Since the open or {\em unknown} category data
only manifests in the inference phase, finding a model with a suitable decision
boundary accommodating for the identification of known classes and
discrimination of the open category is challenging. The performance of existing
models is limited by the lack of effective open category data during the
training stage or the lack of a good mechanism to learn appropriate decision
boundaries. We propose an approach based on \underline{a}daptive
\underline{n}egative \underline{s}amples (ANS) designed to generate effective
synthetic open category samples in the training stage and without requiring any
prior knowledge or external datasets. Empirically, we find a significant
advantage in using auxiliary one-versus-rest binary classifiers, which
effectively utilize the generated negative samples and avoid the complex
threshold-seeking stage in previous works. Extensive experiments on three
benchmark datasets show that ANS achieves significant improvements over
state-of-the-art methods.
