Static bug localization techniques that locate bugs at method granularity have gained much attention from both researchers and practitioners. For a static method-level bug localization technique, a key but challenging step is to fully retrieve the semantics of methods and bug reports. Currently, existing studies mainly use the same bag-of-word space to represent the semantics of methods and bug reports without considering structure information of methods and textual contexts of bug reports, which largely and negatively affects bug localization performance. To address this problem, we develop BLESER, a new bug localization technique based on enhanced semantic retrieval. Specifically, we use an AST-based code embedding model (capturing code structure better) to retrieve the semantics of methods, and word embedding models (capturing textual contexts better) to represent the semantics of bug reports. Then, a deep learning model is built on the enhanced semantic representations. During model building, we compare five typical word embedding models in representing bug reports and try to explore the usefulness of re-sampling strategies and cost-sensitive strategies in handling class imbalance problems. We evaluate our BLESER on five Java projects from the Defects4J dataset. We find that: (1) On the whole, the word embedding model ELMo outperformed the other four models (including word2vec, BERT, etc.) in facilitating bug localization techniques. (2) Among four strategies aiming at solving class imbalance problems, the strategy ROS (random over-sampling) performed much better than the other three strategies (including random under-sampling, Focal Loss, etc.). (3) By integrating ELMo and ROS into BLESER, at method-level bug localization, we could achieve MAP of 0.108-0.504, MRR of 0.134-0.510, and Accuracy@1 of 0.125-0.5 on five Defects4J projects.