Title: JOINTLY LEARNING TOPIC SPECIFIC WORD AND DOCUMENT EMBEDDING. Abstract: Document embedding generally ignores underlying topics, which fails to capture polysemous terms that can mislead to improper thematic representation. Moreover, embedding a new document during the test process needs a complex and expensive inference method. Some models first learn word embeddings and later learn underlying topics using a clustering algorithm for document representation; those methods miss the mutual interaction between the two paradigms. To this point, we propose a novel document-embedding method by weighted averaging of jointly learning topic-specific word embeddings called TDE: Topical Document Embedding, which efficiently captures syntactic and semantic properties by utilizing three levels of knowledge -i.e., word, topic, and document. TDE obtains document vectors on the fly simultaneously during the jointly learning process of the topical word embeddings. Experiments demonstrate better topical word embeddings using document vector as a global context and better document classification results on the obtained document embeddings by the proposed method over the recent related models.