Probabilistic Approach for Embedding Arbitrary Features of Text

Topic modeling is usually used to model words in documents by probabilistic mixtures of topics. We generalize this setup and consider arbitrary features of the positions in a corpus, e.g. “contains a word”, “belongs to a sentence”, “has a word in the local context”, “is labeled with a POS-tag”, etc. We build sparse probabilistic embeddings for positions and derive embeddings for the features by averaging of those. Importantly, we interpret the EM-algorithm as an iterative process of intersection and averaging steps that reestimate position and feature embeddings respectively. With this approach, we get several insights. First, we argue that a sentence should not be represented as an average of its words. While each word is a mixture of multiple senses, each word occurrence refers typically to just one specific sense. So in our approach, we obtain sentence embeddings by averaging position embeddings from the E-step. Second, we show that Biterm Topic Model (Yan et al. [11]) and Word Network Topic Model (Zuo et al. [12]) are equivalent with the only difference of tying word and context embeddings. We further extend these models by adjusting representation of each sliding window with a few iterations of EM-algorithm. Finally, we aim at consistent embeddings for hierarchical entities, e.g. for word-sentence-document structure. We discuss two alternative schemes of training and generalize to the case where the middle level of the hierarchy is unknown. It provides a unified formulation for topic segmentation and word sense disambiguation tasks.