Learning Dynamic Author Representations with Temporal Language Models Language models are at the heart of numerous works, notably in the text mining and information retrieval communities. These statistical models aim at extracting word distributions, from simple unigram models to recurrent approaches with latent variables that capture subtle dependencies in texts. However, those models are learned from word sequences only, and authorsu0027 identities, as well as publication dates, are seldom considered. We propose a neural model, based on recurrent language modeling, which aims at capturing language diffusion tendencies in author communities through time. By conditioning language models with author and temporal vector states, we are able to leverage the latent dependencies between the text contexts. This allows us to beat several temporal and nontemporal language baselines on two realworld corpora, and to learn meaningful author representations that vary through time.

Authorship Clustering using TFIDF weighted WordEmbeddings In this paper, we propose a novel idea to solve the problem of Author Clustering which is introduced in PAN2017 Author Identification task. First, we use word embeddings that brings semantic feature of the words along with the grammatical and syntactic information. Secondly, the documents are represented as the TFIDF weighted sum of the embedding vectors corresponding to each word. A higher TFIDF weight implies that the words have a stronger relationship in the documents in which they appear. Lastly, using hierarchical clustering all those documents written by each author are grouped together. Finally, we compare the proposed technique with the top submissions to PAN2017 author clustering task. Through extensive experimentation, we show that the proposed technique outperforms various stateoftheart models on the given task.