Despite their success, deep networks are used as black-box models with outputs that are not
easily explainable during the learning and the prediction phases. This lack of interpretability …

Neural ranking models for information retrieval (IR) use shallow or deep neural networks to
rank search results in response to a query. Traditional learning to rank models employ …

Word embedding, where semantic and syntactic features are captured from unlabeled text
data, is a basic procedure in Natural Language Processing (NLP). The extracted features …

The availability of massive data and computing power allowing for effective data driven
neural approaches is having a major impact on machine learning and information retrieval …

Sparsity-constrained optimization problems are common in machine learning, such as
sparse coding, low-rank minimization and compressive sensing. However, most of previous …

Two document representation methods are mainly used in solving text mining problems.
Known for its intuitive and simple interpretability, the bag-of-words method represents a …

Neural ranking models for information retrieval (IR) use shallow or deep neural networks to
rank search results in response to a query. Traditional learning to rank models employ …

Embedding representations power machine intelligence in many applications, including
recommendation systems, but they are space intensive-potentially occupying hundreds of …

We present an unsupervised method to generate Word2Sense word embeddings that are
interpretable—each dimension of the embedding space corresponds to a fine-grained …

Because of its efficiency, word embedding has been widely used in many natural language
processing and text modeling tasks. It aims to represent each word by a vector so such that …