Distributed Representations

Distributed representations of words have proven extremely useful in numerous natural lan- guage processing tasks. Their appeal is that they can help alleviate data sparsity problems common to supervised learning. …...

Induction of common sense knowledge about prototypical sequence of events has recently received much attention (e.g., Chambers and Jurafsky (2008); Regneri et al. (2010)). Instead of inducing this knowledge in …...

Text-level discourse parsing remains a challenge: most approaches employ fea- tures that fail to capture the intentional, se- mantic, and syntactic aspects that govern discourse coherence. In this paper, we …...

Distributed representations of words have boosted the performance of many Natu- ral Language Processing tasks. However, usually only one representation per word is obtained, not acknowledging the fact that some …...

Argument extraction is the task of identifying arguments, along with their components in text. Arguments can be usually decomposed into a claim and one or more premises justifying it. The …...

We present a new approach for unsuper- vised semantic role labeling that lever- ages distributed representations. We in- duce embeddings to represent a predi- cate, its arguments and their complex …...

Learning distributed representations for relation instances is a central technique in downstream NLP applications. In or- der to address semantic modeling of rela- tional patterns, this paper constructs a new …...

AbstractIn this paper, we propose an approach to learn distributed representations of users and items from text comments for recommendation systems. Traditional recommendation algorithms, e.g. collaborative filtering and matrix completion, …...

AbstractFor the task of implicit discourse relation recognition, traditional models utilizing manual features can suffer from data sparsity problem. Neural models provide a solution with distributed representations, which could encode …...

Unsupervised methods for learning distributed representations of words are ubiquitous in to- day’s NLP research, but far less is known about the best ways to learn distributed phrase or sentence …...

Methods based on representation learning cur- rently hold the state-of-the-art in many natural language processing and knowledge base in- ference tasks. Yet, a major challenge is how to efficiently incorporate …...

AbstractThis paper presents an approach combining lexico-semantic resources and distributed representations of words applied to the evaluation in machine translation (MT). This study is made through the enrichment of a …...

AbstractDistributed representations of sentences have been developed recently to represent their meaning as real-valued vectors. However, it is not clear how much information such representations retain about the polarity of …...

AbstractThis article evaluates purported progress over the past years in RST discourse parsing. Several studies report a relative error reduction of 24 to 51% on all metrics that authors attribute …...

AbstractDistributed representations of sentences have become ubiquitous in natural language processing tasks. In this paper, we consider a continual learning scenario for sentence representations: Given a sequence of corpora, we …...