Luís Marujo

TL;DR: A model for constructing vector representations of words by composing characters using bidirectional LSTMs that requires only a single vector per character type and a fixed set of parameters for the compositional model, which yields state- of-the-art results in language modeling and part-of-speech tagging.

Abstract: We introduce a model for constructing vector representations of words by composing characters using bidirectional LSTMs. Relative to traditional word representation models that have independent vectors for each word type, our model requires only a single vector per character type and a fixed set of parameters for the compositional model. Despite the compactness of this model and, more importantly, the arbitrary nature of the form-function relationship in language, our "composed" word representations yield state-of-the-art results in language modeling and part-of-speech tagging. Benefits over traditional baselines are particularly pronounced in morphologically rich languages (e.g., Turkish).

TL;DR: A new transfer learning architecture is proposed to divide the sentence representation into two different feature spaces, which are expected to respectively capture the general sentiment words and the other important emotion-specific words via a dual attention mechanism.

TL;DR: This paper builds a corpus of tweets from Twitter annotated with keywords using crowdsourcing methods and proposes methods for addressing issues, which leads to solid improvements on this dataset for this task.

TL;DR: This article investigated the use of additional semantic features and pre-processing steps to improve automatic key phrase extraction, including signal words and freebase categories, which led to significant improvements in the accuracy of the results.