Jointly optimizing word representations for lexical and sentential tasks with the C-PHRASE model

Enhancing Relation Extraction Using Syntactic Indicators and Sentential Contexts

State-of-the-art methods for relation extraction consider the sentential context by modeling the entire sentence. However, syntactic indicators, certain phrases or words like prepositions that are more informative than o…

Improving Lexical Semantics for Sentential Semantics: Modeling Selectional Preference and Similar Words in a Latent Variable Model

Learning to Represent Words in Context with Multilingual Supervision

We present a neural network architecture based on bidirectional LSTMs to compute representations of words in the sentential contexts. These context-sensitive word representations are suitable for, e.g., distinguishing di…

archer at SemEval-2021 Task 1: Contextualising Lexical Complexity

Evaluating the complexity of a target word in a sentential context is the aim of the Lexical Complexity Prediction task at SemEval-2021. This paper presents the system created to assess single words lexical complexity, c…

Autoencoding Keyword Correlation Graph for Document Clustering

Document clustering requires a deep understanding of the complex structure of long-text; in particular, the intra-sentential (local) and inter-sentential features (global). Existing representation learning models do not …