Log-linear models and more recently neural network models used for supervised relation
extraction requires substantial amounts of training data and time, limiting the portability to …

Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment classification task.
Many recent works have used dependency trees to extract the relation between aspects and …

Relation extraction (RE) involves identifying the relations between entities from unstructured
texts. RE serves as the foundation for many natural language processing (NLP) applications …

Active learning methods which present selected examples from the corpus for annotation
provide more efficient learning of supervised relation extraction models, but they leave the …

Entity alignment (EA) is a fundamental data integration task that identifies equivalent entities
between different knowledge graphs (KGs). Temporal Knowledge graphs (TKGs) extend …

Maximizing the user-item engagement based on vectorized embeddings is a standard
procedure of recent recommender models. Despite the superior performance for item …

Unsupervised relation extraction aims to extract the relationship between entities from
natural language sentences without prior information on relational scope or distribution …

Natural language inference (NLI) is an increasingly important task for natural language
understanding, which requires one to infer the relationship between the sentence pair …

The generalizability to new databases is of vital importance to Text-to-SQL systems which
aim to parse human utterances into SQL statements. Existing works achieve this goal by …

We propose the first character-level white-box adversarial attack method against transformer
models. The intuition of our method comes from the observation that words are split into …