ACE2004

. This paper proposes a convolution tree kernel-based approach for relation extraction where the parse tree is expanded with entity features such as entity type, subtype, and mention level etc. …...

We present a novel adaptive clustering model for coreference resolution in which the expert rules of a state of the art deterministic sys- tem are used as features over pairs …...

We explore the interplay of knowledge and structure in co-reference resolution. To inject knowledge, we use a state-of-the-art system which cross-links (or “grounds”) expressions in free text to Wikipedia. We …...

Coreference resolution systems can benefit greatly from inclusion of global context, and a number of recent approaches have demonstrated improvements when precom- puting an alignment to external knowledge sources. However, …...

Many errors in coreference resolution come from semantic mismatches due to inadequate world knowledge. Errors in named-entity linking (NEL), on the other hand, are of- ten caused by superficial modeling …...

We propose a two-phase framework to adapt existing relation extraction classi- fiers to extract relations for new target do- mains. We address two challenges: neg- ative transfer when knowledge in …...

We present a novel end-to-end neural model to extract entities and relations be- tween them. Our recurrent neural net- work based model captures both word se- quence and dependency tree …...

AbstractEnd-to-end relation extraction refers to identifying boundaries of entity mentions, entity types of these mentions and appropriate semantic relation for each pair of mentions. Traditionally, separate predictive models were trained …...

AbstractWe propose two neural network architectures for nested named entity recognition (NER), a setting in which named entities may overlap and also be labeled with more than one label. We …...

AbstractWhen an entity name contains other names within it, the identification of all combinations of names can become difficult and expensive. We propose a new method to recognize not only …...

AbstractThis paper presents Pyramid, a novel layered model for Nested Named Entity Recognition (nested NER). In our approach, token or text region embeddings are recursively inputted into L flat NER …...

AbstractIn this paper, we propose CHOLAN, a modular approach to target end-to-end entity linking (EL) over knowledge bases. CHOLAN consists of a pipeline of two transformer-based models integrated sequentially to …...

AbstractConstituency parsing and nested named entity recognition (NER) are similar tasks since they both aim to predict a collection of nested and non-crossing spans. In this work, we cast nested …...

AbstractNested entities are observed in many domains due to their compositionality, which cannot be easily recognized by the widely-used sequence labeling framework.A natural solution is to treat the task as …...

AbstractWe propose a global entity disambiguation (ED) model based on BERT. To capture global contextual information for ED, our model treats not only words but also entities as input tokens, …...