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Found 14 papers, 4 papers with code
Named Person Coreference in English News
Here, we evaluate two state of the art coreference resolution systems on the subtask of Named Person Coreference, in which we are interested in identifying a person mentioned by name, along with all other mentions of the person, by pronoun or generic noun phrase.
Predicting Annotation Difficulty to Improve Task Routing and Model Performance for Biomedical Information Extraction
Experiments on a complex biomedical information extraction task using expert and lay annotators show that: (i) simply excluding from the training data instances predicted to be difficult yields a small boost in performance; (ii) using difficulty scores to weight instances during training provides further, consistent gains; (iii) assigning instances predicted to be difficult to domain experts is an effective strategy for task routing.
Word Embeddings (Also) Encode Human Personality Stereotypes
Word representations trained on text reproduce human implicit bias related to gender, race and age.
Browsing Health: Information Extraction to Support New Interfaces for Accessing Medical Evidence
Typical information needs, such as retrieving a full list of medical interventions for a given condition, or finding the reported efficacy of a particular treatment with respect to a specific outcome of interest cannot be straightforwardly posed in typical text-box search.
Evaluation of named entity coreference
It is therefore important that coreference resolution systems are able to link these different types of mentions to the correct entity name.
Entity Linking via Dual and Cross-Attention Encoders
Recently, a solution has been proposed for the former as a dual-encoder entity retrieval system (Gillick et al., 2019) that learns mention and entity representations in the same space, and performs linking by selecting the nearest entity to the mention in this space.
Entity-Switched Datasets: An Approach to Auditing the In-Domain Robustness of Named Entity Recognition Models
We propose a method for auditing the in-domain robustness of systems, focusing specifically on differences in performance due to the national origin of entities.
Interpretability Analysis for Named Entity Recognition to Understand System Predictions and How They Can Improve
We examine these questions by contrasting the performance of several variants of LSTM-CRF architectures for named entity recognition, with some provided only representations of the context as features.
Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training
Prior work on Data-To-Text Generation, the task of converting knowledge graph (KG) triples into natural text, focused on domain-specific benchmark datasets.
From Toxicity in Online Comments to Incivility in American News: Proceed with Caution
The ability to quantify incivility online, in news and in congressional debates, is of great interest to political scientists.
Temporal Effects on Pre-trained Models for Language Processing Tasks
Keeping the performance of language technologies optimal as time passes is of great practical interest.
Summarization from Leaderboards to Practice: Choosing A Representation Backbone and Ensuring Robustness
Academic literature does not give much guidance on how to build the best possible customer-facing summarization system from existing research components.
Machine Translation Aided Bilingual Data-to-Text Generation and Semantic Parsing
We present a system for bilingual Data-ToText Generation and Semantic Parsing.
