University of Arizona at SemEval-2019 Task 12: Deep-Affix Named Entity Recognition of Geolocation Entities
We present the Named Entity Recognition (NER) and disambiguation model used by the University of Arizona team (UArizona) for the SemEval 2019 task 12. We achieved fourth place on tasks 1 and 3. We implemented a deep-affix based LSTM-CRF NER model for task 1, which utilizes only character, word, pre- fix and suffix information for the identification of geolocation entities. Despite using just the training data provided by task organizers and not using any lexicon features, we achieved 78.85{\%} strict micro F-score on task 1. We used the unsupervised population heuristics for task 3 and achieved 52.99{\%} strict micro-F1 score in this task.
Code (0)
등록된 구현이 없습니다.
Tasks
named-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)NERSimilar Papers 제목 키워드 기반
The University of Arizona at SemEval-2021 Task 10: Applying Self-training, Active Learning and Data Augmentation to Source-free Domain Adaptation
This paper describes our systems for negation detection and time expression recognition in SemEval 2021 Task 10, Source-Free Domain Adaptation for Semantic Processing. We show that self-training, active learning and data…
Active LearningData AugmentationDomain AdaptationNegation+2SU-NLP at SemEval-2022 Task 11: Complex Named Entity Recognition with Entity Linking
This paper describes the system proposed by Sabanc{\i} University Natural Language Processing Group in the SemEval-2022 MultiCoNER task. We developed an unsupervised entity linking pipeline that detects potential entity …
Entity Linkingnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)Deep Affix Features Improve Neural Named Entity Recognizers
We propose a practical model for named entity recognition (NER) that combines word and character-level information with a specific learned representation of the prefixes and suffixes of the word. We apply this approach t…
Feature EngineeringMorphological Analysisnamed-entity-recognitionNamed Entity Recognition+2L3i at SemEval-2022 Task 11: Straightforward Additional Context for Multilingual Named Entity Recognition
This paper summarizes the participation of the L3i laboratory of the University of La Rochelle in the SemEval-2022 Task 11, Multilingual Complex Named Entity Recognition (MultiCoNER). The task focuses on detecting semant…
Language ModelingLanguage ModellingMultilingual Named Entity Recognitionnamed-entity-recognition+3ASU at TextGraphs 2019 Shared Task: Explanation ReGeneration using Language Models and Iterative Re-Ranking
In this work we describe the system from Natural Language Processing group at Arizona State University for the TextGraphs 2019 Shared Task. The task focuses on Explanation Regeneration, an intermediate step towards gener…
Learning-To-RankRe-Ranking