Jinho D. Choi

It Depends: Dependency Parser Comparison Using A Web-based Evaluation Tool

2015

Jinho D. Choi, Joel Tetreault, Amanda Stent. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2015.

Lexicon Integrated CNN Models with Attention for Sentiment Analysis

2017

With the advent of word embeddings, lexicons are no longer fully utilized for sentiment analysis although they still provide important features in the traditional setting. This paper introduces a novel approach to sentiment analysis that …

Automatic Text-based Personality Recognition on Monologues and Multiparty Dialogues Using Attentive Networks and Contextual Embeddings

2019 · arXiv (Cornell University)

Previous works related to automatic personality recognition focus on using traditional classification models with linguistic features. However, attentive neural networks with contextual embeddings, which have achieved huge success in text classification, are rarely explored for …

Adaptation of Multilingual Transformer Encoder for Robust Enhanced Universal Dependency Parsing

2020

This paper presents our enhanced dependency parsing approach using transformer encoders, coupled with a simple yet powerful ensemble algorithm that takes advantage of both tree and graph dependency parsing. Two types of transformer encoders are …

Adapted End-to-End Coreference Resolution System for Anaphoric Identities in Dialogues

2021 · arXiv (Cornell University)

We present an effective system adapted from the end-to-end neural coreference resolution model, targeting on the task of anaphora resolution in dialogues. Three aspects are specifically addressed in our approach, including the support of singletons, …

NL-Augmenter 🦎 → 🐍 A Framework for Task-Sensitive Natural Language Augmentation

2023 · Northern European Journal of Language Technology

Data augmentation is an important method for evaluating the robustness of and enhancing the diversity of training data for natural language processing (NLP) models. In this paper, we present NL-Augmenter, a new participatory Python-based natural …

Cross-genre Document Retrieval: Matching between Conversational and\n Formal Writings

2017 · arXiv (Cornell University)

This paper challenges a cross-genre document retrieval task, where the\nqueries are in formal writing and the target documents are in conversational\nwriting. In this task, a query, is a sentence extracted from either a summary\nor a …

Unleashing the True Potential of Sequence-to-Sequence Models for Sequence Tagging and Structure Parsing

2023 · arXiv (Cornell University)

Sequence-to-Sequence (S2S) models have achieved remarkable success on various text generation tasks. However, learning complex structures with S2S models remains challenging as external neural modules and additional lexicons are often supplemented to predict non-textual outputs. …