홈 › Papers

UCSC-NLP at SemEval-2017 Task 4: Sense n-grams for Sentiment Analysis in Twitter

2017-08-01 · SEMEVAL 2017 8 · Jos{\'e} Abreu, Iv{\'a}n Castro, Claudia Mart{\'\i}nez, Sebasti{\'a}n Oliva, Yoan Guti{\'e}rrez

This paper describes the system submitted to SemEval-2017 Task 4-A Sentiment Analysis in Twitter developed by the UCSC-NLP team. We studied how relationships between sense n-grams and sentiment polarities can contribute to this task, i.e. co-occurrences of WordNet senses in the tweet, and the polarity. Furthermore, we evaluated the effect of discarding a large set of features based on char-grams reported in preceding works. Based on these elements, we developed a SVM system, which exploring SentiWordNet as a polarity lexicon. It achieves an $F_1=0.624$of average. Among 39 submissions to this task, we ranked 10th.

📄 PDF Abstract BibTeX

Code (0)

등록된 구현이 없습니다.

Tasks

Sentiment Analysis

Methods 이 논문이 사용한 방법론

SVM A Support Vector Machine, or SVM, is a non-parametric supervised learning model. For non-linear classification and regression, they utilise the kernel trick to map inputs…

NLDS-UCSC at SemEval-2016 Task 6: A Semi-Supervised Approach to Detecting Stance in Tweets

2016-06-01 · SEMEVAL 2016 6 · Amita Misra, Brian Ecker, H, Theodore leman 외

Sentiment Analysis

SENSEI-LIF at SemEval-2016 Task 4: Polarity embedding fusion for robust sentiment analysis

2016-06-01 · SEMEVAL 2016 6 · Mickael Rouvier, Benoit Favre

Sentence Classification Sentiment Analysis Word Embeddings

UW-FinSent at SemEval-2017 Task 5: Sentiment Analysis on Financial News Headlines using Training Dataset Augmentation

2017-08-01 · SEMEVAL 2017 8 · Vineet John, Olga Vechtomova

This paper discusses the approach taken by the UWaterloo team to arrive at a solution for the Fine-Grained Sentiment Analysis problem posed by Task 5 of SemEval 2017. The paper describes the document vectorization and se…

Data Augmentation regression Sentiment Analysis

MITRE at SemEval-2018 Task 11: Commonsense Reasoning without Commonsense Knowledge

2018-06-01 · SEMEVAL 2018 6 · Elizabeth Merkhofer, John Henderson, David Bloom, Laura Strickhart 외

This paper describes MITRE{'}s participation in SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge. The techniques explored range from simple bag-of-ngrams classifiers to neural architectures with va…

Common Sense Reasoning Information Retrieval Reading Comprehension regression

UCSC at SemEval-2025 Task 3: Context, Models and Prompt Optimization for Automated Hallucination Detection in LLM Output

2025-05-05 · Sicong Huang, Jincheng He, Shiyuan Huang, Karthik Raja Anandan 외

Hallucinations pose a significant challenge for large language models when answering knowledge-intensive queries. As LLMs become more widely adopted, it is crucial not only to detect if hallucinations occur but also to p…

Hallucination