Task 1
Related Papers
ASAP: Automatic Semantic Alignment for Phrases
In this paper we describe the ASAP sys- tem (Automatic Semantic Alignment for Phrases)1 which participated on the Task 1 at the SemEval-2014 contest (Marelli et al., 2014a). Our assumption …...
ezDI: A Supervised NLP System for Clinical Narrative Analysis
This paper describes the approach used by ezDI at the SemEval 2015 Task-14: ”Anal- ysis of Clinical Text”. The task was di- vided into two embedded tasks. Task-1 re- quired …...
SHEF-NN: Translation Quality Estimation with Neural Networks
We describe our systems for Tasks 1 and 2 of the WMT15 Shared Task on Qual- ity Estimation. Our submissions use (i) a continuous space language model to ex- tract …...
TKLBLIIR: Detecting Twitter Paraphrases with TweetingJay
When tweeting on a topic, Twitter users often post messages that convey the same or similar meaning. We describe TweetingJay, a system for detecting paraphrases and semantic simi- larity of …...
UWM: A Simple Baseline Method for Identifying Attributes of Disease and Disorder Mentions in Clinical Text
In this paper the system that was developed by Team UWM for the Task 14 of SemEval 2015 competition is described. Task 14 included two tasks: Task 1 was identification …...
FCICU at SemEval-2017 Task 1: Sense-Based Language Independent Semantic Textual Similarity Approach
AbstractThis paper describes FCICU team systems that participated in SemEval-2017 Semantic Textual Similarity task (Task1) for monolingual and cross-lingual sentence pairs. A sense-based language independent textual similarity approach is presented, …...
Competence-Level Prediction and Resume & Job Description Matching Using Context-Aware Transformer Models
AbstractThis paper presents a comprehensive study on resume classification to reduce the time and labor needed to screen an overwhelming number of applications significantly, while improving the selection of suitable …...
RTM Ensemble Learning Results at Quality Estimation Task
AbstractWe obtain new results using referential translation machines (RTMs) with predictions mixed and stacked to obtain a better mixture of experts prediction. We are able to achieve better results than …...
SarcasmDet at SemEval-2021 Task 7: Detect Humor and Offensive based on Demographic Factors using RoBERTa Pre-trained Model
AbstractThis paper presents one of the top winning solution systems for task 7 at SemEval2021, HaHackathon: Detecting and Rating Humor and Offense. This competition is divided into two tasks, task1 …...