Binary Classification

We consider the problem of training logis- tic regression models for binary classifi- cation in information extraction and infor- mation retrieval tasks. Fitting probabilis- tic models for use with such …...

In this paper we address the issue of au- tomatically assigning information status to discourse entities. Using an annotated cor- pus of conversational English and exploit- ing morpho-syntactic and lexical …...

Out-of-vocabulary (OOV) words represent an important source of error in large vocabulary continuous speech recognition (LVCSR) sys- tems. These words cause recognition failures, which propagate through pipeline systems im- pacting …...

We propose an efficient way to train maximum entropy language models (MELM) and neural network language models (NNLM). The ad- vantage of the proposed method comes from a more robust …...

We propose an unsupervised system that learns continuous degrees of lexicality for noun-noun compounds, beating a strong base- line on several tasks. We demonstrate that the distributional representations of compounds …...

We describe our system for the DDIExtraction-2013 shared task of classifying Drug-Drug interactions (DDIs) given labeled drug mentions. The challenge called for a five-way classification of all drug pairs in …...

AbstractWe present a complete UIMA-based pipeline for sentiment analysis in Portuguese news using freely available resources and a minimal set of manually annotated training data. We obtained good precision on …...

Automatically identifying anomalous newswire events is a hard problem. We discuss the com- plexity of the problem and introduce a novel technique to model events based on recursive neural networks …...

Answer Validation is an important step in Automatic Question Answering systems and nowadays by spreading Community Question Answering systems it is known as an impor- tant task by itself. Previous …...

AbstractWe study the problem of detecting sentences describing adverse drug reactions (ADRs) and frame the problem as binary classification. We investigate different neural network (NN) architectures for ADR classification. In …...

AbstractWe investigate the value of feature engineering and neural network models for predicting successful writing. Similar to previous work, we treat this as a binary classification task and explore new …...

AbstractThe sentiment analysis task has been traditionally divided into lexicon or machine learning approaches, but recently the use of word embeddings methods have emerged, that provide powerful algorithms to allow …...

AbstractSentiment analysis is the process of identifying the opinion expressed in text. Recently it has been used to study behavioral finance, and in particular the effect of opinions and emotions …...

We introduce SentEval, a toolkit for evaluating the quality of universal sentence representations. SentEval encompasses a variety of tasks, including binary and multi-class classification, natural language inference and sentence similarity. …...

AbstractParallel sentences provide semantically similar information which can vary on a given dimension, such as language or register. Parallel sentences with register variation (like expert and non-expert documents) can be …...

AbstractThis paper presents a novel scheme for the annotation of hate speech in corpora of Web 2.0 commentary. The proposed scheme is motivated by the critical analysis of posts made …...

AbstractClickbait is a form of web content designed to attract attention and entice users to click on specific hyperlinks. The detection of clickbaits is an important task for online platforms …...

AbstractFew-shot learning addresses the the problem of learning based on a small amount of training data. Although more well-studied in the domain of computer vision, recent work has adapted the …...

AbstractHoping for a large number of clicks and potentially high social shares, journalists of various news media outlets publish sensationalist headlines on social media. These headlines lure the readers to …...

AbstractIn this paper, we propose several protocols to evaluate specific embeddings for Arabic sentiment analysis (SA) task. In fact, Arabic language is characterized by its agglutination and morphological richness contributing …...

Neural biomedical named entity recognition (BioNER) methods usually require a large amount of annotated data, while the anno- tated BioNER datasets are often difficult to obtain and small in scale …...

AbstractArgument pair extraction (APE) is a research task for extracting arguments from two passages and identifying potential argument pairs. Prior research work treats this task as a sequence labeling problem …...

AbstractThis paper presents a translationese study based on the parallel data from the Russian National Corpus (RNC). We explored differences between literary texts originally authored in Russian and fiction translated …...

Open-domain neural dialogue models have achieved high performance in response rank- ing and evaluation tasks. These tasks are formulated as a binary classification of re- sponses given in a dialogue …...

AbstractThis paper describes the system used for detecting humor in text. The system developed by the team TECHSSN uses binary classification techniques to classify the text. The data undergoes preprocessing …...

AbstractThis paper describes the experiments ran for SemEval-2022 Task 2, subtask A, zero-shot and one-shot settings for idiomaticity detection. Our main approach is based on fine-tuning transformer-based language models as …...

AbstractIn recent years, data augmentation has become an important field of machine learning. While images can use simple techniques such as cropping or rotating, textual data augmentation needs more complex …...

AbstractComparative Question Answering (cQA) is the task of providing concrete and accurate responses to queries such as: “Is Lyft cheaper than a regular taxi?” or “What makes a mortgage different …...

AbstractThis paper describes the authors’ submission to the SemEval-2022 task 4: Patronizing and Condescending Language (PCL) Detection. The aim of the task is the detection and classification of PCL in …...

AbstractThe “iSarcasmEval - Intended Sarcasm Detection in English and Arabic” task at the SemEval 2022 competition focuses on detectingand rating the distinction between intendedand perceived sarcasm in the context of …...

AbstractIn this paper, we formulate system combination for grammatical error correction (GEC) as a simple machine learning task: binary classification. We demonstrate that with the right problem formulation, a simple …...

Warning: This paper contains examples of the language that some people may find offensive. Transformer-based Language models have achieved state-of-the-art performance on a wide range of Natural Language Process- ing …...

AbstractDue to the severity of the social media offensive and hateful comments in Brazil, and the lack of research in Portuguese, this paper provides the first large-scale expert annotated corpus …...

AbstractThis paper describes our system submitted for SemEval Task 3: Presupposed Taxonomies: Evaluating Neural Network Semantics (Zamparelli et al., 2022). We participated in both the binary classification and the regression …...

AbstractThe goal of meta-learning is to learn to adapt to a new task with only a few labeled examples. Inspired by the recent progress in large language models, we propose …...

AbstractThis paper describes our system for Task 4 of SemEval 2022: Patronizing and Condescending Language Detection. Patronizing and Condescending Language (PCL) refers to language used with respect to vulnerable communities …...

AbstractThis paper presents the shared task on Multilingual Idiomaticity Detection and Sentence Embedding, which consists of two subtasks: (a) a binary classification task aimed at identifying whether a sentence contains …...

AbstractMitigating bias in training on biased datasets is an important open problem. Several techniques have been proposed, however the typical evaluation regime is very limited, considering very narrow data conditions. …...

AbstractAutomatic approaches to irony detection have been of interest to the NLP community for a long time, yet, state-of-the-art approaches still fall way short of what one would consider a …...

AbstractWe propose the use of a contextual embedding based-neural model on strictly textual inputs to detect the presence of patronizing or condescending language (PCL). We finetuned a pre-trained BERT model …...

AbstractWe propose a simple refactoring of multi-choice question answering (MCQA) tasks as a series of binary classifications. The MCQA task is generally performed by scoring each (question, answer) pair normalized …...

AbstractIn this manuscript we detail the participation of the UMUTeam in the iSarcasm shared task (SemEval-2022). This shared task is related to the identification of sarcasm in English and Arabic …...

AbstractThis paper details our implementations for finding Patronizing and Condescending Language in texts, as part of the SemEval Workshop Task 4. We have used a variety of methods from simple …...

AbstractThis paper presents our strategy to address the SemEval-2022 Task 3 PreTENS: Presupposed Taxonomies Evaluating Neural Network Semantics. The goal of the task is to identify if a sentence is …...

AbstractIn current NLP research, large-scale language models and their abilities are widely being discussed. Some recent works have also found notable failures of these models. Often these failure examples involve …...

AbstractFor school teachers and Designated Safeguarding Leads (DSLs), computers and other school-owned communication devices are both indispensable and deeply worrisome. For their education, children require access to the Internet, as …...

AbstractEntailment has been recognized as an important metric for evaluating natural language understanding (NLU) models, and recent studies have found that entailment pretraining benefits weakly supervised fine-tuning. In this work, …...

AbstractIn this paper, we present our system for the textual entailment identification task as a subtask of the SemEval-2023 Task 7: Multi-evidence Natural Language Inference for Clinical Trial Data. The …...