Convolutional Neural Networks

We report on a series of experiments with convolutional neural networks (CNN) trained on top of pre-trained word vec- tors for sentence-level classification tasks. We show that a simple CNN …...

We describe a semantic role labeler with state- of-the-art performance and low computational requirements, which uses convolutional and time-domain neural networks. The system is designed to work with features derived …...

AbstractSeveral tasks in argumentation mining and debating, question-answering, and natural language inference involve classifying a sequence in the context of another sequence (referred as bi-sequence classification). For several single sequence …...

A key challenge in entity linking is making ef- fective use of contextual information to dis- ambiguate mentions that might refer to differ- ent entities in different contexts. We present …...

AbstractWhen processing arguments in online user interactive discourse, it is often necessary to determine their bases of support. In this paper, we describe a supervised approach, based on deep neural …...

This paper investigates two different neural architectures for the task of relation classi- fication: convolutional neural networks and recurrent neural networks. For both mod- els, we demonstrate the effect of …...

We describe our system, ConvKN, participat- ing to the SemEval-2016 Task 3 “Commu- nity Question Answering”. The task targeted the reranking of questions and comments in real-life web fora both …...

Convolutional Neural Networks (CNNs) have shown to yield very strong results in several Computer Vision tasks. Their application to language has received much less attention, and it has mainly focused …...

In this paper, we study, compare and combine two state-of-the-art approaches to automatic feature engineering: Convolution Tree Ker- nels (CTKs) and Convolutional Neural Net- works (CNNs) for learning to rank …...

Coreference resolution for event men- tions enables extraction systems to process document-level information. Current sys- tems in this area base their decisions on rich semantic features from various knowledge bases, …...

Dialogue topic tracking is a sequential la- belling problem of recognizing the topic state at each time step in given dialogue sequences. This paper presents various ar- tificial neural network …...

AbstractHuman concept representations are often grounded with visual information, yet some aspects of meaning cannot be visually represented or are better described with language. Thus, vision and language provide complementary …...

Event extraction is a particularly challenging problem in information extraction. The state- of-the-art models for this problem have ei- ther applied convolutional neural networks in a pipelined framework (Chen et …...

This paper describes our system submitted for the Sentiment Analysis in Twitter task of SemEval-2016, and specifically for the Mes- sage Polarity Classification subtask. We used a system that combines …...

Convolutional neural networks (CNN) have achieved the top performance for event de- tection due to their capacity to induce the underlying structures of the k-grams in the sentences. However, the …...

We present an approach to improve sta- tistical machine translation of image de- scriptions by multimodal pivots defined in visual space. The key idea is to perform image retrieval over …...

This paper describes the system developed at LIF for the SemEval-2016 evaluation cam- paign. The goal of Task 4.A was to iden- tify sentiment polarity in tweets. The system extends …...

Recent approaches based on artificial neural networks (ANNs) have shown promising re- sults for short-text classification. However, many short texts occur in sequences (e.g., sen- tences in a document or …...

In this paper we describe our participa- tion in the STS Core subtask which is the determination of the monolingual seman- tic similarity between pair of sentences. In our participation …...

AbstractEmotion cause extraction aims to identify the reasons behind a certain emotion expressed in text. It is a much more difficult task compared to emotion classification. Inspired by recent advances …...

AbstractNeural network models have recently been applied to the task of automatic essay scoring, giving promising results. Existing work used recurrent neural networks and convolutional neural networks to model input …...

AbstractIn this paper, we describe a methodology to infer Bullish or Bearish sentiment towards companies/brands. More specifically, our approach leverages affective lexica and word embeddings in combination with convolutional neural …...

AbstractWe describes deep neural networks frameworks in this paper to address the community question answering (cQA) ranking task (SemEval-2017 task 3). Convolutional neural networks and bi-directional long-short term memory networks …...

AbstractWe introduce globally normalized convolutional neural networks for joint entity classification and relation extraction. In particular, we propose a way to utilize a linear-chain conditional random field output layer for …...

AbstractConvolutional Neural Networks (CNNs) are widely used in NLP tasks. This paper presents a novel weight initialization method to improve the CNNs for text classification. Instead of randomly initializing the …...

AbstractFor the competition SemEval-2017 we investigated the possibility of performing stance classification (support, deny, query or comment) for messages in Twitter conversation threads related to rumours. Stance classification is interesting …...

AbstractRecent work has shown that Tree Kernels (TKs) and Convolutional Neural Networks (CNNs) obtain the state of the art in answer sentence reranking. Additionally, their combination used in Support Vector …...

AbstractPrevious work on dialog act (DA) classification has investigated different methods, such as hidden Markov models, maximum entropy, conditional random fields, graphical models, and support vector machines. A few recent …...

AbstractThe dominant approach for many NLP tasks are recurrent neural networks, in particular LSTMs, and convolutional neural networks. However, these architectures are rather shallow in comparison to the deep convolutional …...

AbstractThe cataloging of product listings through taxonomy categorization is a fundamental problem for any e-commerce marketplace, with applications ranging from personalized search recommendations to query understanding. However, manual and rule …...

AbstractIn this paper, we analyze and evaluate word embeddings for representation of longer texts in the multi-label classification scenario. The embeddings are used in three convolutional neural network topologies. The …...

AbstractThe literature in automated sarcasm detection has mainly focused on lexical-, syntactic- and semantic-level analysis of text. However, a sarcastic sentence can be expressed with contextual presumptions, background and commonsense …...

AbstractWe propose a machine reading comprehension model based on the compare-aggregate framework with two-staged attention that achieves state-of-the-art results on the MovieQA question answering dataset. To investigate the limitations of …...

AbstractEncoder-decoder based Sequence to Sequence learning (S2S) has made remarkable progress in recent years. Different network architectures have been used in the encoder/decoder. Among them, Convolutional Neural Networks (CNN) and …...

AbstractThis paper describes the participation of ELiRF-UPV team at tasks 1 and 3 of Semeval-2018. We present a deep learning based system that assembles Convolutional Neural Networks and Long Short-Term …...

AbstractCoding EMRs with diagnosis and procedure codes is an indispensable task for billing, secondary data analyses, and monitoring health trends. Both speed and accuracy of coding are critical. While coding …...

AbstractReliably detecting relevant relations between entities in unstructured text is a valuable resource for knowledge extraction, which is why it has awaken significant interest in the field of Natural Language …...

This paper investigates the robustness of NLP against perturbed word forms. While neural approaches can achieve (almost) human-like accuracy for certain tasks and conditions, they often are sensitive to small …...

AbstractArtificial Neural Networks (ANNs) have experienced great success in the past few years. The increasing complexity of these models leads to less understanding about their decision processes. Therefore, introspection techniques …...

AbstractThe rapid growth of documents across the web has necessitated finding means of discarding redundant documents and retaining novel ones. Capturing redundancy is challenging as it may involve investigating at …...

AbstractWe present an analysis into the inner workings of Convolutional Neural Networks (CNNs) for processing text. CNNs used for computer vision can be interpreted by projecting filters into image space, …...

AbstractThis shared task is a typical question answering task. Compared with the normal question and answer system, it needs to give the answer to the question based on the text …...

Neural machine translation (NMT) sys- tems operate primarily on words (or sub- words), ignoring lower-level patterns of morphology. We present a character- aware decoder designed to capture such patterns when …...

Neural end-to-end architectures have been recently proposed for spoken language translation (SLT), following the state-of- the-art results obtained in machine transla- tion (MT) and speech recognition (ASR). Motivated by this …...

AbstractResearchers use wordnets as a knowledge base in many natural language processing tasks and applications, such as question answering, textual entailment, discourse classification, and so forth. Lexico-semantic relations among words …...

AbstractIn this paper, we describe our approach to detect hate speech against women and immigrants on Twitter in a multilingual context, English and Spanish. This challenge was proposed by the …...

AbstractIn this work convolutional neural networks were used in order to determine the sentiment in a conversational setting. This paper’s contributions include a method for handling any sized input and …...

AbstractThis paper describes the participation of team “bertha-von-suttner” in the SemEval2019 task 4 Hyperpartisan News Detection task. Our system uses sentence representations from averaged word embeddings generated from the pre-trained …...

AbstractText representation plays a vital role in retrieval-based question answering, especially in the legal domain where documents are usually long and complicated. The better the question and the legal documents …...

AbstractAspect-based sentiment classification is a popular task aimed at identifying the corresponding emotion of a specific aspect. One sentence may contain various sentiments for different aspects. Many sophisticated methods such …...

AbstractExisting studies on multimodal neural machine translation (MNMT) have mainly focused on the effect of combining visual and textual modalities to improve translations. However, it has been suggested that the …...

AbstractIn order to achieve interoperability of information in the context of the Semantic Web, it is necessary to find effective ways to align different ontologies. As the number of ontologies …...

AbstractSign languages are complex languages. Research into them is ongoing, supported by large video corpora of which only small parts are annotated. Sign language recognition can be used to speed …...

With the rapid growth of information, browsing social media on the Internet is becoming a part of people’s daily lives. Social platforms give us the latest information in real time, …...

AbstractIn the era of pre-trained language models, Transformers are the de facto choice of model architectures. While recent research has shown promise in entirely convolutional, or CNN, architectures, they have …...

AbstractWe present two convolutional neural networks for predicting the complexity of words and phrases in context on a continuous scale. Both models utilize word and character embeddings alongside lexical features …...

AbstractIrrespective of the success of the deep learning-based mixed-domain transfer learning approach for solving various Natural Language Processing tasks, it does not lend a generalizable solution for detecting misinformation from …...

Ever since the advent of deep learning, cross- modal representation learning has been dom- inated by the approaches involving convolu- tional neural networks for visual representa- tion and recurrent neural …...

AbstractThis paper describes the multimodal late fusion model proposed in the SemEval-2022 Multimedia Automatic Misogyny Identification (MAMI) task. The main contribution of this paper is the exploration of different late …...

AbstractImage captioning is a prominent Artificial Intelligence (AI) research area that deals with visual recognition and a linguistic description of the image. It is an interdisciplinary field concerning how computers …...

In this paper, we explore compact convolutional neural networks (CNNs) for end-to-end key- word spotting from raw audio to final recogni- tion results, without using traditional feature extraction based on …...