Глубокая нейронная сеть

In this paper, we explore a novel bilin- gual word alignment approach based on DNN (Deep Neural Network), which has been proven to be very effective in var- ious machine …...

Detecting opinion relation is a crucial step for fine-gained opinion summarization. A valid opin- ion relation has three requirements: a correct opinion word, a correct opinion target and the linking …...

This paper describes the system submit- ted by the University of Heidelberg to the Shared Task on Word-level Quality Esti- mation at the 2015 Workshop on Statis- tical Machine Translation. …...

AbstractCommunity Question Answering (cQA) forums have become a popular medium for soliciting direct answers to specific questions of users from experts or other experienced users on a given topic. However, …...

AbstractOn microblogging services, people usually use hashtags to mark microblogs, which have a specific theme or content, making them easier for users to find. Hence, how to automatically recommend hashtags …...

AbstractNatural-language processing of historical documents is complicated by the abundance of variant spellings and lack of annotated data. A common approach is to normalize the spelling of historical words to …...

AbstractThe multi-level adaptive networks (MLAN) technique is a cross-lingual adaptation framework where a bottleneck (BN) layer in a deep neural network (DNN) trained in a source language is used for …...

We introduce a deep neural network for automated sarcasm detection. Recent work has emphasized the need for mod- els to capitalize on contextual features, be- yond lexical and syntactic cues …...

AbstractIn this paper we propose an approach to predict punctuation marks for unsegmented speech transcript. The approach is purely lexical, with pre-trained Word Vectors as the only input. A training …...

AbstractFor automatic chatting systems, it is indeed a great challenge to reply the given query considering the conversation history, rather than based on the query only. This paper proposes a …...

AbstractRecognizing Textual Entailment (RTE) is a fundamentally important task in natural language processing that has many applications. The recently released Stanford Natural Language Inference (SNLI) corpus has made it possible …...

In this paper, we propose a deep neural net- work based natural language processing sys- tem for semantic textual similarity prediction. We leverage multi-layer bidirectional LSTM to learn sentence representation. …...

We propose a deep neural network based nat- ural language processing system for clinical information (such as time information, event spans, and their attributes) extraction from raw clinical notes and …...

AbstractGiven the constantly growing proliferation of false claims online in recent years, there has been also a growing research interest in automatically distinguishing false rumors from factually true claims. Here, …...

AbstractIn this paper, a new deep reinforcement learning based augmented general tagging system is proposed. The new system contains two parts: a deep neural network (DNN) based sequence labeling model …...

AbstractSatirical news detection is important in order to prevent the spread of misinformation over the Internet. Existing approaches to capture news satire use machine learning models such as SVM and …...

The paper presents several configurations of deep neural networks aimed at the task of coreference resolution for Polish. Starting with the basic feature set and standard word embedding vector size …...

AbstractWord vector specialisation (also known as retrofitting) is a portable, light-weight approach to fine-tuning arbitrary distributional word vector spaces by injecting external knowledge from rich lexical resources such as WordNet. …...

AbstractDeep neural network models such as long short-term memory (LSTM) and tree-LSTM have been proven to be effective for sentiment analysis. However, sequential LSTM is a bias model wherein the …...

AbstractIn this paper, we present two methods to identify and categorize the offensive language in Twitter. In the first method, we establish a probabilistic model to evaluate the sentence offensiveness …...

AbstractReferring Expression Generation (REG) is the task of generating contextually appropriate references to entities. A limitation of existing REG systems is that they rely on entity-specific supervised training, which means …...

AbstractAutomatic sarcasm detection from text is an important classification task that can help identify the actual sentiment in user-generated data, such as reviews or tweets. Despite its usefulness, sarcasm detection …...

AbstractThis paper describes the systems submitted by the Arabic Language Technology group (ALT) at SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media. We focus on sub-task A (Offensive …...

AbstractIn this paper, we investigate the effectiveness of using rich annotations in deep neural network (DNN)-based statistical speech synthesis. DNN-based frameworks typically use linguistic information as input features called context …...

AbstractExtracting event temporal relations is a critical task for information extraction and plays an important role in natural language understanding. Prior systems leverage deep learning and pre-trained language models to …...

AbstractWe present an annotated corpus of English cooking recipe procedures, and describe and evaluate computational methods for learning these annotations. The corpus consists of 300 recipes written by members of …...

AbstractThis paper describes our contribution to SemEval-2020 Task 7: Assessing Humor in Edited News Headlines. Here we present a method based on a deep neural network. In recent years, quite …...

AbstractIn natural languages, words are used in association to construct sentences. It is not words in isolation, but the appropriate use of hierarchical structures that conveys the meaning of the …...

In this research, we developed the Taiwanese speech recognition system which used the Kaldi toolkit to implement. The Taiwanese corpus was collected by Taiwan Taiwanese National Reading Competition and Classmate …...

AbstractThe task of Dialogue Act Classification (DAC) that purports to capture communicative intent has been studied extensively. But these studies limit themselves to text. Non-verbal features (change of tone, facial …...

AbstractIdentifying statements related to suicidal behaviour in psychiatric electronic health records (EHRs) is an important step when modeling that behaviour, and when assessing suicide risk. We apply a deep neural …...

AbstractBuilding an interpretable AI diagnosis system for breast cancer is an important embodiment ofAI assisted medicine. Traditional breast cancer diagnosis methods based on machine learning areeasy to explain but the …...

AbstractThe masking-based speech enhancement method pursues a multiplicative mask that applies to the spectrogram of input noise-corrupted utterance, and a deep neural network (DNN) is often used to learn the …...

In this paper, we describe the system team NSYSU-MITLab implemented for Formosa Speech Recognition Challenge 2020. We use the Transformer architecture  國立中山大學資訊工程學系 Department of Computer Science and Information Engineering …...

AbstractMetaphor detection plays an important role in tasks such as machine translation and human-machine dialogue. As more users express their opinions on products or other topics on socialmedia through metaphorical …...

AbstractLarge datasets as required for deep learning of lip reading do not exist in many languages. In this paper we present the dataset GLips (German Lips) consisting of 250,000 publicly …...

AbstractNews recommender systems face certain challenges. These challenges arise due to evolving users’ preferences over dynamically created news articles. The diversity is necessary for a news recommender system to expose …...

One of the most important factors that sig- nificantly affects the quality of sequence la- beling models is the selection and encod- ing of input features. The complexity and morphological …...

AbstractWhen working with textual data, a natural application of disentangled representations is the fair classification where the goal is to make predictions without being biased (or influenced) by sensible attributes …...