[PDF] Top 20 Recognizing Textual Entailment based on Deep Learning Approach
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Recognizing Textual Entailment based on Deep Learning Approach
... machine learning models that achieve excellent performance on difficult problems such as speech recognition and visual object ...building deep and complex encoder to transform a sentence into encoded ... See full document
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Recognizing Partial Textual Entailment
... partial textual entail- ...for recognizing (complete) textual entailment can be successfully adapted to this ...for recognizing complete entailment in a semi-automatic setting, ... See full document
5
Recognizing Textual Entailment using Dependency Analysis and Machine Learning
... and deep semantic features using logical inference to build a hybrid model that achieves an accuracy of ...for textual entailment based on graph matching theory applied to syntactic dependency ... See full document
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Performance Impact Caused by Hidden Bias of Training Data for Recognizing Textual Entailment
... a learning-centered approach including neural network (NN) is ...the learning result of Bayesian ...other learning-centered ...metrics based on multiple human annotation results were ... See full document
6
Towards Component Based Textual Entailment
... Although several approaches to face this task have been experimented, and progresses in TE tech- nologies have been shown in RTE evaluation campaigns, a renewed interest is rising in the TE community towards a deeper and ... See full document
5
A Study of Machine Learning Algorithms for Recognizing Textual Entailment
... We created a metric based on Wordnet to try to capture the semantic similarity between T and H to sentence level. Longest common substring is selected because is easy to implement and provides a good measure for ... See full document
6
Multi Task Learning for Semantic Relatedness and Textual Entailment
... several deep learning models have been successfully proposed and have been applied to solve different Natural Language Processing (NLP) ...problem based on single-task super- vised learning ... See full document
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Controlling the effect of crowd noisy annotations in NLP Tasks
... Chapter 2 - Background Work and Concepts - This chapter provides a general overview on supervised learning approaches and Support Vector Machines (SVMs) in particular. Specifically, we focused on classification ... See full document
142
Inference Rules and their Application to Recognizing Textual Entailment
... of recognizing textual ...our approach on the recognizing textual entailment data shows promising results on precision and the error analysis suggests possible im- ... See full document
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A mostly unlexicalized model for recognizing textual entailment
... ing entailment relations have employed classi- fiers over hand engineered lexicalized features, or deep learning models that implicitly cap- ture lexicalization through word ...determine ... See full document
6
A Semantic Approach to Recognizing Textual Entailment
... We believe that a logic-based semantic approach is highly appropriate for the RTE task 1 . Text T seman- tically entails H if its meaning logically implies the meaning of H . Because the set of semantic ... See full document
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A Machine Learning Approach for Recognizing Textual Entailment in Spanish
... This paper presents a system that uses ma- chine learning algorithms for the task of re- cognizing textual entailment in Spanish language. The datasets used include SPARTE Corpus and a translated ... See full document
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A Logic Based Semantic Approach to Recognizing Textual Entailment
... for entailment is not to start from or include too general concepts 7 ...weight based on its relative position within its hi- erarchy and on its frequency in a large ... See full document
8
Análise de Medidas de Similaridade Semântica na Tarefa de Reconhecimento de Implicação Textual (Analysis of Semantic Similarity Measures in the Recognition of Textual Entailment Task)[In Portuguese]
... [Lai and Hockenmaier 2014] descreve o sistema vencedor da tarefa de RTE no SEME- VAL em 2014 para lingua inglesa. O sistema combina diferentes fontes semˆanticas para predizer a relac¸˜ao e implicac¸˜ao textual. ... See full document
10
Advanced Machine Learning Approach: Deep Learning
... machine learning is undergoing its golden age as deep learning becomes gradually the pioneer in this ...field. Deep learning uses multiple layers to represent information abstractions ... See full document
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Prediction of a Movie’s Success From Plot Summaries Using Deep Learning Models
... Movie industry is a huge sector within the en- tertainment industry. The global movie box of- fice revenue is predicted to reach nearly 50 bil- lion U.S dollars in 2020 (Sachdev et al., 2018). With huge capital ... See full document
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Abductive Reasoning with a Large Knowledge Base for Discourse Processing
... The lower performance of the system using the KB based on axioms extracted from extended Word- Net can be easily explained. At the moment we define non-merge constraints (see section 2) for the input propositions ... See full document
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An overview of Natural Language Inference Data Collection: The way forward?
... way entailment tasks), most of the examples do not involve deep semantic inference but are rather complicated in terms of their ...as Deep Learning approaches ... See full document
7
Multimodal DBN for Predicting High Quality Answers in cQA portals
... Table 3: Comparing results on YAHOO It is promising to see that the proposed mDBN method notably outperforms almost all the other methods on both datasets over all the metrics as expected, except for the recall on ... See full document
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Solving the Problem of Cascading Errors: Approximate Bayesian Inference for Linguistic Annotation Pipelines
... At first glance, k-best lists may seem like they should outperform sampling, because in effect they are the k best samples. However, there are several important reasons why one might prefer sampling. One reason is that ... See full document
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