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arXiv 2020年

作者： Liang, Yunlong Meng, Fandong Zhang, Jinchao Chen, Yufeng Xu, Jinan Zhou, Jie Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China Pattern Recognition Center WeChat AI Tencent Inc. China

Aspect-based sentiment analysis (ABSA) mainly involves three subtasks: aspect term extraction, opinion term extraction, and aspect-level sentiment classification, which are typically handled in a separate or joint manner. However, previous approaches do not well exploit the interactive relations among three subtasks and do not pertinently leverage the easily available document-level labeled domain/sentiment knowledge, which restricts their performances. To address these issues, we propose a novel Iterative Multi-Knowledge Transfer Network (IMKTN) for end-to-end ABSA. For one thing, through the interactive correlations between the ABSA subtasks, our IMKTN transfers the task-specific knowledge from any two of the three subtasks to another one at the token level by utilizing a well-designed routing algorithm, that is, any two of the three subtasks will help the third one. For another, our IMKTN pertinently transfers the document-level knowledge, i.e., domain-specific and sentiment-related knowledge, to the aspect-level subtasks to further enhance the corresponding performance. Experimental results on three benchmark datasets demonstrate the effectiveness and superiority of our approach. Copyright © 2020, The Authors. All rights reserved.

关键词： Sentiment analysis

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A knowledge-driven generative model for multi-implication Chinese medical procedure entity normalization

A knowledge-driven generative model for multi-implication Ch...

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2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020

作者： Yan, Jinghui Wang, Yining Xiang, Lu Zhou, Yu Zong, Chengqing School of Computer Science and Information Technology Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China National Laboratory of Pattern Recognition Institute of Automation CAS Beijing China University of Chinese Academy of Sciences Beijing China CAS Center for Excellence in Brain Science and Intelligence Technology Beijing China Beijing Fanyu Technology Co. Ltd China

ISBN: (纸本)9781952148606

Medical entity normalization, which links medical mentions in the text to entities in knowledge bases, is an important research topic in medical natural language processing. In this paper, we focus on Chinese medical procedure entity normalization. However, nonstandard Chinese expressions and combined procedures present challenges in our problem. The existing strategies relying on the discriminative model are poorly to cope with normalizing combined procedure mentions. We propose a sequence generative framework to directly generate all the corresponding medical procedure entities. we adopt two strategies: category-based constraint decoding and category-based model refining to avoid unrealistic results. The method is capable of linking entities when a mention contains multiple procedure concepts and our comprehensive experiments demonstrate that the proposed model can achieve remarkable improvements over existing baselines, particularly significant in the case of multi-implication Chinese medical procedures. © 2020 Association for Computational Linguistics

关键词： Natural language processing systems

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UO UPV2 at HAHA 2019: Bigru neural network informed with linguistic features for humor recognition

UO UPV2 at HAHA 2019: Bigru neural network informed with lin...

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2019 Iberian Languages Evaluation Forum, IberLEF 2019

作者： Ortega-Bueno, Reynier Rosso, Paolo Medina Pagola, José E. Center for Pattern Recognition and Data Mining Universidad de Oriente Santiago de Cuba Cuba PRHLT Research Center Universitat Politècnica de València Valencia Spain University of Informatics Sciences Havana Cuba

Verbal humor is an illustrative example of how humans use creative language to produce funny content. We, as human being, access to humor or comicality with the purpose of projecting more complex meanings which, usually, represent a real challenge, not only for computers, but for humans as well. For that, understanding and recognizing humorous content automatically has been and continue being an important issue in Natural Language Processing (NLP) and even more in Cognitive Computing. In order to addressing this challenge, in this paper we describe our UO UPV2 system developed for participating in the second edition of the HAHA (Humor Analysis based on Human Annotation) task proposed at IberLEF 2019 Forum. Our starting point was the UO UPV system we participated in HAHA 2018 with some modification in its architecture. This year we explored other way to inform our Attention based Recurrent Neural Network model with linguistic knowledge. Experimental results show that our system achieves positive results ranked 7th out of 18 teams. © 2019 CEUR-WS. All rights reserved.

关键词： Classification (of information)

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Overview of the task on irony detection in Spanish variants

Overview of the task on irony detection in Spanish variants

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2019 Iberian Languages Evaluation Forum, IberLEF 2019

作者： Ortega-Bueno, Reynier Rangel, Francisco Farías, Delia Irazú Hernández Rosso, Paolo Montes-Y-Gómez, Manuel Medina-Pagola, José E. Center for Pattern Recognition and Data Mining University of Oriente Cuba Autoritas Consulting S.A. Spain PRHLT Research Center Universitat Politècnica de València Spain Mexico University of Informatics Science Havana Cuba

This paper introduces IroSvA, the first shared task fully dedicated to identify the presence of irony in short messages (tweets and news comments) written in three different variants of Spanish. The task consists in: given a message, automatic systems should recognize whether the message is ironic or not. Moreover, with respect to the previous tasks on irony detection, the messages are not considered as isolated texts but together with a given context (e.g. a headline or a topic). The task is comprised by three different subtasks: i) irony detection in tweets from Spain, ii) irony detection in Mexican tweets, and iii) irony detection in news comments from Cuba. These subtasks aim at studying the way irony changes across the distinct Spanish variants. We received 14 submissions from 12 teams. Participating systems were evaluated against the test dataset using F1 macro averaged. The highest classification scores obtained for the three subtasks are F1=0.7167, F1=0.6803, and F1=0.6596, respectively. © 2019 CEUR-WS. All rights reserved.

关键词： Statistical tests

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Attention mechanism for aggressive detection 3

Attention mechanism for aggressive detection

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3rd Workshop on Evaluation of Human Language Technologies for Iberian Languages, IberEval 2018

作者： Enrique Muñiz Cuza, Carlos Liz De la Peña Sarracén, Gretel Rosso, Paolo Center for Pattern Recognition and Data Mining Cuba PRHLT Universitat Politècnica de València Spain

This paper describes the system we developed for IberEval 2018 on Aggressive detection track of Authorship and Aggressiveness Analysis on Twitter (MEX-A3T)1. The task focuses on the detection of aggressive comments in tweets that come from Mexican users. Systems must be able to determine whether a tweet is aggressive or not. Our approach is an Attention-based Long Short-Term Memory Network Recurrent Neural Network where the attention layer helps to calculate the contribution of each word towards targeted aggressive classes. In particular, we build a Bidireccional LSTM to extract information from the word embeddings over the sentence, then apply attention over the hidden states to estimate the importance of each word and finally feed this context vector to another LSTM model to estimate whether the tweet is aggressive or not. The experimental results show that our model achieves outstanding results. © 2018 CEUR-WS. All Rights Reserved.

关键词： Long short-term memory

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Automatic text summarization based on betweenness centrality 18

Automatic text summarization based on betweenness centrality

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5th Spanish Conference on Information Retrieval, CERI 2018

作者： De La Peña Sarracén, Gretel Liz Rosso, Paolo Center for Pattern Recognition and Data Mining Cuba PRHLT Universitat Politècnica de València Spain

ISBN: (纸本)9781450365437

Automatic text summary plays an important role in information retrieval. With a large volume of information, presenting the user only a summary greatly facilitates the search work of the most relevant. Therefore, this task can provide a solution to the problem of information overload. Automatic text summary is a process of automatically creating a compressed version of a certain text that provides useful information for users. This article presents an unsupervised extractive approach based on graphs. The method constructs an indirected weighted graph from the original text by adding a vertex for each sentence, and calculates a weighted edge between each pair of sentences that is based on a similarity/dissimilarity criterion. The main contribution of the work is that we do a study of the impact of a known algorithm for the social network analysis, which allows to analyze large graphs efficiently. As a measure to select the most relevant sentences, we use betweenness centrality. The method was evaluated in an open reference data set of DUC2002 with Rouge scores. 2018 Association for Computing Machinery.

关键词： Text processing

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Uo Iro: Linguistic informed deep-learning model for irony detection 6

Uo Iro: Linguistic informed deep-learning model for irony de...

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6th Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop, EVALITA 2018

作者： Ortega-Bueno, Reynier Medina Pagola, José E. Center for Pattern Recognition and Data Mining Santiago de Cuba Cuba Computer Science Department University of Oriente Venezuela University of Informatics Sciences Havana Cuba

This paper describes our UO - IRO system developed for participating in the shared task IronITA, organized within EVALITA: 2018 Workshop. Our approach is based on a deep learning model informed with linguistic knowledge. Specifically, a Convolutional (CNN) and Long Short Term Memory (LSTM) neural network are ensembled, also, the model is informed with linguistics information incorporated through its second to last hidden layer. Results achieved by our system are encouraged, however a more fine-tuned hyper-parameters setting is required for improving the model’s effectiveness. © 2018 CEUR-WS. All Rights Reserved.

关键词： Long short-term memory

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UO UPV: Deep linguistic humor detection in Spanish social media 3

UO UPV: Deep linguistic humor detection in Spanish social me...

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3rd Workshop on Evaluation of Human Language Technologies for Iberian Languages, IberEval 2018

作者： Ortega-Bueno, Reynier Muñiz-Cuza, Carlos E. Medina Pagola, José E. Rosso, Paolo Center for Pattern Recognition and Data Mining Santiago de Cuba Cuba University of Informatics Sciences Havana Cuba PRHLT Research Center Universitat Politècnica de València Valencia Spain

Natural Language Understanding becomes very hard when creativity and figurative language are used in social communication. Humor constitutes an illustrative example of how humans use creative language to produce funny content. Therefore, create new methods and resources for analyzing properly humorous texts is an important issue in Natural Language Processing (NLP) and even more in Human Computer Interaction (HCI). In this sense, this paper introduces our UO UPV system developed for the Humor Analysis based on Human Annotation (HAHA) track proposed in IberEval 2018 Workshop. The task focuses on classifying tweets in Spanish as humorous or not, and predicting how funny they are. To solve this task, our proposal combines both linguistic features and an Attention-based Recurrent Neural Network, where the attention layer helps to calculate the contribution of each term towards targeted humorous classes. Experimental results show that our model achieves encourage results. © 2018 CEUR-WS. All Rights Reserved.

关键词： Recurrent neural networks

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Ensembles of methods for tweet topic Classification 2

Ensembles of methods for tweet topic Classification

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2nd Workshop on Evaluation of Human Language Technologies for Iberian Languages, IberEval 2017

作者： De La Peña Sarracén, Gretel Liz Center for Pattern Recognition and Data Mining Cuba

This paper describes the system we developed for IberEval 2017 on Classification Of Spanish Election Tweets (COSET) task. Our approach is based on a weighted average ensemble of five classifiers: 1) a classifier based on logistic regression;2) a support vector machine classi fier;3) a Naive Bayes classifier for multinomial models;4) a Guassian Naive Bayes classifier;and 5) a classifier implementing the k-nearest neighbors vote. Each such classifier was choice taking into account its contributes to the success of the system. The aim is to design a approach by using a voting method, where individual classifiers can have weaknesses. The performance of the ensemble is compared to the individual classifiers, and the experimental results show that the ensemble has better results.

关键词： Nearest neighbor search

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$\ell_0$ Sparse Approximation of Coastline Inflection Method on FY-3C MWRI data

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IEEE Geoscience and Remote Sensing Letters 2019年第1期16卷 85-89页

作者： Weifu Li Zhicheng Luo Chengbao Liu Jiazheng Liu Lijun Shen Qiwei Xie Hua Han Lei Yang Faculty of Mathematics and Statistics Hubei University Wuhan China National Satellite Meteorological Center China Meteorological Administration Beijing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China Data Mining Laboratory Beijing University of Technology Beijing China

The microwave radiation imager (MWRI) located onboard the FengYun-3C (FY-3C) satellite provides a considerable amount of critical information for numerical weather predictions. Obtaining accurate geolocation results from the FY3C MWRI data is of great importance. In this letter, we improve the traditional coastline inflection method (CIM) and propose an ℓ 0 sparse approximation model for geolocation error estimation and correction. Specifically, we propose using the jump point of the step function to estimate the true coastline point. This approach can characterize the geolocation errors more accurately than the CIM, which further improves the geolocation accuracy. In the theoretical part, we provide a complete solution to obtain the step function through an iterative blind deconvolution. For a practical use, we demonstrate the effectiveness of the proposed method for geolocation error estimation through quantitative results obtained on the FY-3C MWRI data. The experimental results show that the proposed method can achieve an improvement of up to 33.33% in the standard deviation of geolocation errors (approximately 0.00030) compared to the traditional CIM (approximately 0.00045). Furthermore, we also apply the proposed method to the FY-3C satellite and improve the geolocation accuracy of the MWRI data through geolocation error correction.

关键词： Geology Satellite broadcasting Ocean temperature Temperature measurement Instruments Mathematical model Terrestrial atmosphere

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