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Workshops held at the 16th European Conference on Computer Vision, ECCV 2020

作者： Liang, Xing Angelopoulou, Anastassia Kapetanios, Epaminondas Woll, Bencie Al Batat, Reda Woolfe, Tyron IoT and Security Research Group University of Greenwich London United Kingdom Cognitive Computing Research Lab University of Westminster London United Kingdom Deafness Cognition and Language Research Centre University College London London United Kingdom

ISBN: (纸本)9783030660956

The ageing population trend is correlated with an increased prevalence of acquired cognitive impairments such as dementia. Although there is no cure for dementia, a timely diagnosis helps in obtaining necessary support and appropriate medication. Researchers are working urgently to develop effective technological tools that can help doctors undertake early identification of cognitive disorder. In particular, screening for dementia in ageing Deaf signers of British Sign language (BSL) poses additional challenges as the diagnostic process is bound up with conditions such as quality and availability of interpreters, as well as appropriate questionnaires and cognitive tests. On the other hand, deep learning based approaches for image and video analysis and understanding are promising, particularly the adoption of Convolutional Neural Network (CNN), which require large amounts of training data. In this paper, however, we demonstrate novelty in the following way: a) a multi-modal machine learning based automatic recognition toolkit for early stages of dementia among BSL users in that features from several parts of the body contributing to the sign envelope, e.g., hand-arm movements and facial expressions, are combined, b) universality in that it is possible to apply our technique to users of any sign language, since it is language independent, c) given the trade-off between complexity and accuracy of machine learning (ML) prediction models as well as the limited amount of training and testing data being available, we show that our approach is not over-fitted and has the potential to scale up. © 2020, Springer Nature Switzerland AG.

关键词： Surveys

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Machine translation of Chinese long sentences based on recognition of implicit period and comma 23

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23rd Chinese National Conference on Computational Linguistics, CCL 2024

作者： Zhang, Wenjuan Li, Manjia Feng, Wenhe Lab of language engineering and computing Center for Linguistics and Applied Linguistics Guangdong University of Foreign Studies China

ISBN: (纸本)9780000000002

The translation of long sentences has always been a difficult problem for machine translation. In this paper, based on the feature that a considerable number of commas (intra-sentence punctuation) and periods (inter-sentence punctuation) in Chinese text can be transformed into each other, we propose the concepts of "implicit period" (comma that can be transformed into period) and "implicit comma" (period that can be transformed into comma), and realize their automatic recognition to transform Chinese long sentences into short sentences for Chinese-English machine translation. In this paper, a dataset of implicit period and comma is constructed by combining manual and semi-supervised learning methods, and an implicit period and comma recognition method is realized based on a pre-trained model, in which Hierarchical BERT, which has the best performance, is used as the subsequent application model. In this paper, a Chinese-English machine translation method based on implicit period and comma recognition is realized. The experiments based on pre-trained machine translation models on the WMT2018 (News) and WMT2023 (Literature) corpus show that for the English translation of long Chinese sentences, the method in this paper improves the BLEU value compared to the benchmark translation as a whole, and the effect of the method in this paper is more obvious the longer the sentence is for the relatively robust machine translation model. © 2024 China National Conference on Computational Linguistics.

关键词： Machine translation

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COFFEE: A Contrastive Oracle-Free Framework for Event Extraction

arXiv

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arXiv 2023年

作者： Zhang, Meiru Su, Yixuan Meng, Zaiqiao Fu, Zihao Collier, Nigel Language Technology Lab University of Cambridge United Kingdom School of Computing Science University of Glasgow United Kingdom

Event extraction is a complex task that involves extracting events from unstructured text. Prior classification-based methods require comprehensive entity annotations for joint training, while newer generation-based methods rely on heuristic templates containing oracle information such as event type, which is often unavailable in real-world scenarios. In this study, we consider a more realistic task setting, namely the Oracle-Free Event Extraction (OFEE) task, where only the input context is given, without any oracle information including event type, event ontology, or trigger word. To address this task, we propose a new framework, COFFEE. This framework extracts events solely based on the document context, without referring to any oracle information. In particular, COFFEE introduces a contrastive selection model to refine the generated triggers and handle multi-event instances. Our proposed COFFEE outperforms state-of-the-art approaches in the oracle-free setting of the event extraction task, as evaluated on two public variants of the ACE05 benchmark. The code used in our study has been made publicly available. © 2023, CC BY.

关键词： Classification (of information)

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TKLBLIIR: Detecting Twitter Paraphrases with TweetingJay 9

TKLBLIIR: Detecting Twitter Paraphrases with TweetingJay

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9th International Workshop on Semantic Evaluation, SemEval 2015

作者： Karan, Mladen Glavaš, Goran Šnajder, Jan Bašić, Bojana Dalbelo Vulić, Ivan Moens, Marie-Francine University of Zagreb Faculty of Electrical Engineering and Computing Text Analysis and Knowledge Engineering Lab Unska 3 Zagreb10000 Croatia KU Leuven Department of Computer Science Language Intelligence Information Retrieval Group Celestijnenlaan 200A Leuven Belgium

ISBN: (纸本)9781941643402

When tweeting on a topic, Twitter users often post messages that convey the same or similar meaning. We describe TweetingJay, a system for detecting paraphrases and semantic similarity of tweets, with which we participated in Task 1 of SemEval 2015. TweetingJay uses a supervised model that combines semantic overlap and word alignment features, previously shown to be effective for detecting semantic textual similarity. TweetingJay reaches 65.9% F1-score and ranked fourth among the 18 participating systems. We additionally provide an analysis of the dataset and point to some peculiarities of the evaluation setup. © 2015 Association for Computational Linguistics

关键词： Semantics

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Biomedical Named Entity Recognition via Dictionary-based Synonym Generalization

arXiv

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arXiv 2023年

作者： Fu, Zihao Su, Yixuan Meng, Zaiqiao Collier, Nigel Language Technology Lab University of Cambridge United Kingdom Cohere School of Computing Science University of Glasgow United Kingdom

Biomedical named entity recognition is one of the core tasks in biomedical natural language processing (BioNLP). To tackle this task, numerous supervised/distantly supervised approaches have been proposed. Despite their remarkable success, these approaches inescapably demand laborious human effort. To alleviate the need of human effort, dictionary-based approaches have been proposed to extract named entities simply based on a given dictionary. However, one downside of existing dictionary-based approaches is that they are challenged to identify concept synonyms that are not listed in the given dictionary, which we refer as the synonym generalization problem. In this study, we propose a novel Synonym Generalization (SynGen) framework that recognizes the biomedical concepts contained in the input text using span-based predictions. In particular, SynGen introduces two regularization terms, namely, (1) a synonym distance regularizer;and (2) a noise perturbation regularizer, to minimize the synonym generalization error. To demonstrate the effectiveness of our approach, we provide a theoretical analysis of the bound of synonym generalization error. We extensively evaluate our approach on a wide range of benchmarks and the results verify that SynGen outperforms previous dictionary-based models by notable margins. Lastly, we provide a detailed analysis to further reveal the merits and inner-workings of our approach. © 2023, CC BY.

关键词： Natural language processing systems

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Learning a stochastic part of speech tagger for sinhala

Learning a stochastic part of speech tagger for sinhala

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International Conference on Advances in ICT for Emerging Regions

作者： M. Jayasuriya A. R. Weerasinghe Virtusa (Pvt) Ltd Sri Lanka Language Technology Research Lab University of Colombo School of Computing Sri Lanka

This paper presents the results of developing a part of speech (POS) tagger for Sinhala. The tagger is able to handle lexical items with multiple POS tags while also predicting POS tags of previously unseen words. A stochastic approach, Hidden Markov Model (HMM) with tri-gram probabilities was used as the training and tagging model. Linear Interpolation is used to smoothen the tri-gram probabilities while the Viterbi algorithm is used to decode the results of the HMM to decide on the best POS tags for each word. The tagger learns the lexical items (words and their possible POS tags) and the tri-gram probabilities using a POS tag annotated corpus. The tagger achieved an overall accuracy of 62%. Approximately 24% of the errors were for words whose POS tags have been unknown in the corpus. The lack of a Named Entity recognizer has also contributed to 10% of the overall error.

关键词： Hidden Markov models Tagging Accuracy Training Speech Probability Stochastic processes

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HiEve: A corpus for extracting event hierarchies from news stories 9

HiEve: A corpus for extracting event hierarchies from news s...

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9th International Conference on language Resources and Evaluation, LREC 2014

作者： Glavaš, Goran Šnajder, Jan Kordjamshidi, Parisa Moens, Marie-Francine University of Zagreb Faculty of Electrical Engineering and Computing Text Analysis and Knowledge Engineering Lab. Unska 3 Zagreb10000 Croatia KU Leuven Department of Computer Science Language Intelligence and Information Retrieval Group Celestijnenlaan 200A LeuvenB-3000 Belgium

ISBN: (纸本)9782951740884

In news stories, event mentions denote real-world events of different spatial and temporal granularity. Narratives in news stories typically describe some real-world event of coarse spatial and temporal granularity along with its subevents. In this work, we present HiEve, a corpus for recognizing relations of spatiotemporal containment between events. In HiEve, the narratives are represented as hierarchies of events based on relations of spatiotemporal containment (i.e., superevent-subevent relations). We describe the process of manual annotation of HiEve. Furthermore, we build a supervised classifier for recognizing spatiotemporal containment between events to serve as a baseline for future research. Preliminary experimental results are encouraging, with classifier performance reaching 58% F1-score, only 11% less than the inter-annotator agreement.

关键词： Event hierarchies Relation extraction Spatiotemporal containment

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Automatically creating bilingual lexicons for machine translation from bilingual text 98

Automatically creating bilingual lexicons for machine transl...

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36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, COLING-ACL 1998

作者： Turcato, Davide Natural Language Lab School of Computing Science Simon Fraser University BurnabyBCV5A 1S6 Canada TCC Communications 100-6722 Oldfield Road VictoriaBCV8M 2A3 Canada

A method is presented for automatically augmenting the bilingual lexicon of an existing Machine Translation system, by extracting bilingual entries from aligned bilingual text. The proposed method only relies on the resources already available in the MT system itself. It is based on the use of bilingual lexical templates to match the terminal symbols in the parses of the aligned sentences. © 1998 Universit~ de Montreal.

关键词： Computer aided language translation

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Negation Disambiguation Using the Maximum Entropy Model

Negation Disambiguation Using the Maximum Entropy Model

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The 6th International Conference on Natural language Processing and Knowledge Engineering(第六届IEEE自然语言处理与知识工程国际会议 NLP-KE 2010)

作者： Chunliang ZHANG Xiaoxu FEI Jingbo ZHU Natural Language Lab Northeastern University Shenyang China Foreign Studies CollegeNortheastern Un Key Laboratory of Medical　Image Computing (Northeastern　University Ministry of　Education)　Shenyang Natural Language Lab Northeastern University Shenyang China

ISBN: (纸本)9781424468973

Handling negation issue is of great significance for sentiment analysis. Most previous studies adopted a simple heuristic rule for sentiment negation disambiguation within a fixed context window. In this paper we present a supervised method to disambiguate which sentiment word is attached to the negator such as “ 不(not)” in an opinionated sentence. Experimental results show that our method can achieve better performance than traditional methods.

关键词： Negator relation pair sentiment negation disambiguation

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Audio-Driven High Definetion and Lip-Synchronized Talking Face Generation Based on Face Reenactment 48

Audio-Driven High Definetion and Lip-Synchronized Talking Fa...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Wang, Xianyu Zhang, Yuhan He, Weihua Wang, Yaoyuan Li, Minglei Wang, Yuchen Zhang, Jingyi Zhou, Shunbo Zhang, Ziyang Huawei Technologies Co. Ltd. Advanced Computing and Storage Lab China Huawei Technologies Co. Ltd. Language & Speech Innovation Lab China Huawei Technologies Co. Ltd. Nce Dept. China Huawei Technologies Co. Ltd. Ascend Lab China Huawei Technologies Co. Ltd. Edge Cloud Innovation Lab China

ISBN: (纸本)9781728163277

Generating audio-driven photo-realistic talking face has received intensive attention due to its ability to bring more new human-computer interaction experiences. However, previous works struggled to balance high definition, lip synchronization, and low customization costs, which would degrade the user experience. In this paper, a novel audio-driven talking face generation method was proposed, which subtly converts the problem of improving video definition into the problem of face reenactment to produce both lip-synchronized and high- definition face video. The framework is decoupled, meaning that the same trained model can be used on arbitrary characters and audio without further customizing training for specific people, thus significantly reducing costs. Experiment results show that our proposed method achieves the high video definition, and comparable lip synchronization performance with the existing state-of-the-art methods. © 2023 IEEE.

关键词： Human computer interaction

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