Paper Group NANR 168
Automatic Curation and Visualization of Crime Related Information from Incrementally Crawled Multi-source News Reports. Do Character-Level Neural Network Language Models Capture Knowledge of Multiword Expression Compositionality?. A Language Model based Evaluator for Sentence Compression. Learning to Understand Image Blur. A Lexicon of Discourse Ma …
Automatic Curation and Visualization of Crime Related Information from Incrementally Crawled Multi-source News Reports
| Title | Automatic Curation and Visualization of Crime Related Information from Incrementally Crawled Multi-source News Reports |
| Authors | Tirthankar Dasgupta, Lipika Dey, Rupsa Saha, Abir Naskar |
| Abstract | In this paper, we demonstrate a system for the automatic extraction and curation of crime-related information from multi-source digitally published News articles collected over a period of five years. We have leveraged the use of deep convolution recurrent neural network model to analyze crime articles to extract different crime related entities and events. The proposed methods are not restricted to detecting known crimes only but contribute actively towards maintaining an updated crime ontology. We have done experiments with a collection of 5000 crime-reporting News articles span over time, and multiple sources. The end-product of our experiments is a crime-register that contains details of crime committed across geographies and time. This register can be further utilized for analytical and reporting purposes. |
| Tasks | Entity Resolution |
| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/C18-2023/ |
| https://www.aclweb.org/anthology/C18-2023 | |
| PWC | https://paperswithcode.com/paper/automatic-curation-and-visualization-of-crime |
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Do Character-Level Neural Network Language Models Capture Knowledge of Multiword Expression Compositionality?
| Title | Do Character-Level Neural Network Language Models Capture Knowledge of Multiword Expression Compositionality? |
| Authors | Ali Hakimi Parizi, Paul Cook |
| Abstract | In this paper, we propose the first model for multiword expression (MWE) compositionality prediction based on character-level neural network language models. Experimental results on two kinds of MWEs (noun compounds and verb-particle constructions) and two languages (English and German) suggest that character-level neural network language models capture knowledge of multiword expression compositionality, in particular for English noun compounds and the particle component of English verb-particle constructions. In contrast to many other approaches to MWE compositionality prediction, this character-level approach does not require token-level identification of MWEs in a training corpus, and can potentially predict the compositionality of out-of-vocabulary MWEs. |
| Tasks | Machine Translation |
| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/W18-4920/ |
| https://www.aclweb.org/anthology/W18-4920 | |
| PWC | https://paperswithcode.com/paper/do-character-level-neural-network-language |
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A Language Model based Evaluator for Sentence Compression
| Title | A Language Model based Evaluator for Sentence Compression |
| Authors | Yang Zhao, Zhiyuan Luo, Akiko Aizawa |
| Abstract | We herein present a language-model-based evaluator for deletion-based sentence compression and view this task as a series of deletion-and-evaluation operations using the evaluator. More specifically, the evaluator is a syntactic neural language model that is first built by learning the syntactic and structural collocation among words. Subsequently, a series of trial-and-error deletion operations are conducted on the source sentences via a reinforcement learning framework to obtain the best target compression. An empirical study shows that the proposed model can effectively generate more readable compression, comparable or superior to several strong baselines. Furthermore, we introduce a 200-sentence test set for a large-scale dataset, setting a new baseline for the future research. |
| Tasks | Language Modelling, Sentence Compression |
| Published | 2018-07-01 |
| URL | https://www.aclweb.org/anthology/P18-2028/ |
| https://www.aclweb.org/anthology/P18-2028 | |
| PWC | https://paperswithcode.com/paper/a-language-model-based-evaluator-for-sentence |
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Learning to Understand Image Blur
| Title | Learning to Understand Image Blur |
| Authors | Shanghang Zhang, Xiaohui Shen, Zhe Lin, RadomÃr MÄch, João P. Costeira, José M. F. Moura |
| Abstract | While many approaches have been proposed to estimate and remove blur in a photo, few efforts were made to have an algorithm automatically understand the blur desirability: whether the blur is desired or not, and how it affects the quality of the photo. Such a task not only relies on low-level visual features to identify blurry regions, but also requires high-level understanding of the image content as well as user intent during photo capture. In this paper, we propose a unified framework to estimate a spatially-varying blur map and understand its desirability in terms of image quality at the same time. In particular, we use a dilated fully convolutional neural network with pyramid pooling and boundary refinement layers to generate high-quality blur response maps. If blur exists, we classify its desirability to three levels ranging from good to bad, by distilling high-level semantics and learning an attention map to adaptively localize the important content in the image. The whole framework is end-to-end jointly trained with both supervisions of pixel-wise blur responses and image-wise blur desirability levels. Considering the limitations of existing image blur datasets, we collected a new large-scale dataset with both annotations to facilitate training. The proposed methods are extensively evaluated on two datasets and demonstrate state-of-the-art performance on both tasks. |
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| Published | 2018-06-01 |
| URL | http://openaccess.thecvf.com/content_cvpr_2018/html/Zhang_Learning_to_Understand_CVPR_2018_paper.html |
| http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhang_Learning_to_Understand_CVPR_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/learning-to-understand-image-blur |
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A Lexicon of Discourse Markers for Portuguese – LDM-PT
| Title | A Lexicon of Discourse Markers for Portuguese – LDM-PT |
| Authors | Am{'a}lia Mendes, Iria del Rio, Manfred Stede, Felix Dombek |
| Abstract | |
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| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1693/ |
| https://www.aclweb.org/anthology/L18-1693 | |
| PWC | https://paperswithcode.com/paper/a-lexicon-of-discourse-markers-for-portuguese |
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Data Management Plan (DMP) for Language Data under the New General Da-ta Protection Regulation (GDPR)
| Title | Data Management Plan (DMP) for Language Data under the New General Da-ta Protection Regulation (GDPR) |
| Authors | Pawel Kamocki, Val{'e}rie Mapelli, Khalid Choukri |
| Abstract | |
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| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1021/ |
| https://www.aclweb.org/anthology/L18-1021 | |
| PWC | https://paperswithcode.com/paper/data-management-plan-dmp-for-language-data |
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A Flexible and Easy-to-use Semantic Role Labeling Framework for Different Languages
| Title | A Flexible and Easy-to-use Semantic Role Labeling Framework for Different Languages |
| Authors | Quynh Ngoc Thi Do, Artuur Leeuwenberg, Geert Heyman, Marie-Francine Moens |
| Abstract | This paper presents a flexible and open source framework for deep semantic role labeling. We aim at facilitating easy exploration of model structures for multiple languages with different characteristics. It provides flexibility in its model construction in terms of word representation, sequence representation, output modeling, and inference styles and comes with clear output visualization. The framework is available under the Apache 2.0 license. |
| Tasks | Feature Engineering, Semantic Role Labeling |
| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/C18-2035/ |
| https://www.aclweb.org/anthology/C18-2035 | |
| PWC | https://paperswithcode.com/paper/a-flexible-and-easy-to-use-semantic-role |
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Frame Semantics across Languages: Towards a Multilingual FrameNet
| Title | Frame Semantics across Languages: Towards a Multilingual FrameNet |
| Authors | Collin Baker, Michael Ellsworth, Miriam R. L. Petruck, Swabha Swayamdipta |
| Abstract | |
| Tasks | Question Answering, Semantic Parsing, Semantic Role Labeling, Structured Prediction |
| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/C18-3003/ |
| https://www.aclweb.org/anthology/C18-3003 | |
| PWC | https://paperswithcode.com/paper/frame-semantics-across-languages-towards-a |
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Topological map construction and scene recognition for vehicle localization
| Title | Topological map construction and scene recognition for vehicle localization |
| Authors | Huei-Yung Lin |
| Abstract | This paper presents a vehicle localization method to assist vehicle navigation based on topological map construction and scene recognition. A topological map is constructed using omni-directional image sequences, and the node information of the topological map is used for place recognition and derivation of vehicle location. In topological map construction and scene change detection, we utilize the Extended-HCT method for semantic description and feature extraction. Content-based and feature-based image retrieval approaches are adopted for place recognition and vehicle localization on the real scene image dataset. The proposed technique is able to construct a real-time image retrieval system for navigation assistance and validate the correctness of the route. Experiments are carried out in both the indoor and outdoor environments using real world images. |
| Tasks | Image Retrieval, Scene Recognition |
| Published | 2018-01-01 |
| URL | https://link.springer.com/article/10.1007%2Fs10514-017-9638-9#Sec2 |
| https://link.springer.com/article/10.1007%2Fs10514-017-9638-9#Sec2 | |
| PWC | https://paperswithcode.com/paper/topological-map-construction-and-scene |
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A Multi- versus a Single-classifier Approach for the Identification of Modality in the Portuguese Language
| Title | A Multi- versus a Single-classifier Approach for the Identification of Modality in the Portuguese Language |
| Authors | Jo{~a}o Sequeira, Teresa Gon{\c{c}}alves, Paulo Quaresma, Am{'a}lia Mendes, Iris Hendrickx |
| Abstract | |
| Tasks | Opinion Mining, Sentiment Analysis, Text Classification, Word Sense Disambiguation |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1161/ |
| https://www.aclweb.org/anthology/L18-1161 | |
| PWC | https://paperswithcode.com/paper/a-multi-versus-a-single-classifier-approach |
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What do RNN Language Models Learn about Filler–Gap Dependencies?
| Title | What do RNN Language Models Learn about Filler–Gap Dependencies? |
| Authors | Ethan Wilcox, Roger Levy, Takashi Morita, Richard Futrell |
| Abstract | RNN language models have achieved state-of-the-art perplexity results and have proven useful in a suite of NLP tasks, but it is as yet unclear what syntactic generalizations they learn. Here we investigate whether state-of-the-art RNN language models represent long-distance \textbf{filler{–}gap dependencies} and constraints on them. Examining RNN behavior on experimentally controlled sentences designed to expose filler{–}gap dependencies, we show that RNNs can represent the relationship in multiple syntactic positions and over large spans of text. Furthermore, we show that RNNs learn a subset of the known restrictions on filler{–}gap dependencies, known as \textbf{island constraints}: RNNs show evidence for wh-islands, adjunct islands, and complex NP islands. These studies demonstrates that state-of-the-art RNN models are able to learn and generalize about empty syntactic positions. |
| Tasks | Language Modelling, Machine Translation |
| Published | 2018-11-01 |
| URL | https://www.aclweb.org/anthology/W18-5423/ |
| https://www.aclweb.org/anthology/W18-5423 | |
| PWC | https://paperswithcode.com/paper/what-do-rnn-language-models-learn-about |
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Temporally Grounding Natural Sentence in Video
| Title | Temporally Grounding Natural Sentence in Video |
| Authors | Jingyuan Chen, Xinpeng Chen, Lin Ma, Zequn Jie, Tat-Seng Chua |
| Abstract | We introduce an effective and efficient method that grounds (i.e., localizes) natural sentences in long, untrimmed video sequences. Specifically, a novel Temporal GroundNet (TGN) is proposed to temporally capture the evolving fine-grained frame-by-word interactions between video and sentence. TGN sequentially scores a set of temporal candidates ended at each frame based on the exploited frame-by-word interactions, and finally grounds the segment corresponding to the sentence. Unlike traditional methods treating the overlapping segments separately in a sliding window fashion, TGN aggregates the historical information and generates the final grounding result in one single pass. We extensively evaluate our proposed TGN on three public datasets with significant improvements over the state-of-the-arts. We further show the consistent effectiveness and efficiency of TGN through an ablation study and a runtime test. |
| Tasks | Video Captioning |
| Published | 2018-10-01 |
| URL | https://www.aclweb.org/anthology/D18-1015/ |
| https://www.aclweb.org/anthology/D18-1015 | |
| PWC | https://paperswithcode.com/paper/temporally-grounding-natural-sentence-in |
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Construction of the Corpus of Everyday Japanese Conversation: An Interim Report
| Title | Construction of the Corpus of Everyday Japanese Conversation: An Interim Report |
| Authors | Hanae Koiso, Yasuharu Den, Yuriko Iseki, Wakako Kashino, Yoshiko Kawabata, Ken{'}ya Nishikawa, Yayoi Tanaka, Yasuyuki Usuda |
| Abstract | |
| Tasks | |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1672/ |
| https://www.aclweb.org/anthology/L18-1672 | |
| PWC | https://paperswithcode.com/paper/construction-of-the-corpus-of-everyday |
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Toward Bayesian Synchronous Tree Substitution Grammars for Sentence Planning
| Title | Toward Bayesian Synchronous Tree Substitution Grammars for Sentence Planning |
| Authors | David M. Howcroft, Dietrich Klakow, Vera Demberg |
| Abstract | Developing conventional natural language generation systems requires extensive attention from human experts in order to craft complex sets of sentence planning rules. We propose a Bayesian nonparametric approach to learn sentence planning rules by inducing synchronous tree substitution grammars for pairs of text plans and morphosyntactically-specified dependency trees. Our system is able to learn rules which can be used to generate novel texts after training on small datasets. |
| Tasks | Text Generation |
| Published | 2018-11-01 |
| URL | https://www.aclweb.org/anthology/W18-6546/ |
| https://www.aclweb.org/anthology/W18-6546 | |
| PWC | https://paperswithcode.com/paper/toward-bayesian-synchronous-tree-substitution |
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Generating E-Commerce Product Titles and Predicting their Quality
| Title | Generating E-Commerce Product Titles and Predicting their Quality |
| Authors | Jos{'e} G. Camargo de Souza, Michael Kozielski, Prashant Mathur, Ernie Chang, Marco Guerini, Matteo Negri, Marco Turchi, Evgeny Matusov |
| Abstract | E-commerce platforms present products using titles that summarize product information. These titles cannot be created by hand, therefore an algorithmic solution is required. The task of automatically generating these titles given noisy user provided titles is one way to achieve the goal. The setting requires the generation process to be fast and the generated title to be both human-readable and concise. Furthermore, we need to understand if such generated titles are usable. As such, we propose approaches that (i) automatically generate product titles, (ii) predict their quality. Our approach scales to millions of products and both automatic and human evaluations performed on real-world data indicate our approaches are effective and applicable to existing e-commerce scenarios. |
| Tasks | Text Generation |
| Published | 2018-11-01 |
| URL | https://www.aclweb.org/anthology/W18-6530/ |
| https://www.aclweb.org/anthology/W18-6530 | |
| PWC | https://paperswithcode.com/paper/generating-e-commerce-product-titles-and |
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