Paper Group NANR 116
SHIHbot: A Facebook chatbot for Sexual Health Information on HIV/AIDS. Recognizing Mentions of Adverse Drug Reaction in Social Media Using Knowledge-Infused Recurrent Models. Open Set Text Classification Using CNNs. Annotating and parsing to semantic frames: feedback from the French FrameNet project. Scalable Bio-Molecular Event Extraction System t …
SHIHbot: A Facebook chatbot for Sexual Health Information on HIV/AIDS
| Title | SHIHbot: A Facebook chatbot for Sexual Health Information on HIV/AIDS |
| Authors | Jacqueline Brixey, Rens Hoegen, Wei Lan, Joshua Rusow, Karan Singla, Xusen Yin, Ron Artstein, Anton Leuski |
| Abstract | We present the implementation of an autonomous chatbot, SHIHbot, deployed on Facebook, which answers a wide variety of sexual health questions on HIV/AIDS. The chatbot{'}s response database is com-piled from professional medical and public health resources in order to provide reliable information to users. The system{'}s backend is NPCEditor, a response selection platform trained on linked questions and answers; to our knowledge this is the first retrieval-based chatbot deployed on a large public social network. |
| Tasks | Chatbot |
| Published | 2017-08-01 |
| URL | https://www.aclweb.org/anthology/W17-5544/ |
| https://www.aclweb.org/anthology/W17-5544 | |
| PWC | https://paperswithcode.com/paper/shihbot-a-facebook-chatbot-for-sexual-health |
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Recognizing Mentions of Adverse Drug Reaction in Social Media Using Knowledge-Infused Recurrent Models
| Title | Recognizing Mentions of Adverse Drug Reaction in Social Media Using Knowledge-Infused Recurrent Models |
| Authors | Gabriel Stanovsky, Daniel Gruhl, Pablo Mendes |
| Abstract | Recognizing mentions of Adverse Drug Reactions (ADR) in social media is challenging: ADR mentions are context-dependent and include long, varied and unconventional descriptions as compared to more formal medical symptom terminology. We use the CADEC corpus to train a recurrent neural network (RNN) transducer, integrated with knowledge graph embeddings of DBpedia, and show the resulting model to be highly accurate (93.4 F1). Furthermore, even when lacking high quality expert annotations, we show that by employing an active learning technique and using purpose built annotation tools, we can train the RNN to perform well (83.9 F1). |
| Tasks | Active Learning, Knowledge Graph Embeddings |
| Published | 2017-04-01 |
| URL | https://www.aclweb.org/anthology/E17-1014/ |
| https://www.aclweb.org/anthology/E17-1014 | |
| PWC | https://paperswithcode.com/paper/recognizing-mentions-of-adverse-drug-reaction |
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Open Set Text Classification Using CNNs
| Title | Open Set Text Classification Using CNNs |
| Authors | Sridhama Prakhya, Vinodini Venkataram, Jugal Kalita |
| Abstract | |
| Tasks | Text Classification |
| Published | 2017-12-01 |
| URL | https://www.aclweb.org/anthology/W17-7557/ |
| https://www.aclweb.org/anthology/W17-7557 | |
| PWC | https://paperswithcode.com/paper/open-set-text-classification-using-cnns |
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Annotating and parsing to semantic frames: feedback from the French FrameNet project
| Title | Annotating and parsing to semantic frames: feedback from the French FrameNet project |
| Authors | C, Marie ito |
| Abstract | |
| Tasks | Semantic Parsing |
| Published | 2017-01-01 |
| URL | https://www.aclweb.org/anthology/W17-7601/ |
| https://www.aclweb.org/anthology/W17-7601 | |
| PWC | https://paperswithcode.com/paper/annotating-and-parsing-to-semantic-frames |
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Scalable Bio-Molecular Event Extraction System towards Knowledge Acquisition
| Title | Scalable Bio-Molecular Event Extraction System towards Knowledge Acquisition |
| Authors | Pattabhi RK Rao, Sindhuja Gopalan, Sobha Lalitha Devi |
| Abstract | |
| Tasks | |
| Published | 2017-12-01 |
| URL | https://www.aclweb.org/anthology/W17-7547/ |
| https://www.aclweb.org/anthology/W17-7547 | |
| PWC | https://paperswithcode.com/paper/scalable-bio-molecular-event-extraction |
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Using Linked Disambiguated Distributional Networks for Word Sense Disambiguation
| Title | Using Linked Disambiguated Distributional Networks for Word Sense Disambiguation |
| Authors | Alex Panchenko, er, Stefano Faralli, Simone Paolo Ponzetto, Chris Biemann |
| Abstract | We introduce a new method for unsupervised knowledge-based word sense disambiguation (WSD) based on a resource that links two types of sense-aware lexical networks: one is induced from a corpus using distributional semantics, the other is manually constructed. The combination of two networks reduces the sparsity of sense representations used for WSD. We evaluate these enriched representations within two lexical sample sense disambiguation benchmarks. Our results indicate that (1) features extracted from the corpus-based resource help to significantly outperform a model based solely on the lexical resource; (2) our method achieves results comparable or better to four state-of-the-art unsupervised knowledge-based WSD systems including three hybrid systems that also rely on text corpora. In contrast to these hybrid methods, our approach does not require access to web search engines, texts mapped to a sense inventory, or machine translation systems. |
| Tasks | Machine Translation, Word Embeddings, Word Sense Disambiguation |
| Published | 2017-04-01 |
| URL | https://www.aclweb.org/anthology/W17-1909/ |
| https://www.aclweb.org/anthology/W17-1909 | |
| PWC | https://paperswithcode.com/paper/using-linked-disambiguated-distributional |
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Simple strategies for recovering inner products from coarsely quantized random projections
| Title | Simple strategies for recovering inner products from coarsely quantized random projections |
| Authors | Ping Li, Martin Slawski |
| Abstract | Random projections have been increasingly adopted for a diverse set of tasks in machine learning involving dimensionality reduction. One specific line of research on this topic has investigated the use of quantization subsequent to projection with the aim of additional data compression. Motivated by applications in nearest neighbor search and linear learning, we revisit the problem of recovering inner products (respectively cosine similarities) in such setting. We show that even under coarse scalar quantization with 3 to 5 bits per projection, the loss in accuracy tends to range from negligible'' to moderate’'. One implication is that in most scenarios of practical interest, there is no need for a sophisticated recovery approach like maximum likelihood estimation as considered in previous work on the subject. What we propose herein also yields considerable improvements in terms of accuracy over the Hamming distance-based approach in Li et al. (ICML 2014) which is comparable in terms of simplicity |
| Tasks | Dimensionality Reduction, Quantization |
| Published | 2017-12-01 |
| URL | http://papers.nips.cc/paper/7043-simple-strategies-for-recovering-inner-products-from-coarsely-quantized-random-projections |
| http://papers.nips.cc/paper/7043-simple-strategies-for-recovering-inner-products-from-coarsely-quantized-random-projections.pdf | |
| PWC | https://paperswithcode.com/paper/simple-strategies-for-recovering-inner |
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Sequence to Better Sequence: Continuous Revision of Combinatorial Structures
| Title | Sequence to Better Sequence: Continuous Revision of Combinatorial Structures |
| Authors | Jonas Mueller, David Gifford, Tommi Jaakkola |
| Abstract | We present a model that, after learning on observations of (sequence, outcome) pairs, can be efficiently used to revise a new sequence in order to improve its associated outcome. Our framework requires neither example improvements, nor additional evaluation of outcomes for proposed revisions. To avoid combinatorial-search over sequence elements, we specify a generative model with continuous latent factors, which is learned via joint approximate inference using a recurrent variational autoencoder (VAE) and an outcome-predicting neural network module. Under this model, gradient methods can be used to efficiently optimize the continuous latent factors with respect to inferred outcomes. By appropriately constraining this optimization and using the VAE decoder to generate a revised sequence, we ensure the revision is fundamentally similar to the original sequence, is associated with better outcomes, and looks natural. These desiderata are proven to hold with high probability under our approach, which is empirically demonstrated for revising natural language sentences. |
| Tasks | |
| Published | 2017-08-01 |
| URL | https://icml.cc/Conferences/2017/Schedule?showEvent=623 |
| http://proceedings.mlr.press/v70/mueller17a/mueller17a.pdf | |
| PWC | https://paperswithcode.com/paper/sequence-to-better-sequence-continuous |
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SwissAlps at SemEval-2017 Task 3: Attention-based Convolutional Neural Network for Community Question Answering
| Title | SwissAlps at SemEval-2017 Task 3: Attention-based Convolutional Neural Network for Community Question Answering |
| Authors | Jan Milan Deriu, Mark Cieliebak |
| Abstract | In this paper we propose a system for reranking answers for a given question. Our method builds on a siamese CNN architecture which is extended by two attention mechanisms. The approach was evaluated on the datasets of the SemEval-2017 competition for Community Question Answering (cQA), where it achieved 7th place obtaining a MAP score of 86:24 points on the Question-Comment Similarity subtask. |
| Tasks | Community Question Answering, Question Answering, Question Similarity |
| Published | 2017-08-01 |
| URL | https://www.aclweb.org/anthology/S17-2054/ |
| https://www.aclweb.org/anthology/S17-2054 | |
| PWC | https://paperswithcode.com/paper/swissalps-at-semeval-2017-task-3-attention |
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Proceedings of the 4th Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA 2017)
| Title | Proceedings of the 4th Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA 2017) |
| Authors | |
| Abstract | |
| Tasks | |
| Published | 2017-12-01 |
| URL | https://www.aclweb.org/anthology/W17-5900/ |
| https://www.aclweb.org/anthology/W17-5900 | |
| PWC | https://paperswithcode.com/paper/proceedings-of-the-4th-workshop-on-natural |
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The Use of Object Labels and Spatial Prepositions as Keywords in a Web-Retrieval-Based Image Caption Generation System
| Title | The Use of Object Labels and Spatial Prepositions as Keywords in a Web-Retrieval-Based Image Caption Generation System |
| Authors | Br Birmingham, on, Adrian Muscat |
| Abstract | In this paper, a retrieval-based caption generation system that searches the web for suitable image descriptions is studied. Google{'}s reverse image search is used to find potentially relevant web multimedia content for query images. Sentences are extracted from web pages and the likelihood of the descriptions is computed to select one sentence from the retrieved text documents. The search mechanism is modified to replace the caption generated by Google with a caption composed of labels and spatial prepositions as part of the query{'}s text alongside the image. The object labels are obtained using an off-the-shelf R-CNN and a machine learning model is developed to predict the prepositions. The effect on the caption generation system performance when using the generated text is investigated. Both human evaluations and automatic metrics are used to evaluate the retrieved descriptions. Results show that the web-retrieval-based approach performed better when describing single-object images with sentences extracted from stock photography websites. On the other hand, images with two image objects were better described with template-generated sentences composed of object labels and prepositions. |
| Tasks | Image Retrieval, Text Generation |
| Published | 2017-04-01 |
| URL | https://www.aclweb.org/anthology/W17-2002/ |
| https://www.aclweb.org/anthology/W17-2002 | |
| PWC | https://paperswithcode.com/paper/the-use-of-object-labels-and-spatial |
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Reasoning with Heterogeneous Knowledge for Commonsense Machine Comprehension
| Title | Reasoning with Heterogeneous Knowledge for Commonsense Machine Comprehension |
| Authors | Hongyu Lin, Le Sun, Xianpei Han |
| Abstract | Reasoning with commonsense knowledge is critical for natural language understanding. Traditional methods for commonsense machine comprehension mostly only focus on one specific kind of knowledge, neglecting the fact that commonsense reasoning requires simultaneously considering different kinds of commonsense knowledge. In this paper, we propose a multi-knowledge reasoning method, which can exploit heterogeneous knowledge for commonsense machine comprehension. Specifically, we first mine different kinds of knowledge (including event narrative knowledge, entity semantic knowledge and sentiment coherent knowledge) and encode them as inference rules with costs. Then we propose a multi-knowledge reasoning model, which selects inference rules for a specific reasoning context using attention mechanism, and reasons by summarizing all valid inference rules. Experiments on RocStories show that our method outperforms traditional models significantly. |
| Tasks | Reading Comprehension |
| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/D17-1216/ |
| https://www.aclweb.org/anthology/D17-1216 | |
| PWC | https://paperswithcode.com/paper/reasoning-with-heterogeneous-knowledge-for |
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Learning to Recognize Animals by Watching Documentaries: Using Subtitles as Weak Supervision
| Title | Learning to Recognize Animals by Watching Documentaries: Using Subtitles as Weak Supervision |
| Authors | Aparna Nurani Venkitasubramanian, Tinne Tuytelaars, Marie-Francine Moens |
| Abstract | We investigate animal recognition models learned from wildlife video documentaries by using the weak supervision of the textual subtitles. This is a particularly challenging setting, since i) the animals occur in their natural habitat and are often largely occluded and ii) subtitles are to a large degree complementary to the visual content, providing a very weak supervisory signal. This is in contrast to most work on integrated vision and language in the literature, where textual descriptions are tightly linked to the image content, and often generated in a curated fashion for the task at hand. In particular, we investigate different image representations and models, including a support vector machine on top of activations of a pretrained convolutional neural network, as well as a Naive Bayes framework on a {`}bag-of-activations{'} image representation, where each element of the bag is considered separately. This representation allows key components in the image to be isolated, in spite of largely varying backgrounds and image clutter, without an object detection or image segmentation step. The methods are evaluated based on how well they transfer to unseen camera-trap images captured across diverse topographical regions under different environmental conditions and illumination settings, involving a large domain shift. | |
| Tasks | Object Detection, Semantic Segmentation |
| Published | 2017-04-01 |
| URL | https://www.aclweb.org/anthology/W17-2003/ |
| https://www.aclweb.org/anthology/W17-2003 | |
| PWC | https://paperswithcode.com/paper/learning-to-recognize-animals-by-watching |
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Multi-Modal Fashion Product Retrieval
| Title | Multi-Modal Fashion Product Retrieval |
| Authors | Antonio Rubio Romano, LongLong Yu, Edgar Simo-Serra, Francesc Moreno-Noguer |
| Abstract | Finding a product in the fashion world can be a daunting task. Everyday, e-commerce sites are updating with thousands of images and their associated metadata (textual information), deepening the problem. In this paper, we leverage both the images and textual metadata and propose a joint multi-modal embedding that maps both the text and images into a common latent space. Distances in the latent space correspond to similarity between products, allowing us to effectively perform retrieval in this latent space. We compare against existing approaches and show significant improvements in retrieval tasks on a large-scale e-commerce dataset. |
| Tasks | |
| Published | 2017-04-01 |
| URL | https://www.aclweb.org/anthology/W17-2007/ |
| https://www.aclweb.org/anthology/W17-2007 | |
| PWC | https://paperswithcode.com/paper/multi-modal-fashion-product-retrieval |
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Making Travel Smarter: Extracting Travel Information From Email Itineraries Using Named Entity Recognition
| Title | Making Travel Smarter: Extracting Travel Information From Email Itineraries Using Named Entity Recognition |
| Authors | Divyansh Kaushik, Shashank Gupta, Chakradhar Raju, Reuben Dias, Sanjib Ghosh |
| Abstract | The purpose of this research is to address the problem of extracting information from travel itineraries and discuss the challenges faced in the process. Business-to-customer emails like booking confirmations and e-tickets are usually machine generated by filling slots in pre-defined templates which improve the presentation of such emails but also make the emails more complex in structure. Extracting the relevant information from these emails would let users track their journeys and important updates on applications installed on their devices to give them a consolidated over view of their itineraries and also save valuable time. We investigate the use of an HMM-based named entity recognizer on such emails which we will use to label and extract relevant entities. NER in such emails is challenging as these itineraries offer less useful contextual information. We also propose a rich set of features which are integrated into the model and are specific to our domain. The result from our model is a list of lists containing the relevant information extracted from ones itinerary. |
| Tasks | Named Entity Recognition |
| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/R17-1047/ |
| https://doi.org/10.26615/978-954-452-049-6_047 | |
| PWC | https://paperswithcode.com/paper/making-travel-smarter-extracting-travel |
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