Paper Group NANR 59
Evaluation of Finite State Morphological Analyzers Based on Paradigm Extraction from Wiktionary. Distractor Generation for Chinese Fill-in-the-blank Items. Constructing an Alias List for Named Entities during an Event. Gated Recurrent Convolution Neural Network for OCR. A Joint Sequential and Relational Model for Frame-Semantic Parsing. Getting the …
Evaluation of Finite State Morphological Analyzers Based on Paradigm Extraction from Wiktionary
| Title | Evaluation of Finite State Morphological Analyzers Based on Paradigm Extraction from Wiktionary |
| Authors | Ling Liu, Mans Hulden |
| Abstract | |
| Tasks | Language Modelling, Lemmatization, Machine Translation, Morphological Inflection, Part-Of-Speech Tagging |
| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/W17-4009/ |
| https://www.aclweb.org/anthology/W17-4009 | |
| PWC | https://paperswithcode.com/paper/evaluation-of-finite-state-morphological |
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Distractor Generation for Chinese Fill-in-the-blank Items
| Title | Distractor Generation for Chinese Fill-in-the-blank Items |
| Authors | Shu Jiang, John Lee |
| Abstract | This paper reports the first study on automatic generation of distractors for fill-in-the-blank items for learning Chinese vocabulary. We investigate the quality of distractors generated by a number of criteria, including part-of-speech, difficulty level, spelling, word co-occurrence and semantic similarity. Evaluations show that a semantic similarity measure, based on the word2vec model, yields distractors that are significantly more plausible than those generated by baseline methods. |
| Tasks | Semantic Similarity, Semantic Textual Similarity |
| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/W17-5015/ |
| https://www.aclweb.org/anthology/W17-5015 | |
| PWC | https://paperswithcode.com/paper/distractor-generation-for-chinese-fill-in-the |
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Constructing an Alias List for Named Entities during an Event
| Title | Constructing an Alias List for Named Entities during an Event |
| Authors | Anietie Andy, Mark Dredze, Mugizi Rwebangira, Chris Callison-Burch |
| Abstract | In certain fields, real-time knowledge from events can help in making informed decisions. In order to extract pertinent real-time knowledge related to an event, it is important to identify the named entities and their corresponding aliases related to the event. The problem of identifying aliases of named entities that spike has remained unexplored. In this paper, we introduce an algorithm, EntitySpike, that identifies entities that spike in popularity in tweets from a given time period, and constructs an alias list for these spiked entities. EntitySpike uses a temporal heuristic to identify named entities with similar context that occur in the same time period (within minutes) during an event. Each entity is encoded as a vector using this temporal heuristic. We show how these entity-vectors can be used to create a named entity alias list. We evaluated our algorithm on a dataset of temporally ordered tweets from a single event, the 2013 Grammy Awards show. We carried out various experiments on tweets that were published in the same time period and show that our algorithm identifies most entity name aliases and outperforms a competitive baseline. |
| Tasks | Community Question Answering, Question Answering |
| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/W17-4405/ |
| https://www.aclweb.org/anthology/W17-4405 | |
| PWC | https://paperswithcode.com/paper/constructing-an-alias-list-for-named-entities |
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Gated Recurrent Convolution Neural Network for OCR
| Title | Gated Recurrent Convolution Neural Network for OCR |
| Authors | Jianfeng Wang, Xiaolin Hu |
| Abstract | Optical Character Recognition (OCR) aims to recognize text in natural images. Inspired by a recently proposed model for general image classification, Recurrent Convolution Neural Network (RCNN), we propose a new architecture named Gated RCNN (GRCNN) for solving this problem. Its critical component, Gated Recurrent Convolution Layer (GRCL), is constructed by adding a gate to the Recurrent Convolution Layer (RCL), the critical component of RCNN. The gate controls the context modulation in RCL and balances the feed-forward information and the recurrent information. In addition, an efficient Bidirectional Long Short-Term Memory (BLSTM) is built for sequence modeling. The GRCNN is combined with BLSTM to recognize text in natural images. The entire GRCNN-BLSTM model can be trained end-to-end. Experiments show that the proposed model outperforms existing methods on several benchmark datasets including the IIIT-5K, Street View Text (SVT) and ICDAR. |
| Tasks | Image Classification, Optical Character Recognition |
| Published | 2017-12-01 |
| URL | http://papers.nips.cc/paper/6637-gated-recurrent-convolution-neural-network-for-ocr |
| http://papers.nips.cc/paper/6637-gated-recurrent-convolution-neural-network-for-ocr.pdf | |
| PWC | https://paperswithcode.com/paper/gated-recurrent-convolution-neural-network |
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A Joint Sequential and Relational Model for Frame-Semantic Parsing
| Title | A Joint Sequential and Relational Model for Frame-Semantic Parsing |
| Authors | Bishan Yang, Tom Mitchell |
| Abstract | We introduce a new method for frame-semantic parsing that significantly improves the prior state of the art. Our model leverages the advantages of a deep bidirectional LSTM network which predicts semantic role labels word by word and a relational network which predicts semantic roles for individual text expressions in relation to a predicate. The two networks are integrated into a single model via knowledge distillation, and a unified graphical model is employed to jointly decode frames and semantic roles during inference. Experiments on the standard FrameNet data show that our model significantly outperforms existing neural and non-neural approaches, achieving a 5.7 F1 gain over the current state of the art, for full frame structure extraction. |
| Tasks | Machine Translation, Question Answering, Semantic Parsing, Semantic Role Labeling, Structured Prediction, Word Sense Disambiguation |
| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/D17-1128/ |
| https://www.aclweb.org/anthology/D17-1128 | |
| PWC | https://paperswithcode.com/paper/a-joint-sequential-and-relational-model-for |
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Getting the Most out of AMR Parsing
| Title | Getting the Most out of AMR Parsing |
| Authors | Chuan Wang, Nianwen Xue |
| Abstract | This paper proposes to tackle the AMR parsing bottleneck by improving two components of an AMR parser: concept identification and alignment. We first build a Bidirectional LSTM based concept identifier that is able to incorporate richer contextual information to learn sparse AMR concept labels. We then extend an HMM-based word-to-concept alignment model with graph distance distortion and a rescoring method during decoding to incorporate the structural information in the AMR graph. We show integrating the two components into an existing AMR parser results in consistently better performance over the state of the art on various datasets. |
| Tasks | Amr Parsing, Feature Engineering, Reading Comprehension, Text Generation |
| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/D17-1129/ |
| https://www.aclweb.org/anthology/D17-1129 | |
| PWC | https://paperswithcode.com/paper/getting-the-most-out-of-amr-parsing |
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Dual Embeddings and Metrics for Relational Similarity
| Title | Dual Embeddings and Metrics for Relational Similarity |
| Authors | D Li, an, Douglas Summers-Stay |
| Abstract | |
| Tasks | Learning Word Embeddings, Machine Translation, Question Answering, Word Embeddings, Word Sense Disambiguation |
| Published | 2017-01-01 |
| URL | https://www.aclweb.org/anthology/W17-6924/ |
| https://www.aclweb.org/anthology/W17-6924 | |
| PWC | https://paperswithcode.com/paper/dual-embeddings-and-metrics-for-relational |
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The AFRL-MITLL WMT17 Systems: Old, New, Borrowed, BLEU
| Title | The AFRL-MITLL WMT17 Systems: Old, New, Borrowed, BLEU |
| Authors | Jeremy Gwinnup, Timothy Anderson, Grant Erdmann, Katherine Young, Michaeel Kazi, Elizabeth Salesky, Brian Thompson, Jonathan Taylor |
| Abstract | |
| Tasks | Machine Translation, Tokenization |
| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/W17-4728/ |
| https://www.aclweb.org/anthology/W17-4728 | |
| PWC | https://paperswithcode.com/paper/the-afrl-mitll-wmt17-systems-old-new-borrowed |
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Predicting Word Association Strengths
| Title | Predicting Word Association Strengths |
| Authors | Andrew Cattle, Xiaojuan Ma |
| Abstract | This paper looks at the task of predicting word association strengths across three datasets; WordNet Evocation (Boyd-Graber et al., 2006), University of Southern Florida Free Association norms (Nelson et al., 2004), and Edinburgh Associative Thesaurus (Kiss et al., 1973). We achieve results of r=0.357 and p=0.379, r=0.344 and p=0.300, and r=0.292 and p=0.363, respectively. We find Word2Vec (Mikolov et al., 2013) and GloVe (Pennington et al., 2014) cosine similarities, as well as vector offsets, to be the highest performing features. Furthermore, we examine the usefulness of Gaussian embeddings (Vilnis and McCallum, 2014) for predicting word association strength, the first work to do so. |
| Tasks | Word Embeddings |
| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/D17-1132/ |
| https://www.aclweb.org/anthology/D17-1132 | |
| PWC | https://paperswithcode.com/paper/predicting-word-association-strengths |
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Multimodal Learning and Reasoning for Visual Question Answering
| Title | Multimodal Learning and Reasoning for Visual Question Answering |
| Authors | Ilija Ilievski, Jiashi Feng |
| Abstract | Reasoning about entities and their relationships from multimodal data is a key goal of Artificial General Intelligence. The visual question answering (VQA) problem is an excellent way to test such reasoning capabilities of an AI model and its multimodal representation learning. However, the current VQA models are over-simplified deep neural networks, comprised of a long short-term memory (LSTM) unit for question comprehension and a convolutional neural network (CNN) for learning single image representation. We argue that the single visual representation contains a limited and general information about the image contents and thus limits the model reasoning capabilities. In this work we introduce a modular neural network model that learns a multimodal and multifaceted representation of the image and the question. The proposed model learns to use the multimodal representation to reason about the image entities and achieves a new state-of-the-art performance on both VQA benchmark datasets, VQA v1.0 and v2.0, by a wide margin. |
| Tasks | Question Answering, Representation Learning, Visual Question Answering |
| Published | 2017-12-01 |
| URL | http://papers.nips.cc/paper/6658-multimodal-learning-and-reasoning-for-visual-question-answering |
| http://papers.nips.cc/paper/6658-multimodal-learning-and-reasoning-for-visual-question-answering.pdf | |
| PWC | https://paperswithcode.com/paper/multimodal-learning-and-reasoning-for-visual |
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SentiNLP at IJCNLP-2017 Task 4: Customer Feedback Analysis Using a Bi-LSTM-CNN Model
| Title | SentiNLP at IJCNLP-2017 Task 4: Customer Feedback Analysis Using a Bi-LSTM-CNN Model |
| Authors | Shuying Lin, Huosheng Xie, Liang-Chih Yu, K. Robert Lai |
| Abstract | The analysis of customer feedback is useful to provide good customer service. There are a lot of online customer feedback are produced. Manual classification is impractical because the high volume of data. Therefore, the automatic classification of the customer feedback is of importance for the analysis system to identify meanings or intentions that the customer express. The aim of shared Task 4 of IJCNLP 2017 is to classify the customer feedback into six tags categorization. In this paper, we present a system that uses word embeddings to express the feature of the sentence in the corpus and the neural network as the classifier to complete the shared task. And then the ensemble method is used to get final predictive result. The proposed method get ranked first among twelve teams in terms of micro-averaged F1 and second for accura-cy metric. |
| Tasks | Multi-Label Classification, Sarcasm Detection, Sentence Classification, Sentiment Analysis, Word Embeddings |
| Published | 2017-12-01 |
| URL | https://www.aclweb.org/anthology/I17-4025/ |
| https://www.aclweb.org/anthology/I17-4025 | |
| PWC | https://paperswithcode.com/paper/sentinlp-at-ijcnlp-2017-task-4-customer |
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Developing Lexicon and Classifier for Personality Identification in Texts
| Title | Developing Lexicon and Classifier for Personality Identification in Texts |
| Authors | Kumar Gourav Das, Dipankar Das |
| Abstract | |
| Tasks | |
| Published | 2017-12-01 |
| URL | https://www.aclweb.org/anthology/W17-7545/ |
| https://www.aclweb.org/anthology/W17-7545 | |
| PWC | https://paperswithcode.com/paper/developing-lexicon-and-classifier-for |
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| Framework | |
Clustering Stable Instances of Euclidean k-means.
| Title | Clustering Stable Instances of Euclidean k-means. |
| Authors | Aravindan Vijayaraghavan, Abhratanu Dutta, Alex Wang |
| Abstract | The Euclidean k-means problem is arguably the most widely-studied clustering problem in machine learning. While the k-means objective is NP-hard in the worst-case, practitioners have enjoyed remarkable success in applying heuristics like Lloyd’s algorithm for this problem. To address this disconnect, we study the following question: what properties of real-world instances will enable us to design efficient algorithms and prove guarantees for finding the optimal clustering? We consider a natural notion called additive perturbation stability that we believe captures many practical instances of Euclidean k-means clustering. Stable instances have unique optimal k-means solutions that does not change even when each point is perturbed a little (in Euclidean distance). This captures the property that k-means optimal solution should be tolerant to measurement errors and uncertainty in the points. We design efficient algorithms that provably recover the optimal clustering for instances that are additive perturbation stable. When the instance has some additional separation, we can design a simple, efficient algorithm with provable guarantees that is also robust to outliers. We also complement these results by studying the amount of stability in real datasets, and demonstrating that our algorithm performs well on these benchmark datasets. |
| Tasks | |
| Published | 2017-12-01 |
| URL | http://papers.nips.cc/paper/7228-clustering-stable-instances-of-euclidean-k-means |
| http://papers.nips.cc/paper/7228-clustering-stable-instances-of-euclidean-k-means.pdf | |
| PWC | https://paperswithcode.com/paper/clustering-stable-instances-of-euclidean-k |
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Modelling semantic acquisition in second language learning
| Title | Modelling semantic acquisition in second language learning |
| Authors | Ekaterina Kochmar, Ekaterina Shutova |
| Abstract | Using methods of statistical analysis, we investigate how semantic knowledge is acquired in English as a second language and evaluate the pace of development across a number of predicate types and content word combinations, as well as across the levels of language proficiency and native languages. Our exploratory study helps identify the most problematic areas for language learners with different backgrounds and at different stages of learning. |
| Tasks | |
| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/W17-5033/ |
| https://www.aclweb.org/anthology/W17-5033 | |
| PWC | https://paperswithcode.com/paper/modelling-semantic-acquisition-in-second |
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Identifying attack and support argumentative relations using deep learning
| Title | Identifying attack and support argumentative relations using deep learning |
| Authors | Oana Cocarascu, Francesca Toni |
| Abstract | We propose a deep learning architecture to capture argumentative relations of attack and support from one piece of text to another, of the kind that naturally occur in a debate. The architecture uses two (unidirectional or bidirectional) Long Short-Term Memory networks and (trained or non-trained) word embeddings, and allows to considerably improve upon existing techniques that use syntactic features and supervised classifiers for the same form of (relation-based) argument mining. |
| Tasks | Argument Mining, Word Embeddings |
| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/D17-1144/ |
| https://www.aclweb.org/anthology/D17-1144 | |
| PWC | https://paperswithcode.com/paper/identifying-attack-and-support-argumentative |
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