Paper Group NANR 149
Proceedings of the 1st Workshop on Explainable Computational Intelligence (XCI 2017). A Simple Method for Clarifying Sentences with Coordination Ambiguities. Partitioned Tensor Factorizations for Learning Mixed Membership Models. Learning Infinite Layer Networks without the Kernel Trick. Detecting Personal Medication Intake in Twitter: An Annotated …
Proceedings of the 1st Workshop on Explainable Computational Intelligence (XCI 2017)
| Title | Proceedings of the 1st Workshop on Explainable Computational Intelligence (XCI 2017) |
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| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/W17-3700/ |
| https://www.aclweb.org/anthology/W17-3700 | |
| PWC | https://paperswithcode.com/paper/proceedings-of-the-1st-workshop-on-1 |
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A Simple Method for Clarifying Sentences with Coordination Ambiguities
| Title | A Simple Method for Clarifying Sentences with Coordination Ambiguities |
| Authors | Michael White, Manjuan Duan, David L. King |
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| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/W17-3702/ |
| https://www.aclweb.org/anthology/W17-3702 | |
| PWC | https://paperswithcode.com/paper/a-simple-method-for-clarifying-sentences-with |
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Partitioned Tensor Factorizations for Learning Mixed Membership Models
| Title | Partitioned Tensor Factorizations for Learning Mixed Membership Models |
| Authors | Zilong Tan, Sayan Mukherjee |
| Abstract | We present an efficient algorithm for learning mixed membership models when the number of variables p is much larger than the number of hidden components k. This algorithm reduces the computational complexity of state-of-the-art tensor methods, which require decomposing an $O(p^3)$ tensor, to factorizing $O(p/k)$ sub-tensors each of size $O(k^3)$. In addition, we address the issue of negative entries in the empirical method of moments based estimators. We provide sufficient conditions under which our approach has provable guarantees. Our approach obtains competitive empirical results on both simulated and real data. |
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| Published | 2017-08-01 |
| URL | https://icml.cc/Conferences/2017/Schedule?showEvent=551 |
| http://proceedings.mlr.press/v70/tan17a/tan17a.pdf | |
| PWC | https://paperswithcode.com/paper/partitioned-tensor-factorizations-for |
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Learning Infinite Layer Networks without the Kernel Trick
| Title | Learning Infinite Layer Networks without the Kernel Trick |
| Authors | Roi Livni, Daniel Carmon, Amir Globerson |
| Abstract | Infinite Layer Networks (ILN) have been proposed as an architecture that mimics neural networks while enjoying some of the advantages of kernel methods. ILN are networks that integrate over infinitely many nodes within a single hidden layer. It has been demonstrated by several authors that the problem of learning ILN can be reduced to the kernel trick, implying that whenever a certain integral can be computed analytically they are efficiently learnable. In this work we give an online algorithm for ILN, which avoids the kernel trick assumption. More generally and of independent interest, we show that kernel methods in general can be exploited even when the kernel cannot be efficiently computed but can only be estimated via sampling. We provide a regret analysis for our algorithm, showing that it matches the sample complexity of methods which have access to kernel values. Thus, our method is the first to demonstrate that the kernel trick is not necessary, as such, and random features suffice to obtain comparable performance. |
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| Published | 2017-08-01 |
| URL | https://icml.cc/Conferences/2017/Schedule?showEvent=755 |
| http://proceedings.mlr.press/v70/livni17a/livni17a.pdf | |
| PWC | https://paperswithcode.com/paper/learning-infinite-layer-networks-without-the |
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Detecting Personal Medication Intake in Twitter: An Annotated Corpus and Baseline Classification System
| Title | Detecting Personal Medication Intake in Twitter: An Annotated Corpus and Baseline Classification System |
| Authors | Ari Klein, Abeed Sarker, Masoud Rouhizadeh, Karen O{'}Connor, Graciela Gonzalez |
| Abstract | Social media sites (e.g., Twitter) have been used for surveillance of drug safety at the population level, but studies that focus on the effects of medications on specific sets of individuals have had to rely on other sources of data. Mining social media data for this in-formation would require the ability to distinguish indications of personal medication in-take in this media. Towards that end, this paper presents an annotated corpus that can be used to train machine learning systems to determine whether a tweet that mentions a medication indicates that the individual posting has taken that medication at a specific time. To demonstrate the utility of the corpus as a training set, we present baseline results of supervised classification. |
| Tasks | Epidemiology |
| Published | 2017-08-01 |
| URL | https://www.aclweb.org/anthology/W17-2316/ |
| https://www.aclweb.org/anthology/W17-2316 | |
| PWC | https://paperswithcode.com/paper/detecting-personal-medication-intake-in |
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Proceedings of the Second Conference on Machine Translation
| Title | Proceedings of the Second Conference on Machine Translation |
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| Tasks | Machine Translation |
| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/W17-4700/ |
| https://www.aclweb.org/anthology/W17-4700 | |
| PWC | https://paperswithcode.com/paper/proceedings-of-the-second-conference-on |
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Evaluation Metrics for Machine Reading Comprehension: Prerequisite Skills and Readability
| Title | Evaluation Metrics for Machine Reading Comprehension: Prerequisite Skills and Readability |
| Authors | Saku Sugawara, Yusuke Kido, Hikaru Yokono, Akiko Aizawa |
| Abstract | Knowing the quality of reading comprehension (RC) datasets is important for the development of natural-language understanding systems. In this study, two classes of metrics were adopted for evaluating RC datasets: prerequisite skills and readability. We applied these classes to six existing datasets, including MCTest and SQuAD, and highlighted the characteristics of the datasets according to each metric and the correlation between the two classes. Our dataset analysis suggests that the readability of RC datasets does not directly affect the question difficulty and that it is possible to create an RC dataset that is easy to read but difficult to answer. |
| Tasks | Coreference Resolution, Machine Reading Comprehension, Reading Comprehension |
| Published | 2017-07-01 |
| URL | https://www.aclweb.org/anthology/P17-1075/ |
| https://www.aclweb.org/anthology/P17-1075 | |
| PWC | https://paperswithcode.com/paper/evaluation-metrics-for-machine-reading |
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Generating Text with Correct Verb Conjugation: Proposal for a New Automatic Conjugator with NooJ
| Title | Generating Text with Correct Verb Conjugation: Proposal for a New Automatic Conjugator with NooJ |
| Authors | H{'e}la Fehri, Sondes Dardour |
| Abstract | |
| Tasks | Text Generation |
| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/W17-3806/ |
| https://www.aclweb.org/anthology/W17-3806 | |
| PWC | https://paperswithcode.com/paper/generating-text-with-correct-verb-conjugation |
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Dictionary Learning Based on Sparse Distribution Tomography
| Title | Dictionary Learning Based on Sparse Distribution Tomography |
| Authors | Pedram Pad, Farnood Salehi, Elisa Celis, Patrick Thiran, Michael Unser |
| Abstract | We propose a new statistical dictionary learning algorithm for sparse signals that is based on an $\alpha$-stable innovation model. The parameters of the underlying model—that is, the atoms of the dictionary, the sparsity index $\alpha$ and the dispersion of the transform-domain coefficients—are recovered using a new type of probability distribution tomography. Specifically, we drive our estimator with a series of random projections of the data, which results in an efficient algorithm. Moreover, since the projections are achieved using linear combinations, we can invoke the generalized central limit theorem to justify the use of our method for sparse signals that are not necessarily $\alpha$-stable. We evaluate our algorithm by performing two types of experiments: image in-painting and image denoising. In both cases, we find that our approach is competitive with state-of-the-art dictionary learning techniques. Beyond the algorithm itself, two aspects of this study are interesting in their own right. The first is our statistical formulation of the problem, which unifies the topics of dictionary learning and independent component analysis. The second is a generalization of a classical theorem about isometries of $\ell_p$-norms that constitutes the foundation of our approach. |
| Tasks | Denoising, Dictionary Learning, Image Denoising |
| Published | 2017-08-01 |
| URL | https://icml.cc/Conferences/2017/Schedule?showEvent=662 |
| http://proceedings.mlr.press/v70/pad17a/pad17a.pdf | |
| PWC | https://paperswithcode.com/paper/dictionary-learning-based-on-sparse |
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An Essay on Self-explanatory Computational Intelligence: A Linguistic Model of Data Processing Systems
| Title | An Essay on Self-explanatory Computational Intelligence: A Linguistic Model of Data Processing Systems |
| Authors | Jose M. Alonso, Gracian Trivino |
| Abstract | |
| Tasks | Text Generation |
| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/W17-3704/ |
| https://www.aclweb.org/anthology/W17-3704 | |
| PWC | https://paperswithcode.com/paper/an-essay-on-self-explanatory-computational |
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Evaluating an Automata Approach to Query Containment
| Title | Evaluating an Automata Approach to Query Containment |
| Authors | Michael Minock |
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| Tasks | Automated Theorem Proving, Text Generation |
| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/W17-4010/ |
| https://www.aclweb.org/anthology/W17-4010 | |
| PWC | https://paperswithcode.com/paper/evaluating-an-automata-approach-to-query |
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Poet’s Little Helper: A methodology for computer-based poetry generation. A case study for the Basque language
| Title | Poet’s Little Helper: A methodology for computer-based poetry generation. A case study for the Basque language |
| Authors | Aitzol Astigarraga, Jos{'e} Mar{'\i}a Mart{'\i}nez-Otzeta, Igor Rodriguez, Basilio Sierra, Elena Lazkano |
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| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/W17-3901/ |
| https://www.aclweb.org/anthology/W17-3901 | |
| PWC | https://paperswithcode.com/paper/poets-little-helper-a-methodology-for |
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Stochastic DCA for the Large-sum of Non-convex Functions Problem and its Application to Group Variable Selection in Classification
| Title | Stochastic DCA for the Large-sum of Non-convex Functions Problem and its Application to Group Variable Selection in Classification |
| Authors | Hoai An Le Thi, Hoai Minh Le, Duy Nhat Phan, Bach Tran |
| Abstract | In this paper, we present a stochastic version of DCA (Difference of Convex functions Algorithm) to solve a class of optimization problems whose objective function is a large sum of non-convex functions and a regularization term. We consider the $\ell_{2,0}$ regularization to deal with the group variables selection. By exploiting the special structure of the problem, we propose an efficient DC decomposition for which the corresponding stochastic DCA scheme is very inexpensive: it only requires the projection of points onto balls that is explicitly computed. As an application, we applied our algorithm for the group variables selection in multiclass logistic regression. Numerical experiments on several benchmark datasets and synthetic datasets illustrate the efficiency of our algorithm and its superiority over well-known methods, with respect to classification accuracy, sparsity of solution as well as running time. |
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| Published | 2017-08-01 |
| URL | https://icml.cc/Conferences/2017/Schedule?showEvent=886 |
| http://proceedings.mlr.press/v70/thi17a/thi17a.pdf | |
| PWC | https://paperswithcode.com/paper/stochastic-dca-for-the-large-sum-of-non |
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Building Lexical Vector Representations from Concept Definitions
| Title | Building Lexical Vector Representations from Concept Definitions |
| Authors | Danilo Silva de Carvalho, Minh Le Nguyen |
| Abstract | The use of distributional language representations have opened new paths in solving a variety of NLP problems. However, alternative approaches can take advantage of information unavailable through pure statistical means. This paper presents a method for building vector representations from meaning unit blocks called concept definitions, which are obtained by extracting information from a curated linguistic resource (Wiktionary). The representations obtained in this way can be compared through conventional cosine similarity and are also interpretable by humans. Evaluation was conducted in semantic similarity and relatedness test sets, with results indicating a performance comparable to other methods based on single linguistic resource extraction. The results also indicate noticeable performance gains when combining distributional similarity scores with the ones obtained using this approach. Additionally, a discussion on the proposed method{'}s shortcomings is provided in the analysis of error cases. |
| Tasks | Dependency Parsing, Machine Translation, Named Entity Recognition, Semantic Similarity, Semantic Textual Similarity |
| Published | 2017-04-01 |
| URL | https://www.aclweb.org/anthology/E17-1085/ |
| https://www.aclweb.org/anthology/E17-1085 | |
| PWC | https://paperswithcode.com/paper/building-lexical-vector-representations-from |
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UINSUSKA-TiTech at SemEval-2017 Task 3: Exploiting Word Importance Levels for Similarity Features for CQA
| Title | UINSUSKA-TiTech at SemEval-2017 Task 3: Exploiting Word Importance Levels for Similarity Features for CQA |
| Authors | Surya Agustian, Hiroya Takamura |
| Abstract | The majority of core techniques to solve many problems in Community Question Answering (CQA) task rely on similarity computation. This work focuses on similarity between two sentences (or questions in subtask B) based on word embeddings. We exploit words importance levels in sentences or questions for similarity features, for classification and ranking with machine learning. Using only 2 types of similarity metric, our proposed method has shown comparable results with other complex systems. This method on subtask B 2017 dataset is ranked on position 7 out of 13 participants. Evaluation on 2016 dataset is on position 8 of 12, outperforms some complex systems. Further, this finding is explorable and potential to be used as baseline and extensible for many tasks in CQA and other textual similarity based system. |
| Tasks | Community Question Answering, Knowledge Graphs, Question Answering, Semantic Textual Similarity, Word Embeddings |
| Published | 2017-08-01 |
| URL | https://www.aclweb.org/anthology/S17-2061/ |
| https://www.aclweb.org/anthology/S17-2061 | |
| PWC | https://paperswithcode.com/paper/uinsuska-titech-at-semeval-2017-task-3 |
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