Sparck Jones, 1972) using easy to write configuration with little to no code. Consistently reproducing results is a fundamental What sets DeepZensols apart from other criteria of the scientific method, without frameworks is its capability of reproducing which, a hypothesis may be weakened or results, efficient mini-batch creation for feature even invalidated. This is becoming even more swapping for model comparisons, and necessary, because a growing number of publications an emphasis on vectorization of natural language are inflated by false positives, to the text providing zero coding neural network point that they are labeled with the pejorative (NN) construction. The framework was term "p-hacking", the intentional or unintentional written with NLP researchers, science related act to bias results in favor of publication outcomers, and students in mind.

Recursive Neural Networks (RvNNs), which compose sequences according to their underlying hierarchical syntactic structure, have performed well in several natural language processing tasks compared to similar models without structural biases. However, traditional RvNNs are incapable of inducing the latent structure in a plain text sequence on their own. Several extensions have been proposed to overcome this limitation. Nevertheless, these extensions tend to rely on surrogate gradients or reinforcement learning at the cost of higher bias or variance. In this work, we propose Continuous Recursive Neural Network (CRvNN) as a backpropagation-friendly alternative to address the aforementioned limitations. This is done by incorporating a continuous relaxation to the induced structure. We demonstrate that CRvNN achieves strong performance in challenging synthetic tasks such as logical inference and ListOps. We also show that CRvNN performs comparably or better than prior latent structure models on real-world tasks such as sentiment analysis and natural language inference.

Healthcare question answering assistance aims to provide customer healthcare information, which widely appears in both Web and mobile Internet. The questions usually require the assistance to have proficient healthcare background knowledge as well as the reasoning ability on the knowledge. Recently a challenge involving complex healthcare reasoning, HeadQA dataset, has been proposed, which contains multiple-choice questions authorized for the public healthcare specialization exam. Unlike most other QA tasks that focus on linguistic understanding, HeadQA requires deeper reasoning involving not only knowledge extraction, but also complex reasoning with healthcare knowledge. These questions are the most challenging for current QA systems, and the current performance of the state-of-the-art method is slightly better than a random guess. In order to solve this challenging task, we present a Multi-step reasoning with Knowledge extraction framework (MurKe). The proposed framework first extracts the healthcare knowledge as supporting documents from the large corpus. In order to find the reasoning chain and choose the correct answer, MurKe iterates between selecting the supporting documents, reformulating the query representation using the supporting documents and getting entailment score for each choice using the entailment model. The reformulation module leverages selected documents for missing evidence, which maintains interpretability. Moreover, we are striving to make full use of off-the-shelf pre-trained models. With less trainable weight, the pre-trained model can easily adapt to healthcare tasks with limited training samples. From the experimental results and ablation study, our system is able to outperform several strong baselines on the HeadQA dataset.

With the exponential increase in the number of images that are shared online every day, the development of effective and efficient learning methods for image privacy prediction has become crucial. Prior works have used as features automatically derived object tags from images' content and manually annotated user tags. However, we believe that in addition to objects, the scene context obtained from images’ content can improve the performance of privacy prediction. Hence, we propose to uncover scene-based tags from images' content using convolutional neural networks. Experimental results on a Flickr dataset show that the scene tags and object tags complement each other and yield the best performance when used in combination with user tags.

Extracting emotional support in Online Health Communities provides insightful information about patients’ emotional states. Current computational approaches to identifying emotional messages, i.e., messages that contain emotional support, are typically based on a set of handcrafted features. In this paper, we show that high-level and abstract features derived from a combination of convolutional neural networks (CNN) with Long Short Term Memory (LSTM) networks can be successfully employed for emotional message identification and can obviate the need for handcrafted features.

Given the large amounts of online textual documents available these days, e.g., news articles and scientific papers, effective methods for extracting keyphrases, which provide a high-level topic description of a document, are greatly needed.We propose PositionRank, an unsupervised graph-based approach to keyphrase extraction that incorporates information from all positions of a word's occurrences into a biased PageRank to extract keyphrases. Our model obtains remarkable improvements in performance over strong baselines.

The Workshop Program of the Association for the Advancement of Artificial Intelligence's Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16) was held at the beginning of the conference, February 12-13, 2016. Workshop participants met and discussed issues with a selected focus -- providing an informal setting for active exchange among researchers, developers and users on topics of current interest. To foster interaction and exchange of ideas, the workshops were kept small, with 25-65 participants. Attendance was sometimes limited to active participants only, but most workshops also allowed general registration by other interested individuals.

The Workshop Program of the Association for the Advancement of Artificial Intelligence’s Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16) was held at the beginning of the conference, February 12-13, 2016. Workshop participants met and discussed issues with a selected focus — providing an informal setting for active exchange among researchers, developers and users on topics of current interest. To foster interaction and exchange of ideas, the workshops were kept small, with 25-65 participants. Attendance was sometimes limited to active participants only, but most workshops also allowed general registration by other interested individuals. The AAAI-16 Workshops were an excellent forum for exploring emerging approaches and task areas, for bridging the gaps between AI and other fields or between subfields of AI, for elucidating the results of exploratory research, or for critiquing existing approaches. The fifteen workshops held at AAAI-16 were Artificial Intelligence Applied to Assistive Technologies and Smart Environments (WS-16-01), AI, Ethics, and Society (WS-16-02), Artificial Intelligence for Cyber Security (WS-16-03), Artificial Intelligence for Smart Grids and Smart Buildings (WS-16-04), Beyond NP (WS-16-05), Computer Poker and Imperfect Information Games (WS-16-06), Declarative Learning Based Programming (WS-16-07), Expanding the Boundaries of Health Informatics Using AI (WS-16-08), Incentives and Trust in Electronic Communities (WS-16-09), Knowledge Extraction from Text (WS-16-10), Multiagent Interaction without Prior Coordination (WS-16-11), Planning for Hybrid Systems (WS-16-12), Scholarly Big Data: AI Perspectives, Challenges, and Ideas (WS-16-13), Symbiotic Cognitive Systems (WS-16-14), and World Wide Web and Population Health Intelligence (WS-16-15).

Online image sharing in social media sites such as Facebook, Flickr, and Instagram can lead to unwanted disclosure and privacy violations, when privacy settings are used inappropriately. With the exponential increase in the number of images that are shared online, the development of effective and efficient prediction methods for image privacy settings are highly needed. In this study, we explore deep visual features and deep image tags for image privacy prediction. The results of our experiments show that models trained on deep visual features outperform those trained on SIFT and GIST. The results also show that deep image tags combined with user tags perform best among all tested features.

Meta-analysis is a principled statistical approach for summarizing quantitative information reported across studies within a research domain of interest. Although the results of meta-analyses can be highly informative,the process of collecting and coding the data for a meta analysis is often a labor-intensive effort fraught with the potential for human error and idiosyncrasy. This is due to the fact that researchers typically spend weeks poring over published journal articles, technical reports, book chapters and other materials in order to retrieve key data elements that are then manually coded for subsequent analyses (e.g., descriptive statistics, effect sizes, reliability estimates, demographics, and study conditions).In this paper, we propose a machine learning based system developed to support automated extraction of data pertinent to STEM education meta-analyses, including educational and human resource initiatives aimed at improving achievement, literacy and interest in the fields of science, technology, engineering, and mathematics.