Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Artificial Intelligence
Title: Template-based Question Answering using Recursive Neural Networks
Abstract: Most question answering (QA) systems over Linked Data, i.e. Knowledge Graphs, approach the question answering task as a conversion from a natural language question to its corresponding SPARQL query. A common approach is to use query templates to generate SPARQL queries with slots that need to be filled. Using templates instead of running an extensive NLP pipeline or end-to-end model shifts the QA problem into a classification task, where the system needs to match the input question to the appropriate template. This paper presents an approach to automatically learn and classify natural language questions into corresponding templates using recursive neural networks. Our model was trained on 5000 questions and their respective SPARQL queries from the preexisting LC-QuAD dataset grounded in DBpedia, spanning 5042 entities and 615 predicates. The resulting model was evaluated using the FAIR GERBIL QA framework resulting in 0.419 macro f-measure on LC-QuAD and 0.417 macro f-measure on QALD-7.
Extracted statement — Material: LC-QuAD dataset
Research field: Artificial Intelligence
Title: Learning ontology from relational database
Abstract: Ontology provides a shared and reusable piece of knowledge about a specific domain, and has been applied in many fields, such as semantic Web, e-commerce and information retrieval, etc. However, building ontology by hand is a very hard and error-prone task. Learning ontology from existing resources is a good solution. Because relational database is widely used for storing data and OWL is the latest standard recommended by W3C, this paper proposes an approach of learning OWL ontology from data in relational database. Compared with existing methods, the approach can acquire ontology from relational database automatically by using a group of learning rules instead of using a middle model. In addition, it can obtain OWL ontology, including the classes, properties, properties characteristics, cardinality and instances, while none of existing methods can acquire all of them. The proposed learning rules have been proven to be correct by practice.
Extracted statement — Learning method: Learning rule
Research field: Artificial Intelligence
Title: Sarcasm Detection Using Soft Attention-Based Bidirectional Long Short-Term Memory Model With Convolution Network
Abstract: A large community of research has been developed in recent years to analyze social media and social networks, with the aim of understanding, discovering insights, and exploiting the available information. The focus has shifted from conventional polarity classification to contemporary application-oriented fine-grained aspects such as, emotions, sarcasm, stance, rumor, and hate speech detection in the user-generated content. Detecting a sarcastic tone in natural language hinders the performance of sentiment analysis tasks. The majority of the studies on automatic sarcasm detection emphasize on the use of lexical, syntactic, or pragmatic features that are often unequivocally expressed through figurative literary devices such as words, emoticons, and exclamation marks. In this paper, we propose a deep learning model called sAtt-BLSTM convNet that is based on the hybrid of soft attention-based bidirectional long short-term memory (sAtt-BLSTM) and convolution neural network (convNet) applying global vectors for word representation (GLoVe) for building semantic word embeddings. In addition to the feature maps generated by the sAtt-BLSTM, punctuation-based auxiliary features are also merged into the convNet. The robustness of the proposed model is investigated using balanced (tweets from benchmark SemEval 2015 Task 11) and unbalanced (approximately 40000 random tweets using the Sarcasm Detector tool with 15000 sarcastic and 25000 non-sarcastic messages) datasets. An experimental study using the training- and test-set accuracy metrics is performed to compare the proposed deep neural model with convNet, LSTM, and bidirectional LSTM with/without attention and it is observed that the novel sAtt-BLSTM convNet model outperforms others with a superior sarcasm-classification accuracy of 97.87% for the Twitter dataset and 93.71% for the random-tweet dataset.
Extracted statement — Data: lexical, syntactic, or pragmatic features
Research field: Artificial Intelligence
Title: A Relational Learning Approach for Collective Entity Resolution in the Web of Data
Abstract: The integration of different datasets in the Linked Data Cloud is a key aspect to the success of the Web of Data. To tackle this problem most of existent solutions have been supported by the task of entity resolution. However, many challenges still prevail specially when considering different types, structures and vocabularies used in the Web. Another common problem is that data usually are incomplete, inconsistent and contain outliers. To overcome these limitations, some works have applied machine learning algorithms since they are typically robust to both noise and data inconsistencies and are able to efficiently utilize nondeterministic dependencies in the data. In this paper we propose an approach based in a relational learning algorithm that addresses the problem by statistical approximation method. Modeling the problem as a relational machine learning task allows exploit contextual information that might be too distant in the relational graph. The joint application of relationship patterns between entities and evidences of similarity between their descriptions can improve the effectiveness of results. Furthermore, it is based on a sparse structure that scales well to large datasets. We present initial experiments based on BTC2012 datasets.
Extracted statement — Material: Linked Data Cloud
Research field: Artificial Intelligence
Title: Towards Exploring Literals to Enrich Data Linking in Knowledge Graphs
Abstract: Knowledge graph completion is still a challenging solution that uses techniques from distinct areas to solve many different tasks. Most recent works, which are based on embedding models, were conceived to improve an existing knowledge graph using the link prediction task. However, even considering the ability of these solutions to solve other tasks, they did not present results for data linking, for example. Furthermore, most of these works focuses only on structural information, i.e., the relations between entities. In this paper, we present an approach for data linking that enrich entity embeddings in a model with their literal information and that do not rely on external information of these entities. The key aspect of this proposal is that we use a blocking scheme to improve the effectiveness of the solution in relation to the use of literals. Thus, in addition to the literals from object elements in a triple, we use other literals from subjects and predicates. By merging entity embeddings with their literal information it is possible to extend many popular embedding models. Preliminary experiments were performed on real-world datasets and our solution showed competitive results to the performance of the task of data linking.
Extracted statement — Material: literals from subjects and predicates
Research field: Artificial Intelligence
Title: Speech Recognition Using Deep Neural Networks: A Systematic Review
Abstract: Over the past decades, a tremendous amount of research has been done on the use of machine learning for speech processing applications, especially speech recognition. However, in the past few years, research has focused on utilizing deep learning for speech-related applications. This new area of machine learning has yielded far better results when compared to others in a variety of applications including speech, and thus became a very attractive area of research. This paper provides a thorough examination of the different studies that have been conducted since 2006, when deep learning first arose as a new area of machine learning, for speech applications. A thorough statistical analysis is provided in this review which was conducted by extracting specific information from 174 papers published between the years 2006 and 2018. The results provided in this paper shed light on the trends of research in this area as well as bring focus to new research topics.
Extracted statement — Method: Machine learning
Research field: Artificial Intelligence
Title: Detection and Diagnosis of Breast Cancer Using Artificial Intelligence Based Assessment of Maximum Intensity Projection Dynamic Contrast-Enhanced Magnetic Resonance Images
Abstract: We aimed to evaluate an artificial intelligence (AI) system that can detect and diagnose lesions of maximum intensity projection (MIP) in dynamic contrast-enhanced (DCE) breast magnetic resonance imaging (MRI). We retrospectively gathered MIPs of DCE breast MRI for training and validation data from 30 and 7 normal individuals, 49 and 20 benign cases, and 135 and 45 malignant cases, respectively. Breast lesions were indicated with a bounding box and labeled as benign or malignant by a radiologist, while the AI system was trained to detect and calculate possibilities of malignancy using RetinaNet. The AI system was analyzed using test sets of 13 normal, 20 benign, and 52 malignant cases. Four human readers also scored these test data with and without the assistance of the AI system for the possibility of a malignancy in each breast. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were 0.926, 0.828, and 0.925 for the AI system; 0.847, 0.841, and 0.884 for human readers without AI; and 0.889, 0.823, and 0.899 for human readers with AI using a cutoff value of 2%, respectively. The AI system showed better diagnostic performance compared to the human readers (p = 0.002), and because of the increased performance of human readers with the assistance of the AI system, the AUC of human readers was significantly higher with than without the AI system (p = 0.039). Our AI system showed a high performance ability in detecting and diagnosing lesions in MIPs of DCE breast MRI and increased the diagnostic performance of human readers.
Extracted statement — Imaging modality : Magnetic resonance imaging
Research field: Artificial Intelligence
Title: A Markov-Switching Model Approach to Heart Sound Segmentation and Classification
Abstract: Objective: We consider challenges in accurate segmentation of heart sound signals recorded under noisy clinical environments for subsequent classification of pathological events. Existing state-of-the-art solutions to heart sound segmentation use probabilistic models such as hidden Markov models (HMMs), which, however, are limited by its observation independence assumption and rely on pre-extraction of noise-robust features. Methods: We propose a Markov-switching autoregressive (MSAR) process to model the raw heart sound signals directly, which allows efficient segmentation of the cyclical heart sound states according to the distinct dependence structure in each state. To enhance robustness, we extend the MSAR model to a switching linear dynamic system (SLDS) that jointly model both the switching AR dynamics of underlying heart sound signals and the noise effects. We introduce a novel algorithm via fusion of switching Kalman filter and the duration-dependent Viterbi algorithm, which incorporates the duration of heart sound states to improve state decoding. Results: Evaluated on Physionet/CinC Challenge 2016 dataset, the proposed MSAR-SLDS approach significantly outperforms the hidden semi-Markov model (HSMM) in heart sound segmentation based on raw signals and comparable to a feature-based HSMM. The segmented labels were then used to train Gaussian-mixture HMM classifier for identification of abnormal beats, achieving high average precision of 86.1% on the same dataset including very noisy recordings. Conclusion: The proposed approach shows noticeable performance in heart sound segmentation and classification on a large noisy dataset. Significance: It is potentially useful in developing automated heart monitoring systems for pre-screening of heart pathologies.
Extracted statement — uses: Markov-switching autoregressive (MSAR)
Research field: Artificial Intelligence
Title: Multi-Class Sentiment Analysis in Twitter: What if Classification is Not the Answer
Abstract: With the rapid growth of online social media content, and the impact these have made on people’s behavior, many researchers have been interested in studying these media platforms. A major part of their work focused on sentiment analysis and opinion mining. These refer to the automatic identification of opinions of people toward specific topics by analyzing their posts and publications. Multi-class sentiment analysis, in particular, addresses the identification of the exact sentiment conveyed by the user rather than the overall sentiment polarity of his text message or post. That being the case, we introduce a task different from the conventional multi-class classification, which we run on a data set collected from Twitter. We refer to this task as “quantification.” By the term “quantification,” we mean the identification of all the existing sentiments within an online post (i.e., tweet) instead of attributing a single sentiment label to it. For this sake, we propose an approach that automatically attributes different scores to each sentiment in a tweet, and selects the sentiments with the highest scores which we judge as conveyed in the text. To reach this target, we added to our previously introduced tool SENTA the necessary components to run and perform such a task. Throughout this work, we present the added components; we study the feasibility of quantification, and propose an approach to perform it on a data set made of tweets for 11 different sentiment classes. The data set was manually labeled and the results of the automatic analysis were checked against the human annotation. Our experiments show the feasibility of this task and reach an F1 score equal to 45.9%.
Extracted statement — Material: media platforms
Research field: Artificial Intelligence
Title: Multi-Class Sentiment Analysis in Twitter: What if Classification is Not the Answer
Abstract: With the rapid growth of online social media content, and the impact these have made on people’s behavior, many researchers have been interested in studying these media platforms. A major part of their work focused on sentiment analysis and opinion mining. These refer to the automatic identification of opinions of people toward specific topics by analyzing their posts and publications. Multi-class sentiment analysis, in particular, addresses the identification of the exact sentiment conveyed by the user rather than the overall sentiment polarity of his text message or post. That being the case, we introduce a task different from the conventional multi-class classification, which we run on a data set collected from Twitter. We refer to this task as “quantification.” By the term “quantification,” we mean the identification of all the existing sentiments within an online post (i.e., tweet) instead of attributing a single sentiment label to it. For this sake, we propose an approach that automatically attributes different scores to each sentiment in a tweet, and selects the sentiments with the highest scores which we judge as conveyed in the text. To reach this target, we added to our previously introduced tool SENTA the necessary components to run and perform such a task. Throughout this work, we present the added components; we study the feasibility of quantification, and propose an approach to perform it on a data set made of tweets for 11 different sentiment classes. The data set was manually labeled and the results of the automatic analysis were checked against the human annotation. Our experiments show the feasibility of this task and reach an F1 score equal to 45.9%.
Extracted statement — Method: Multi-class sentiment analysis
Research field: Artificial Intelligence
Title: Image-Based Food Calorie Estimation Using Knowledge on Food Categories, Ingredients and Cooking Directions
Abstract: Image-based food calorie estimation is crucial to diverse mobile applications for recording everyday meal. However, some of them need human help for calorie estimation, and even if it is automatic, food categories are often limited or images from multiple viewpoints are required. Then, it is not yet achieved to estimate food calorie with practical accuracy and estimating food calories from a food photo is an unsolved problem. Therefore, in this paper, we propose estimating food calorie from a food photo by simultaneous learning of food calories, categories, ingredients and cooking directions using deep learning. Since there exists a strong correlation between food calories and food categories, ingredients and cooking directions information in general, we expect that simultaneous training of them brings performance boosting compared to independent single training. To this end, we use a multi-task CNN [1]. In addition, in this research, we construct two kinds of datasets that is a dataset of calorie-annotated recipe collected from Japanese recipe sites on the Web and a dataset collected from an American recipe site. In this experiment, we trained multi-task and single-task CNNs. As a result, the multi-task CNN achieved the better performance on both food category estimation and food calorie estimation than single-task CNNs. For the Japanese recipe dataset, by introducing a multi-task CNN, 0.039 were improved on the correlation coefficient, while for the American recipe dataset, 0.090 were raised compared to the result by the single-task CNN.
Extracted statement — Machine Learning Method: multi-task CNN
Research field: Artificial Intelligence
Title: Image-Based Food Calorie Estimation Using Knowledge on Food Categories, Ingredients and Cooking Directions
Abstract: Image-based food calorie estimation is crucial to diverse mobile applications for recording everyday meal. However, some of them need human help for calorie estimation, and even if it is automatic, food categories are often limited or images from multiple viewpoints are required. Then, it is not yet achieved to estimate food calorie with practical accuracy and estimating food calories from a food photo is an unsolved problem. Therefore, in this paper, we propose estimating food calorie from a food photo by simultaneous learning of food calories, categories, ingredients and cooking directions using deep learning. Since there exists a strong correlation between food calories and food categories, ingredients and cooking directions information in general, we expect that simultaneous training of them brings performance boosting compared to independent single training. To this end, we use a multi-task CNN [1]. In addition, in this research, we construct two kinds of datasets that is a dataset of calorie-annotated recipe collected from Japanese recipe sites on the Web and a dataset collected from an American recipe site. In this experiment, we trained multi-task and single-task CNNs. As a result, the multi-task CNN achieved the better performance on both food category estimation and food calorie estimation than single-task CNNs. For the Japanese recipe dataset, by introducing a multi-task CNN, 0.039 were improved on the correlation coefficient, while for the American recipe dataset, 0.090 were raised compared to the result by the single-task CNN.
Extracted statement — Machine Learning Method: Multi-task CNN
Research field: Artificial Intelligence
Title: Multi-Task Learning for Calorie Prediction on a Novel Large-Scale Recipe Dataset Enriched with Nutritional Information
Abstract: A rapidly growing amount of content posted online, such as food recipes, opens doors to new exciting applications at the intersection of vision and language. In this work, we aim to estimate the calorie amount of a meal directly from an image by learning from recipes people have published on the Internet, thus skipping time-consuming manual data annotation. Since there are few large-scale publicly available datasets captured in unconstrained environments, we propose the pic2kcal benchmark comprising 308 000 images from over 70 000 recipes including photographs, ingredients, and instructions. To obtain nutritional information of the ingredients and automatically determine the ground-truth calorie value, we match the items in the recipes with structured information from a food item database. We evaluate various neural networks for regression of the calorie quantity and extend them with the multi-task paradigm. Our learning procedure combines the calorie estimation with prediction of proteins, carbohydrates, and fat amounts as well as a multi-label ingredient classification. Our experiments demonstrate clear benefits of multi-task learning for calorie estimation, surpassing the single-task calorie regression by 9.9%. To encourage further research on this task, we make the code for generating the dataset and the models publicly available.
Extracted statement — Machine Learning Method: Multi-Task Learning
Research field: Artificial Intelligence
Title: Sarcasm Detection Using Soft Attention-Based Bidirectional Long Short-Term Memory Model With Convolution Network
Abstract: A large community of research has been developed in recent years to analyze social media and social networks, with the aim of understanding, discovering insights, and exploiting the available information. The focus has shifted from conventional polarity classification to contemporary application-oriented fine-grained aspects such as, emotions, sarcasm, stance, rumor, and hate speech detection in the user-generated content. Detecting a sarcastic tone in natural language hinders the performance of sentiment analysis tasks. The majority of the studies on automatic sarcasm detection emphasize on the use of lexical, syntactic, or pragmatic features that are often unequivocally expressed through figurative literary devices such as words, emoticons, and exclamation marks. In this paper, we propose a deep learning model called sAtt-BLSTM convNet that is based on the hybrid of soft attention-based bidirectional long short-term memory (sAtt-BLSTM) and convolution neural network (convNet) applying global vectors for word representation (GLoVe) for building semantic word embeddings. In addition to the feature maps generated by the sAtt-BLSTM, punctuation-based auxiliary features are also merged into the convNet. The robustness of the proposed model is investigated using balanced (tweets from benchmark SemEval 2015 Task 11) and unbalanced (approximately 40000 random tweets using the Sarcasm Detector tool with 15000 sarcastic and 25000 non-sarcastic messages) datasets. An experimental study using the training- and test-set accuracy metrics is performed to compare the proposed deep neural model with convNet, LSTM, and bidirectional LSTM with/without attention and it is observed that the novel sAtt-BLSTM convNet model outperforms others with a superior sarcasm-classification accuracy of 97.87% for the Twitter dataset and 93.71% for the random-tweet dataset.
Extracted statement — Material: natural language