320 research outputs found

    Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Path

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    Relation classification is an important research arena in the field of natural language processing (NLP). In this paper, we present SDP-LSTM, a novel neural network to classify the relation of two entities in a sentence. Our neural architecture leverages the shortest dependency path (SDP) between two entities; multichannel recurrent neural networks, with long short term memory (LSTM) units, pick up heterogeneous information along the SDP. Our proposed model has several distinct features: (1) The shortest dependency paths retain most relevant information (to relation classification), while eliminating irrelevant words in the sentence. (2) The multichannel LSTM networks allow effective information integration from heterogeneous sources over the dependency paths. (3) A customized dropout strategy regularizes the neural network to alleviate overfitting. We test our model on the SemEval 2010 relation classification task, and achieve an F1F_1-score of 83.7\%, higher than competing methods in the literature.Comment: EMNLP '1

    Knowledge-based Biomedical Data Science 2019

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    Knowledge-based biomedical data science (KBDS) involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey the progress in the last year in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing, and the expansion of knowledge-based approaches to novel domains, such as Chinese Traditional Medicine and biodiversity.Comment: Manuscript 43 pages with 3 tables; Supplemental material 43 pages with 3 table

    Visual Concept-Metaconcept Learning

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    Humans reason with concepts and metaconcepts: we recognize red and green from visual input; we also understand that they describe the same property of objects (i.e., the color). In this paper, we propose the visual concept-metaconcept learner (VCML) for joint learning of concepts and metaconcepts from images and associated question-answer pairs. The key is to exploit the bidirectional connection between visual concepts and metaconcepts. Visual representations provide grounding cues for predicting relations between unseen pairs of concepts. Knowing that red and green describe the same property of objects, we generalize to the fact that cube and sphere also describe the same property of objects, since they both categorize the shape of objects. Meanwhile, knowledge about metaconcepts empowers visual concept learning from limited, noisy, and even biased data. From just a few examples of purple cubes we can understand a new color purple, which resembles the hue of the cubes instead of the shape of them. Evaluation on both synthetic and real-world datasets validates our claims.Comment: NeurIPS 2019. First two authors contributed equally. Project page: http://vcml.csail.mit.edu

    Automatic taxonomy evaluation

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    This thesis would not be made possible without the generous support of IATA.Les taxonomies sont une représentation essentielle des connaissances, jouant un rôle central dans de nombreuses applications riches en connaissances. Malgré cela, leur construction est laborieuse que ce soit manuellement ou automatiquement, et l'évaluation quantitative de taxonomies est un sujet négligé. Lorsque les chercheurs se concentrent sur la construction d'une taxonomie à partir de grands corpus non structurés, l'évaluation est faite souvent manuellement, ce qui implique des biais et se traduit souvent par une reproductibilité limitée. Les entreprises qui souhaitent améliorer leur taxonomie manquent souvent d'étalon ou de référence, une sorte de taxonomie bien optimisée pouvant service de référence. Par conséquent, des connaissances et des efforts spécialisés sont nécessaires pour évaluer une taxonomie. Dans ce travail, nous soutenons que l'évaluation d'une taxonomie effectuée automatiquement et de manière reproductible est aussi importante que la génération automatique de telles taxonomies. Nous proposons deux nouvelles méthodes d'évaluation qui produisent des scores moins biaisés: un modèle de classification de la taxonomie extraite d'un corpus étiqueté, et un modèle de langue non supervisé qui sert de source de connaissances pour évaluer les relations hyperonymiques. Nous constatons que nos substituts d'évaluation corrèlent avec les jugements humains et que les modèles de langue pourraient imiter les experts humains dans les tâches riches en connaissances.Taxonomies are an essential knowledge representation and play an important role in classification and numerous knowledge-rich applications, yet quantitative taxonomy evaluation remains to be overlooked and left much to be desired. While studies focus on automatic taxonomy construction (ATC) for extracting meaningful structures and semantics from large corpora, their evaluation is usually manual and subject to bias and low reproducibility. Companies wishing to improve their domain-focused taxonomies also suffer from lacking ground-truths. In fact, manual taxonomy evaluation requires substantial labour and expert knowledge. As a result, we argue in this thesis that automatic taxonomy evaluation (ATE) is just as important as taxonomy construction. We propose two novel taxonomy evaluation methods for automatic taxonomy scoring, leveraging supervised classification for labelled corpora and unsupervised language modelling as a knowledge source for unlabelled data. We show that our evaluation proxies can exert similar effects and correlate well with human judgments and that language models can imitate human experts on knowledge-rich tasks

    Embedding Based Link Prediction for Knowledge Graph Completion

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    Knowledge Graphs (KGs) are the most widely used representation of structured information about a particular domain consisting of billions of facts in the form of entities (nodes) and relations (edges) between them. Besides, the KGs also encapsulate the semantic type information of the entities. The last two decades have witnessed a constant growth of KGs in various domains such as government, scholarly data, biomedical domains, etc. KGs have been used in Machine Learning based applications such as entity linking, question answering, recommender systems, etc. Open KGs are mostly heuristically created, automatically generated from heterogeneous resources such as text, images, etc., or are human-curated. However, these KGs are often incomplete, i.e., there are missing links between the entities and missing links between the entities and their corresponding entity types. This thesis focuses on addressing these two challenges of link prediction for Knowledge Graph Completion (KGC): \textbf{(i)} General Link Prediction in KGs that include head and tail prediction, triple classification, and \textbf{(ii)} Entity Type Prediction. Most of the graph mining algorithms are proven to be of high complexity, deterring their usage in KG-based applications. In recent years, KG embeddings have been trained to represent the entities and relations in the KG in a low-dimensional vector space preserving the graph structure. In most published works such as the translational models, convolutional models, semantic matching, etc., the triple information is used to generate the latent representation of the entities and relations. In this dissertation, it is argued that contextual information about the entities obtained from the random walks, and textual entity descriptions, are the keys to improving the latent representation of the entities for KGC. The experimental results show that the knowledge obtained from the context of the entities supports the hypothesis. Several methods have been proposed for KGC and their effectiveness is shown empirically in this thesis. Firstly, a novel multi-hop attentive KG embedding model MADLINK is proposed for Link Prediction. It considers the contextual information of the entities by using random walks as well as textual entity descriptions of the entities. Secondly, a novel architecture exploiting the information contained in a pre-trained contextual Neural Language Model (NLM) is proposed for Triple Classification. Thirdly, the limitations of the current state-of-the-art (SoTA) entity type prediction models have been analysed and a novel entity typing model CAT2Type is proposed that exploits the Wikipedia Categories which is one of the most under-treated features of the KGs. This model can also be used to predict missing types of unseen entities i.e., the newly added entities in the KG. Finally, another novel architecture GRAND is proposed to predict the missing entity types in KGs using multi-label, multi-class, and hierarchical classification by leveraging different strategic graph walks in the KGs. The extensive experiments and ablation studies show that all the proposed models outperform the current SoTA models and set new baselines for KGC. The proposed models establish that the NLMs and the contextual information of the entities in the KGs together with the different neural network architectures benefit KGC. The promising results and observations open up interesting scopes for future research involving exploiting the proposed models in domain-specific KGs such as scholarly data, biomedical data, etc. Furthermore, the link prediction model can be exploited as a base model for the entity alignment task as it considers the neighbourhood information of the entities

    Classifying Relations using Recurrent Neural Network with Ontological-Concept Embedding

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    Relation extraction and classification represents a fundamental and challenging aspect of Natural Language Processing (NLP) research which depends on other tasks such as entity detection and word sense disambiguation. Traditional relation extraction methods based on pattern-matching using regular expressions grammars and lexico-syntactic pattern rules suffer from several drawbacks including the labor involved in handcrafting and maintaining large number of rules that are difficult to reuse. Current research has focused on using Neural Networks to help improve the accuracy of relation extraction tasks using a specific type of Recurrent Neural Network (RNN). A promising approach for relation classification uses an RNN that incorporates an ontology-based concept embedding layer in addition to word embeddings. This dissertation presents several improvements to this approach by addressing its main limitations. First, several different types of semantic relationships between concepts are incorporated into the model; prior work has only considered is-a hierarchical relationships. Secondly, a significantly larger vocabulary of concepts is used. Thirdly, an improved method for concept matching was devised. The results of adding these improvements to two state-of-the-art baseline models demonstrated an improvement to accuracy when evaluated on benchmark data used in prior studies
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