Evaluating Word Embeddings in Multi-label Classification Using Fine-grained Name Typing

Embedding models typically associate each word with a single real-valued vector, representing its different properties. Evaluation methods, therefore, need to analyze the accuracy and completeness of these properties in embeddings. This requires fine-grained analysis of embedding subspaces. Multi-label classification is an appropriate way to do so. We propose a new evaluation method for word embeddings based on multi-label classification given a word embedding. The task we use is fine-grained name typing: given a large corpus, find all types that a name can refer to based on the name embedding. Given the scale of entities in knowledge bases, we can build datasets for this task that are complementary to the current embedding evaluation datasets in: they are very large, contain fine-grained classes, and allow the direct evaluation of embeddings without confounding factors like sentence contextComment: 6 pages, The 3rd Workshop on Representation Learning for NLP (RepL4NLP @ ACL2018

Embedding Based Link Prediction for Knowledge Graph Completion

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

Leveraging distant supervision for improved named entity recognition

Les techniques d'apprentissage profond ont fait un bond au cours des dernières années, et ont considérablement changé la manière dont les tâches de traitement automatique du langage naturel (TALN) sont traitées. En quelques années, les réseaux de neurones et les plongements de mots sont rapidement devenus des composants centraux à adopter dans le domaine. La supervision distante (SD) est une technique connue en TALN qui consiste à générer automatiquement des données étiquetées à partir d'exemples partiellement annotés. Traditionnellement, ces données sont utilisées pour l'entraînement en l'absence d'annotations manuelles, ou comme données supplémentaires pour améliorer les performances de généralisation. Dans cette thèse, nous étudions comment la supervision distante peut être utilisée dans un cadre d'un TALN moderne basé sur l'apprentissage profond. Puisque les algorithmes d'apprentissage profond s'améliorent lorsqu'une quantité massive de données est fournie (en particulier pour l'apprentissage des représentations), nous revisitons la génération automatique des données avec la supervision distante à partir de Wikipédia. On applique des post-traitements sur Wikipédia pour augmenter la quantité d'exemples annotés, tout en introduisant une quantité raisonnable de bruit. Ensuite, nous explorons différentes méthodes d'utilisation de données obtenues par supervision distante pour l'apprentissage des représentations, principalement pour apprendre des représentations de mots classiques (statistiques) et contextuelles. À cause de sa position centrale pour de nombreuses applications du TALN, nous choisissons la reconnaissance d'entité nommée (NER) comme tâche principale. Nous expérimentons avec des bancs d’essai NER standards et nous observons des performances état de l’art. Ce faisant, nous étudions un cadre plus intéressant, à savoir l'amélioration des performances inter-domaines (généralisation).Recent years have seen a leap in deep learning techniques that greatly changed the way Natural Language Processing (NLP) tasks are tackled. In a couple of years, neural networks and word embeddings quickly became central components to be adopted in the domain. Distant supervision (DS) is a well-used technique in NLP to produce labeled data from partially annotated examples. Traditionally, it was mainly used as training data in the absence of manual annotations, or as additional training data to improve generalization performances. In this thesis, we study how distant supervision can be employed within a modern deep learning based NLP framework. As deep learning algorithms gets better when massive amount of data is provided (especially for representation learning), we revisit the task of generating distant supervision data from Wikipedia. We apply post-processing treatments on the original dump to further increase the quantity of labeled examples, while introducing a reasonable amount of noise. Then, we explore different methods for using distant supervision data for representation learning, mainly to learn classic and contextualized word representations. Due to its importance as a basic component in many NLP applications, we choose Named-Entity Recognition (NER) as our main task. We experiment on standard NER benchmarks showing state-of-the-art performances. By doing so, we investigate a more interesting setting, that is, improving the cross-domain (generalization) performances