From Word to Sense Embeddings: A Survey on Vector Representations of Meaning

Over the past years, distributed semantic representations have proved to be effective and flexible keepers of prior knowledge to be integrated into downstream applications. This survey focuses on the representation of meaning. We start from the theoretical background behind word vector space models and highlight one of their major limitations: the meaning conflation deficiency, which arises from representing a word with all its possible meanings as a single vector. Then, we explain how this deficiency can be addressed through a transition from the word level to the more fine-grained level of word senses (in its broader acceptation) as a method for modelling unambiguous lexical meaning. We present a comprehensive overview of the wide range of techniques in the two main branches of sense representation, i.e., unsupervised and knowledge-based. Finally, this survey covers the main evaluation procedures and applications for this type of representation, and provides an analysis of four of its important aspects: interpretability, sense granularity, adaptability to different domains and compositionality.Comment: 46 pages, 8 figures. Published in Journal of Artificial Intelligence Researc

Relation Classification with Limited Supervision

Large reams of unstructured data, for instance in form textual document collections containing entities and relations, exist in many domains. The process of deriving valuable domain insights and intelligence from such documents collections usually involves the extraction of information such as the relations between the entities in such collections. Relation classification is the task of detecting relations between entities. Supervised machine learning models, which have become the tool of choice for relation classification, require substantial quantities of annotated data for each relation in order to perform optimally. For many domains, such quantities of annotated data for relations may not be readily available, and manually curating such annotations may not be practical due to time and cost constraints. In this work, we develop both model-specific and model-agnostic approaches for relation classification with limited supervision. We start by proposing an approach for learning embeddings for contextual surface patterns, which are the set of surface patterns associated with entity pairs across a text corpus, to provide additional supervision signals for relation classification with limited supervision. We find that this approach improves classification performance on relations with limited supervision instances. However, this initial approach assumes the availability of at least one annotated instance per relation during training. In order to address this limitation, we propose an approach which formulates the task of relation classification as that of textual entailment. This reformulation allows us to use the textual descriptions of relations to classify their instances. It also allows us to utilize existing textual entailment datasets and models to classify relations with zero supervision instances. The two methods proposed previously rely on the use of specific model architectures for relation classification. Since a wide variety of models have been proposed for relation classification in the literature, a more general approach is thus desirable. We subsequently propose our first model-agnostic meta-learning algorithm for relation classification with limited supervision. This algorithm is applicable to any gradient-optimized relation classification model. We show that the proposed approach improves the predictive performance of two existing relation classification models when supervision for relations is limited. Next, because all the approaches we have proposed so far assume the availability of all supervision needed for classifying relations prior to model training, they are unable to handle the case when new supervision for relations becomes available after training. Such new supervision may need to be incorporated into the model to enable it classify new relations or to improve its performance on existing relations. Our last approach addresses this short-coming. We propose a model-agnostic algorithm which enables relation classification models to learn continually from new supervision as it becomes available, while doing so in a data-efficient manner and without forgetting knowledge of previous relations

Topic Modelling Meets Deep Neural Networks: A Survey

Topic modelling has been a successful technique for text analysis for almost twenty years. When topic modelling met deep neural networks, there emerged a new and increasingly popular research area, neural topic models, with over a hundred models developed and a wide range of applications in neural language understanding such as text generation, summarisation and language models. There is a need to summarise research developments and discuss open problems and future directions. In this paper, we provide a focused yet comprehensive overview of neural topic models for interested researchers in the AI community, so as to facilitate them to navigate and innovate in this fast-growing research area. To the best of our knowledge, ours is the first review focusing on this specific topic.Comment: A review on Neural Topic Model

Representation Learning for Texts and Graphs: A Unified Perspective on Efficiency, Multimodality, and Adaptability

[...] This thesis is situated between natural language processing and graph representation learning and investigates selected connections. First, we introduce matrix embeddings as an efficient text representation sensitive to word order. [...] Experiments with ten linguistic probing tasks, 11 supervised, and five unsupervised downstream tasks reveal that vector and matrix embeddings have complementary strengths and that a jointly trained hybrid model outperforms both. Second, a popular pretrained language model, BERT, is distilled into matrix embeddings. [...] The results on the GLUE benchmark show that these models are competitive with other recent contextualized language models while being more efficient in time and space. Third, we compare three model types for text classification: bag-of-words, sequence-, and graph-based models. Experiments on five datasets show that, surprisingly, a wide multilayer perceptron on top of a bag-of-words representation is competitive with recent graph-based approaches, questioning the necessity of graphs synthesized from the text. [...] Fourth, we investigate the connection between text and graph data in document-based recommender systems for citations and subject labels. Experiments on six datasets show that the title as side information improves the performance of autoencoder models. [...] We find that the meaning of item co-occurrence is crucial for the choice of input modalities and an appropriate model. Fifth, we introduce a generic framework for lifelong learning on evolving graphs in which new nodes, edges, and classes appear over time. [...] The results show that by reusing previous parameters in incremental training, it is possible to employ smaller history sizes with only a slight decrease in accuracy compared to training with complete history. Moreover, weighting the binary cross-entropy loss function is crucial to mitigate the problem of class imbalance when detecting newly emerging classes. [...

Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective

This paper takes a problem-oriented perspective and presents a comprehensive review of transfer learning methods, both shallow and deep, for cross-dataset visual recognition. Specifically, it categorises the cross-dataset recognition into seventeen problems based on a set of carefully chosen data and label attributes. Such a problem-oriented taxonomy has allowed us to examine how different transfer learning approaches tackle each problem and how well each problem has been researched to date. The comprehensive problem-oriented review of the advances in transfer learning with respect to the problem has not only revealed the challenges in transfer learning for visual recognition, but also the problems (e.g. eight of the seventeen problems) that have been scarcely studied. This survey not only presents an up-to-date technical review for researchers, but also a systematic approach and a reference for a machine learning practitioner to categorise a real problem and to look up for a possible solution accordingly