Investigations into the value of labeled and unlabeled data in biomedical entity recognition and word sense disambiguation

Human annotations, especially in highly technical domains, are expensive and time consuming togather, and can also be erroneous. As a result, we never have sufficiently accurate data to train andevaluate supervised methods. In this thesis, we address this problem by taking a semi-supervised approach to biomedical namedentity recognition (NER), and by proposing an inventory-independent evaluation framework for supervised and unsupervised word sense disambiguation. Our contributions are as follows: We introduce a novel graph-based semi-supervised approach to named entity recognition(NER) and exploit pre-trained contextualized word embeddings in several biomedical NER tasks. We propose a new evaluation framework for word sense disambiguation that permits a fair comparison between supervised methods trained on different sense inventories as well as unsupervised methods without a fixed sense inventory

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

Meta-Learning with Variational Semantic Memory for Word Sense Disambiguation

A critical challenge faced by supervised word sense disambiguation (WSD) is the lack of large annotated datasets with sufficient coverage of words in their diversity of senses. This inspired recent research on few-shot WSD using meta-learning. While such work has successfully applied meta-learning to learn new word senses from very few examples, its performance still lags behind its fully supervised counterpart. Aiming to further close this gap, we propose a model of semantic memory for WSD in a meta-learning setting. Semantic memory encapsulates prior experiences seen throughout the lifetime of the model, which aids better generalization in limited data settings. Our model is based on hierarchical variational inference and incorporates an adaptive memory update rule via a hypernetwork. We show our model advances the state of the art in few-shot WSD, supports effective learning in extremely data scarce (e.g. one-shot) scenarios and produces meaning prototypes that capture similar senses of distinct words.Comment: 15 pages, 5 figure

Tag disambiguation based on social network information

Within 20 years the Web has grown from a tool for scientists at CERN into a global information space. While returning to its roots as a read/write tool, its entering a more social and participatory phase. Hence a new, improved version called the Social Web where users are responsible for generating and sharing content on the global information space, they are also accountable for replicating the information. This collaborative activity can be observed in two of the most widely practised Social Web services such as social network sites and social tagging systems. Users annotate their interests and inclinations with free form keywords while they share them with their social connections. Although these keywords (tag) assist information organization and retrieval, theysuffer from polysemy.In this study we employ the effectiveness of social network sites to address the issue of ambiguity in social tagging. Moreover, we also propose that homophily in social network sites can be a useful aspect is disambiguating tags. We have extracted the ‘Likes’ of 20 Facebook users and employ them in disambiguation tags on Flickr. Classifiers are generated on the retrieved clusters from Flickr using K-Nearest-Neighbour algorithm and then their degree of similarity is calculated with user keywords. As tag disambiguation techniques lack gold standards for evaluation, we asked the users to indicate the contexts and used them as ground truth while examining the results. We analyse the performance of our approach by quantitative methods and report successful results. Our proposed method is able classify images with an accuracy of 6 out of 10 (on average). Qualitative analysis reveal some factors that affect the findings, and if addressed can produce more precise results

Delving into the uncharted territories of Word Sense Disambiguation

The automatic disambiguation of word senses, i.e. Word Sense Disambiguation, is a long-standing task in the field of Natural Language Processing; an AI-complete problem that took its first steps more than half a century ago, and which, to date, has apparently attained human-like performances on standard evaluation benchmarks. Unfortunately, the steady evolution that the task experienced over time in terms of sheer performance has not been followed hand in hand by adequate theoretical support, nor by careful error analysis. Furthermore, we believe that the lack of an exhaustive bird’s eye view which accounts for the sort of high-end and unrealistic computational architectures that systems will soon need in order to further refine their performances could lead the field to a dead angle in a few years. In essence, taking advantage of the current moment of great accomplishments and renewed interest in the task, we argue that Word Sense Disambiguation is mature enough for researchers to really observe the extent of the results hitherto obtained, evaluate what is actually missing, and answer the much sought for question: “are current state-of-the-art systems really able to effectively solve lexical ambiguity?” Driven by the desire to become both architects and participants in this period of pondering, we have identified a few macro-areas representatives of the challenges of automatic disambiguation. From this point of view, in this thesis, we propose experimental solutions and empirical tools so as to bring to the attention of the Word Sense Disambiguation community unusual and unexplored points of view. We hope these will represent a new perspective through which to best observe the current state of disambiguation, as well as to foresee future paths for the task to evolve on. Specifically, 1q) prompted by the growing concern about the rise in performance being closely linked to the demand for more and more unrealistic computational architectures in all areas of application of Deep Learning related techniques, we 1a) provide evidence for the undisclosed potential of approaches based on knowledge-bases, via the exploitation of syntagmatic information. Moreover, 2q) driven by the dissatisfaction with the use of cognitively-inaccurate, finite inventories of word senses in Word Sense Disambiguation, we 2a) introduce an approach based on Definition Modeling paradigms to generate contextual definitions for target words and phrases, hence going beyond the limits set by specific lexical-semantic inventories. Finally, 3q) moved by the desire to analyze the real implications beyond the idea of “machines performing disambiguation on par with their human counterparts” we 3a) put forward a detailed analysis of the shared errors affecting current state-of-the-art systems based on diverse approaches for Word Sense Disambiguation, and highlight, by means of a novel evaluation dataset tailored to represent common and critical issues shared by all systems, performances way lower than those usually reported in the current literature

People on Drugs: Credibility of User Statements in Health Communities

Online health communities are a valuable source of information for patients and physicians. However, such user-generated resources are often plagued by inaccuracies and misinformation. In this work we propose a method for automatically establishing the credibility of user-generated medical statements and the trustworthiness of their authors by exploiting linguistic cues and distant supervision from expert sources. To this end we introduce a probabilistic graphical model that jointly learns user trustworthiness, statement credibility, and language objectivity. We apply this methodology to the task of extracting rare or unknown side-effects of medical drugs --- this being one of the problems where large scale non-expert data has the potential to complement expert medical knowledge. We show that our method can reliably extract side-effects and filter out false statements, while identifying trustworthy users that are likely to contribute valuable medical information