Graph-based approaches to word sense induction

This thesis is a study of Word Sense Induction (WSI), the Natural Language Processing (NLP) task of automatically discovering word meanings from text. WSI is an open problem in NLP whose solution would be of considerable benefit to many other NLP tasks. It has, however, has been studied by relatively few NLP researchers and often in set ways. Scope therefore exists to apply novel methods to the problem, methods that may improve upon those previously applied. This thesis applies a graph-theoretic approach to WSI. In this approach, word senses are identifed by finding particular types of subgraphs in word co-occurrence graphs. A number of original methods for constructing, analysing, and partitioning graphs are introduced, with these methods then incorporated into graphbased WSI systems. These systems are then shown, in a variety of evaluation scenarios, to return results that are comparable to those of the current best performing WSI systems. The main contributions of the thesis are a novel parameter-free soft clustering algorithm that runs in time linear in the number of edges in the input graph, and novel generalisations of the clustering coeficient (a measure of vertex cohesion in graphs) to the weighted case. Further contributions of the thesis include: a review of graph-based WSI systems that have been proposed in the literature; analysis of the methodologies applied in these systems; analysis of the metrics used to evaluate WSI systems, and empirical evidence to verify the usefulness of each novel method introduced in the thesis for inducing word senses

Co-training an Unsupervised Constituency Parser with Weak Supervision

We introduce a method for unsupervised parsing that relies on bootstrapping classifiers to identify if a node dominates a specific span in a sentence. There are two types of classifiers, an inside classifier that acts on a span, and an outside classifier that acts on everything outside of a given span. Through self-training and co-training with the two classifiers, we show that the interplay between them helps improve the accuracy of both, and as a result, effectively parse. A seed bootstrapping technique prepares the data to train these classifiers. Our analyses further validate that such an approach in conjunction with weak supervision using prior branching knowledge of a known language (left/right-branching) and minimal heuristics injects strong inductive bias into the parser, achieving 63.1 F$_1$ on the English (PTB) test set. In addition, we show the effectiveness of our architecture by evaluating on treebanks for Chinese (CTB) and Japanese (KTB) and achieve new state-of-the-art results. Our code and pre-trained models are available at https://github.com/Nickil21/weakly-supervised-parsing.Comment: Accepted to Findings of ACL 202

Acquiring and Harnessing Verb Knowledge for Multilingual Natural Language Processing

Advances in representation learning have enabled natural language processing models to derive non-negligible linguistic information directly from text corpora in an unsupervised fashion. However, this signal is underused in downstream tasks, where they tend to fall back on superficial cues and heuristics to solve the problem at hand. Further progress relies on identifying and filling the gaps in linguistic knowledge captured in their parameters. The objective of this thesis is to address these challenges focusing on the issues of resource scarcity, interpretability, and lexical knowledge injection, with an emphasis on the category of verbs. To this end, I propose a novel paradigm for efficient acquisition of lexical knowledge leveraging native speakers’ intuitions about verb meaning to support development and downstream performance of NLP models across languages. First, I investigate the potential of acquiring semantic verb classes from non-experts through manual clustering. This subsequently informs the development of a two-phase semantic dataset creation methodology, which combines semantic clustering with fine-grained semantic similarity judgments collected through spatial arrangements of lexical stimuli. The method is tested on English and then applied to a typologically diverse sample of languages to produce the first large-scale multilingual verb dataset of this kind. I demonstrate its utility as a diagnostic tool by carrying out a comprehensive evaluation of state-of-the-art NLP models, probing representation quality across languages and domains of verb meaning, and shedding light on their deficiencies. Subsequently, I directly address these shortcomings by injecting lexical knowledge into large pretrained language models. I demonstrate that external manually curated information about verbs’ lexical properties can support data-driven models in tasks where accurate verb processing is key. Moreover, I examine the potential of extending these benefits from resource-rich to resource-poor languages through translation-based transfer. The results emphasise the usefulness of human-generated lexical knowledge in supporting NLP models and suggest that time-efficient construction of lexicons similar to those developed in this work, especially in under-resourced languages, can play an important role in boosting their linguistic capacity.ESRC Doctoral Fellowship [ES/J500033/1], ERC Consolidator Grant LEXICAL [648909

Exploiting Cross-Lingual Representations For Natural Language Processing

Traditional approaches to supervised learning require a generous amount of labeled data for good generalization. While such annotation-heavy approaches have proven useful for some Natural Language Processing (NLP) tasks in high-resource languages (like English), they are unlikely to scale to languages where collecting labeled data is di cult and time-consuming. Translating supervision available in English is also not a viable solution, because developing a good machine translation system requires expensive to annotate resources which are not available for most languages. In this thesis, I argue that cross-lingual representations are an effective means of extending NLP tools to languages beyond English without resorting to generous amounts of annotated data or expensive machine translation. These representations can be learned in an inexpensive manner, often from signals completely unrelated to the task of interest. I begin with a review of different ways of inducing such representations using a variety of cross-lingual signals and study algorithmic approaches of using them in a diverse set of downstream tasks. Examples of such tasks covered in this thesis include learning representations to transfer a trained model across languages for document classification, assist in monolingual lexical semantics like word sense induction, identify asymmetric lexical relationships like hypernymy between words in different languages, or combining supervision across languages through a shared feature space for cross-lingual entity linking. In all these applications, the representations make information expressed in other languages available in English, while requiring minimal additional supervision in the language of interest

Fractals in the Nervous System: conceptual Implications for Theoretical Neuroscience

This essay is presented with two principal objectives in mind: first, to document the prevalence of fractals at all levels of the nervous system, giving credence to the notion of their functional relevance; and second, to draw attention to the as yet still unresolved issues of the detailed relationships among power law scaling, self-similarity, and self-organized criticality. As regards criticality, I will document that it has become a pivotal reference point in Neurodynamics. Furthermore, I will emphasize the not yet fully appreciated significance of allometric control processes. For dynamic fractals, I will assemble reasons for attributing to them the capacity to adapt task execution to contextual changes across a range of scales. The final Section consists of general reflections on the implications of the reviewed data, and identifies what appear to be issues of fundamental importance for future research in the rapidly evolving topic of this review

Unsupervised entity linking using graph-based semantic similarity

Nowadays, the human textual data constitutes a great proportion of the shared information resources such as World Wide Web (WWW). Social networks, news and learning resources as well as Knowledge Bases (KBs) are just the small examples that widely contain the textual data which is used by both human and machine readers. The nature of human languages is highly ambiguous, means that a short portion of a textual context (such as words or phrases) can semantically be interpreted in different ways. A language processor should detect the best interpretation depending on the context in which each word or phrase appears. In case of human readers, the brain is quite proficient in interfering textual data. Human language developed in a way that reflects the innate ability provided by the brain’s neural networks. However, there still exist the moments that the text disambiguation task would remain a hard challenge for the human readers. In case of machine readers, it has been a long-term challenge to develop the ability to do natural language processing and machine learning. Different interpretation can change the broad range of topics and targets. The different in interpretation can cause serious impacts when it is used in critical domains that need high precision. Thus, the correctly inferring the ambiguous words would be highly crucial. To tackle it, two tasks have been developed: Word Sense Disambiguation (WSD) to infer the sense (i.e. meaning) of ambiguous words, when the word has multiple meanings, and Entity Linking (EL) (also called, Named Entity Disambiguation–NED, Named Entity Recognition and Disambiguation–NERD, or Named Entity Normalization–NEN) which is used to explore the correct reference of Named Entity (NE) mentions occurring in documents. The solution to these problems impacts other computer-related writing, such as discourse, improving relevance of search engines, anaphora resolution, coherence, and inference. This document summarizes the works towards developing an unsupervised Entity Linking (EL) system using graph-based semantic similarity aiming to disambiguate Named Entity (NE) mentions occurring in a target document. The EL task is highly challenging since each entity can usually be referred to by several NE mentions (synonymy). In addition, a NE mention may be used to indicate distinct entities (polysemy). Thus, much effort is necessary to tackle these challenges. Our EL system disambiguates the NE mentions in several steps. For each step, we have proposed, implemented, and evaluated several approaches. We evaluated our EL system in TAC-KBP4 English EL evaluation framework in which the system input consists of a set of queries, each containing a query name (target NE mention) along with start and end offsets of that mention in the target document. The output is either a NE entry id in a reference Knowledge Base (KB) or a Not-in-KB (NIL) id in the case that system could not find any appropriate entry for that query. At the end, we have analyzed our result in different aspects. To disambiguate query name we apply a graph-based semantic similarity approach to extract the network of the semantic knowledge existing in the content of target document.Este documento es un resumen del trabajo realizado para la construccion de un sistema de Entity Linking (EL) destinado a desambiguar menciones de Entidades Nombradas (Named Entities, NE) que aparecen en un documento de referencia. La tarea de EL presenta una gran dificultad ya que cada entidad puede ser mencionada de varias maneras (sinonimia). Ademas cada mencion puede referirse a mas de una entidad (polisemia). Asi pues, se debe realizar un gran esfuerzo para hacer frente a estos retos. Nuestro sistema de EL lleva a cabo la desambiguacion de las menciones de NE en varias etapas. Para cada etapa hemos propuesto, implementado y evaluado varias aproximaciones. Hemos evaluado nuestro sistema de EL en el marco del TAC-KBP English EL evaluation framework. En este marco la evaluacion se realiza a partir de una entrada que consiste en un conjunto de consultas cada una de las cuales consta de un nombre (query name) que corresponde a una mencion objetivo cuya posicion en un documento de referencia se indica. La salida debe indicar a que entidad en una base de conocimiento (Knowledge Base, KB) corresponde la mencion. En caso de no existir un referente apropiado la respuesta sera Not-in-KB (NIL). La tesis concluye con un analisis pormenorizado de los resultados obtenidos en la evaluacion.Postprint (published version

Online Spectral Clustering on Network Streams

Graph is an extremely useful representation of a wide variety of practical systems in data analysis. Recently, with the fast accumulation of stream data from various type of networks, significant research interests have arisen on spectral clustering for network streams (or evolving networks). Compared with the general spectral clustering problem, the data analysis of this new type of problems may have additional requirements, such as short processing time, scalability in distributed computing environments, and temporal variation tracking. However, to design a spectral clustering method to satisfy these requirements certainly presents non-trivial efforts. There are three major challenges for the new algorithm design. The first challenge is online clustering computation. Most of the existing spectral methods on evolving networks are off-line methods, using standard eigensystem solvers such as the Lanczos method. It needs to recompute solutions from scratch at each time point. The second challenge is the parallelization of algorithms. To parallelize such algorithms is non-trivial since standard eigen solvers are iterative algorithms and the number of iterations can not be predetermined. The third challenge is the very limited existing work. In addition, there exists multiple limitations in the existing method, such as computational inefficiency on large similarity changes, the lack of sound theoretical basis, and the lack of effective way to handle accumulated approximate errors and large data variations over time. In this thesis, we proposed a new online spectral graph clustering approach with a family of three novel spectrum approximation algorithms. Our algorithms incrementally update the eigenpairs in an online manner to improve the computational performance. Our approaches outperformed the existing method in computational efficiency and scalability while retaining competitive or even better clustering accuracy. We derived our spectrum approximation techniques GEPT and EEPT through formal theoretical analysis. The well established matrix perturbation theory forms a solid theoretic foundation for our online clustering method. We facilitated our clustering method with a new metric to track accumulated approximation errors and measure the short-term temporal variation. The metric not only provides a balance between computational efficiency and clustering accuracy, but also offers a useful tool to adapt the online algorithm to the condition of unexpected drastic noise. In addition, we discussed our preliminary work on approximate graph mining with evolutionary process, non-stationary Bayesian Network structure learning from non-stationary time series data, and Bayesian Network structure learning with text priors imposed by non-parametric hierarchical topic modeling