Learning semantic structures from in-domain documents

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 175-184).Semantic analysis is a core area of natural language understanding that has typically focused on predicting domain-independent representations. However, such representations are unable to fully realize the rich diversity of technical content prevalent in a variety of specialized domains. Taking the standard supervised approach to domainspecific semantic analysis requires expensive annotation effort for each new domain of interest. In this thesis, we study how multiple granularities of semantic analysis can be learned from unlabeled documents within the same domain. By exploiting in-domain regularities in the expression of text at various layers of linguistic phenomena, including lexicography, syntax, and discourse, the statistical approaches we propose induce multiple kinds of structure: relations at the phrase and sentence level, content models at the paragraph and section level, and semantic properties at the document level. Each of our models is formulated in a hierarchical Bayesian framework with the target structure captured as latent variables, allowing them to seamlessly incorporate linguistically-motivated prior and posterior constraints, as well as multiple kinds of observations. Our empirical results demonstrate that the proposed approaches can successfully extract hidden semantic structure over a variety of domains, outperforming multiple competitive baselines.by Harr Chen.Ph.D

Generative Non-Markov Models for Information Extraction

Learning from unlabeled data is a long-standing challenge in machine learning. A principled solution involves modeling the full joint distribution over inputs and the latent structure of interest, and imputing the missing data via marginalization. Unfortunately, such marginalization is expensive for most non-trivial problems, which places practical limits on the expressiveness of generative models. As a result, joint models often encode strict assumptions about the underlying process such as fixed-order Markovian assumptions and employ simple count-based features of the inputs. In contrast, conditional models, which do not directly model the observed data, are free to incorporate rich overlapping features of the input in order to predict the latent structure of interest. It would be desirable to develop expressive generative models that retain tractable inference. This is the topic of this thesis. In particular, we explore joint models which relax fixed-order Markov assumptions, and investigate the use of recurrent neural networks for automatic feature induction in the generative process. We focus on two structured prediction problems: (1) imputing labeled segmentions of input character sequences, and (2) imputing directed spanning trees relating strings in text corpora. These problems arise in many applications of practical interest, but we are primarily concerned with named-entity recognition and cross-document coreference resolution in this work. For named-entity recognition, we propose a generative model in which the observed characters originate from a latent non-Markov process over words, and where the characters are themselves produced via a non-Markov process: a recurrent neural network (RNN). We propose a sampler for the proposed model in which sequential Monte Carlo is used as a transition kernel for a Gibbs sampler. The kernel is amenable to a fast parallel implementation, and results in fast mixing in practice. For cross-document coreference resolution, we move beyond sequence modeling to consider string-to-string transduction. We stipulate a generative process for a corpus of documents in which entity names arise from copying---and optionally transforming---previous names of the same entity. Our proposed model is sensitive to both the context in which the names occur as well as their spelling. The string-to-string transformations correspond to systematic linguistic processes such as abbreviation, typos, and nicknaming, and by analogy to biology, we think of them as mutations along the edges of a phylogeny. We propose a novel block Gibbs sampler for this problem that alternates between sampling an ordering of the mentions and a spanning tree relating all mentions in the corpus

Graph-based broad-coverage semantic parsing

Many broad-coverage meaning representations can be characterized as directed graphs, where nodes represent semantic concepts and directed edges represent semantic relations among the concepts. The task of semantic parsing is to generate such a meaning representation from a sentence. It is quite natural to adopt a graph-based approach for parsing, where nodes are identified conditioning on the individual words, and edges are labeled conditioning on the pairs of nodes. However, there are two issues with applying this simple and interpretable graph-based approach for semantic parsing: first, the anchoring of nodes to words can be implicit and non-injective in several formalisms (Oepen et al., 2019, 2020). This means we do not know which nodes should be generated from which individual word and how many of them. Consequently, it makes a probabilistic formulation of the training objective problematical; second, graph-based parsers typically predict edge labels independent from each other. Such an independence assumption, while being sensible from an algorithmic point of view, could limit the expressiveness of statistical modeling. Consequently, it might fail to capture the true distribution of semantic graphs. In this thesis, instead of a pipeline approach to obtain the anchoring, we propose to model the implicit anchoring as a latent variable in a probabilistic model. We induce such a latent variable jointly with the graph-based parser in an end-to-end differentiable training. In particular, we test our method on Abstract Meaning Representation (AMR) parsing (Banarescu et al., 2013). AMR represents sentence meaning with a directed acyclic graph, where the anchoring of nodes to words is implicit and could be many-to-one. Initially, we propose a rule-based system that circumvents the many-to-one anchoring by combing nodes in some pre-specified subgraphs in AMR and treats the alignment as a latent variable. Next, we remove the need for such a rule-based system by treating both graph segmentation and alignment as latent variables. Still, our graph-based parsers are parameterized by neural modules that require gradient-based optimization. Consequently, training graph-based parsers with our discrete latent variables can be challenging. By combing deep variational inference and differentiable sampling, our models can be trained end-to-end. To overcome the limitation of graph-based parsing and capture interdependency in the output, we further adopt iterative refinement. Starting with an output whose parts are independently predicted, we iteratively refine it conditioning on the previous prediction. We test this method on semantic role labeling (Gildea and Jurafsky, 2000). Semantic role labeling is the task of predicting the predicate-argument structure. In particular, semantic roles between the predicate and its arguments need to be labeled, and those semantic roles are interdependent. Overall, our refinement strategy results in an effective model, outperforming strong factorized baseline models

Report on shape analysis and matching and on semantic matching

In GRAVITATE, two disparate specialities will come together in one working platform for the archaeologist: the fields of shape analysis, and of metadata search. These fields are relatively disjoint at the moment, and the research and development challenge of GRAVITATE is precisely to merge them for our chosen tasks. As shown in chapter 7 the small amount of literature that already attempts join 3D geometry and semantics is not related to the cultural heritage domain. Therefore, after the project is done, there should be a clear ‘before-GRAVITATE’ and ‘after-GRAVITATE’ split in how these two aspects of a cultural heritage artefact are treated.This state of the art report (SOTA) is ‘before-GRAVITATE’. Shape analysis and metadata description are described separately, as currently in the literature and we end the report with common recommendations in chapter 8 on possible or plausible cross-connections that suggest themselves. These considerations will be refined for the Roadmap for Research deliverable.Within the project, a jargon is developing in which ‘geometry’ stands for the physical properties of an artefact (not only its shape, but also its colour and material) and ‘metadata’ is used as a general shorthand for the semantic description of the provenance, location, ownership, classification, use etc. of the artefact. As we proceed in the project, we will find a need to refine those broad divisions, and find intermediate classes (such as a semantic description of certain colour patterns), but for now the terminology is convenient – not least because it highlights the interesting area where both aspects meet.On the ‘geometry’ side, the GRAVITATE partners are UVA, Technion, CNR/IMATI; on the metadata side, IT Innovation, British Museum and Cyprus Institute; the latter two of course also playing the role of internal users, and representatives of the Cultural Heritage (CH) data and target user’s group. CNR/IMATI’s experience in shape analysis and similarity will be an important bridge between the two worlds for geometry and metadata. The authorship and styles of this SOTA reflect these specialisms: the first part (chapters 3 and 4) purely by the geometry partners (mostly IMATI and UVA), the second part (chapters 5 and 6) by the metadata partners, especially IT Innovation while the joint overview on 3D geometry and semantics is mainly by IT Innovation and IMATI. The common section on Perspectives was written with the contribution of all

Data mining using concepts of independence, unimodality and homophily

With the widespread use of information technologies, more and more complex data is generated and collected every day. Such complex data is various in structure, size, type and format, e.g. time series, texts, images, videos and graphs. Complex data is often high-dimensional and heterogeneous, which makes the separation of the wheat (knowledge) from the chaff (noise) more difficult. Clustering is a main mode of knowledge discovery from complex data, which groups objects in such a way that intra-group objects are more similar than inter-group objects. Traditional clustering methods such as k-means, Expectation-Maximization clustering (EM), DBSCAN and spectral clustering are either deceived by "the curse of dimensionality" or spoiled by heterogenous information. So, how to effectively explore complex data? In some cases, people may only have some partial information about the complex data. For example, in social networks, not every user provides his/her profile information such as the personal interests. Can we leverage the limited user information and friendship network wisely to infer the likely labels of the unlabeled users so that the advertisers can do accurate advertising? This is the problem of learning from labeled and unlabeled data, which is literarily attributed to semi-supervised classification. To gain insights into these problems, this thesis focuses on developing clustering and semi-supervised classification methods that are driven by the concepts of independence, unimodality and homophily. The proposed methods leverage techniques from diverse areas, such as statistics, information theory, graph theory, signal processing, optimization and machine learning. Specifically, this thesis develops four methods, i.e. FUSE, ISAAC, UNCut, and wvGN. FUSE and ISAAC are clustering techniques to discover statistically independent patterns from high-dimensional numerical data. UNCut is a clustering technique to discover unimodal clusters in attributed graphs in which not all the attributes are relevant to the graph structure. wvGN is a semi-supervised classification technique using the theory of homophily to infer the labels of the unlabeled vertices in graphs. We have verified our clustering and semi-supervised classification methods on various synthetic and real-world data sets. The results are superior to those of the state-of-the-art.Täglich werden durch den weit verbreiteten Einsatz von Informationstechnologien mehr und mehr komplexe Daten generiert und gesammelt. Diese komplexen Daten unterscheiden sich in der Struktur, Größe, Art und Format. Häufig anzutreffen sind beispielsweise Zeitreihen, Texte, Bilder, Videos und Graphen. Dabei sind diese Daten meist hochdimensional und heterogen, was die Trennung des Weizens ( Wissen ) von der Spreu ( Rauschen ) erschwert. Die Cluster Analyse ist dabei eine der wichtigsten Methoden um aus komplexen Daten wssen zu extrahieren. Dabei werden die Objekte eines Datensatzes in einer solchen Weise gruppiert, dass intra-gruppierte Objekte ähnlicher sind als Objekte anderer Gruppen. Der Einsatz von traditionellen Clustering-Methoden wie k-Means, Expectation-Maximization (EM), DBSCAN und Spektralclustering wird dabei entweder "durch der Fluch der Dimensionalität" erschwert oder ist angesichts der heterogenen Information nicht möglich. Wie erforscht man also solch komplexe Daten effektiv? Darüber hinaus ist es oft der Fall, dass für Objekte solcher Datensätze nur partiell Informationen vorliegen. So gibt in sozialen Netzwerken nicht jeder Benutzer seine Profil-Informationen wie die persönlichen Interessen frei. Können wir diese eingeschränkten Benutzerinformation trotzdem in Kombination mit dem Freundschaftsnetzwerk nutzen, um von von wenigen, einer Klasse zugeordneten Nutzern auf die anderen zu schließen. Beispielsweise um zielgerichtete Werbung zu schalten? Dieses Problem des Lernens aus klassifizierten und nicht klassifizierten Daten wird dem semi-supversised Learning zugeordnet. Um Einblicke in diese Probleme zu gewinnen, konzentriert sich diese Arbeit auf die Entwicklung von Clustering- und semi-überwachten Klassifikationsmethoden, die von den Konzepten der Unabhängigkeit, Unimodalität und Homophilie angetrieben werden. Die vorgeschlagenen Methoden nutzen Techniken aus verschiedenen Bereichen der Statistik, Informationstheorie, Graphentheorie, Signalverarbeitung, Optimierung und des maschinelles Lernen. Dabei stellt diese Arbeit vier Techniken vor: FUSE, ISAAC, UNCut, sowie wvGN. FUSE und ISAAC sind Clustering-Techniken, um statistisch unabhängige Muster aus hochdimensionalen numerischen Daten zu entdecken. UNCut ist eine Clustering-Technik, um unimodale Cluster in attributierten Graphen zu entdecken, in denen die Kanten und Attribute heterogene Informationen liefern. wvGN ist eine halbüberwachte Klassifikationstechnik, die Homophilie verwendet, um von gelabelten Kanten auf ungelabelte Kanten im Graphen zu schließen. Wir haben diese Clustering und semi-überwachten Klassifizierungsmethoden auf verschiedenen synthetischen und realen Datensätze überprüft. Die Ergebnisse sind denen von bisherigen State-of-the-Art-Methoden überlegen

Unsupervised learning of Arabic non-concatenative morphology

Unsupervised approaches to learning the morphology of a language play an important role in computer processing of language from a practical and theoretical perspective, due their minimal reliance on manually produced linguistic resources and human annotation. Such approaches have been widely researched for the problem of concatenative affixation, but less attention has been paid to the intercalated (non-concatenative) morphology exhibited by Arabic and other Semitic languages. The aim of this research is to learn the root and pattern morphology of Arabic, with accuracy comparable to manually built morphological analysis systems. The approach is kept free from human supervision or manual parameter settings, assuming only that roots and patterns intertwine to form a word. Promising results were obtained by applying a technique adapted from previous work in concatenative morphology learning, which uses machine learning to determine relatedness between words. The output, with probabilistic relatedness values between words, was then used to rank all possible roots and patterns to form a lexicon. Analysis using trilateral roots resulted in correct root identification accuracy of approximately 86% for inflected words. Although the machine learning-based approach is effective, it is conceptually complex. So an alternative, simpler and computationally efficient approach was then devised to obtain morpheme scores based on comparative counts of roots and patterns. In this approach, root and pattern scores are defined in terms of each other in a mutually recursive relationship, converging to an optimized morpheme ranking. This technique gives slightly better accuracy while being conceptually simpler and more efficient. The approach, after further enhancements, was evaluated on a version of the Quranic Arabic Corpus, attaining a final accuracy of approximately 93%. A comparative evaluation shows this to be superior to two existing, well used manually built Arabic stemmers, thus demonstrating the practical feasibility of unsupervised learning of non-concatenative morphology

Web Data Extraction, Applications and Techniques: A Survey

Web Data Extraction is an important problem that has been studied by means of different scientific tools and in a broad range of applications. Many approaches to extracting data from the Web have been designed to solve specific problems and operate in ad-hoc domains. Other approaches, instead, heavily reuse techniques and algorithms developed in the field of Information Extraction. This survey aims at providing a structured and comprehensive overview of the literature in the field of Web Data Extraction. We provided a simple classification framework in which existing Web Data Extraction applications are grouped into two main classes, namely applications at the Enterprise level and at the Social Web level. At the Enterprise level, Web Data Extraction techniques emerge as a key tool to perform data analysis in Business and Competitive Intelligence systems as well as for business process re-engineering. At the Social Web level, Web Data Extraction techniques allow to gather a large amount of structured data continuously generated and disseminated by Web 2.0, Social Media and Online Social Network users and this offers unprecedented opportunities to analyze human behavior at a very large scale. We discuss also the potential of cross-fertilization, i.e., on the possibility of re-using Web Data Extraction techniques originally designed to work in a given domain, in other domains.Comment: Knowledge-based System

Multimodal and disentangled representation learning for medical image analysis

Automated medical image analysis is a growing research field with various applications in modern healthcare. Furthermore, a multitude of imaging techniques (or modalities) have been developed, such as Magnetic Resonance (MR) and Computed Tomography (CT), to attenuate different organ characteristics. Research on image analysis is predominately driven by deep learning methods due to their demonstrated performance. In this thesis, we argue that their success and generalisation relies on learning good latent representations. We propose methods for learning spatial representations that are suitable for medical image data, and can combine information coming from different modalities. Specifically, we aim to improve cardiac MR segmentation, a challenging task due to varied images and limited expert annotations, by considering complementary information present in (potentially unaligned) images of other modalities. In order to evaluate the benefit of multimodal learning, we initially consider a synthesis task on spatially aligned multimodal brain MR images. We propose a deep network of multiple encoders and decoders, which we demonstrate outperforms existing approaches. The encoders (one per input modality) map the multimodal images into modality invariant spatial feature maps. Common and unique information is combined into a fused representation, that is robust to missing modalities, and can be decoded into synthetic images of the target modalities. Different experimental settings demonstrate the benefit of multimodal over unimodal synthesis, although input and output image pairs are required for training. The need for paired images can be overcome with the cycle consistency principle, which we use in conjunction with adversarial training to transform images from one modality (e.g. MR) to images in another (e.g. CT). This is useful especially in cardiac datasets, where different spatial and temporal resolutions make image pairing difficult, if not impossible. Segmentation can also be considered as a form of image synthesis, if one modality consists of semantic maps. We consider the task of extracting segmentation masks for cardiac MR images, and aim to overcome the challenge of limited annotations, by taking into account unannanotated images which are commonly ignored. We achieve this by defining suitable latent spaces, which represent the underlying anatomies (spatial latent variable), as well as the imaging characteristics (non-spatial latent variable). Anatomical information is required for tasks such as segmentation and regression, whereas imaging information can capture variability in intensity characteristics for example due to different scanners. We propose two models that disentangle cardiac images at different levels: the first extracts the myocardium from the surrounding information, whereas the second fully separates the anatomical from the imaging characteristics. Experimental analysis confirms the utility of disentangled representations in semi-supervised segmentation, and in regression of cardiac indices, while maintaining robustness to intensity variations such as the ones induced by different modalities. Finally, our prior research is aggregated into one framework that encodes multimodal images into disentangled anatomical and imaging factors. Several challenges of multimodal cardiac imaging, such as input misalignments and the lack of expert annotations, are successfully handled in the shared anatomy space. Furthermore, we demonstrate that this approach can be used to combine complementary anatomical information for the purpose of multimodal segmentation. This can be achieved even when no annotations are provided for one of the modalities. This thesis creates new avenues for further research in the area of multimodal and disentangled learning with spatial representations, which we believe are key to more generalised deep learning solutions in healthcare