8 research outputs found
Information extraction from multimedia web documents: an open-source platform and testbed
The LivingKnowledge project aimed to enhance the current state of the art in search, retrieval and knowledge management on the web by advancing the use of sentiment and opinion analysis within multimedia applications. To achieve this aim, a diverse set of novel and complementary analysis techniques have been integrated into a single, but extensible software platform on which such applications can be built. The platform combines state-of-the-art techniques for extracting facts, opinions and sentiment from multimedia documents, and unlike earlier platforms, it exploits both visual and textual techniques to support multimedia information retrieval. Foreseeing the usefulness of this software in the wider community, the platform has been made generally available as an open-source project. This paper describes the platform design, gives an overview of the analysis algorithms integrated into the system and describes two applications that utilise the system for multimedia information retrieval
Joint parsing of syntactic and semantic dependencies
Syntactic Dependency Parsing and Semantic Role Labeling (SRL) are two main problems in Natural Language Understanding. Both tasks are closely related and can be regarded as parsing on top of a given sequence. In the data-driven approach context, these tasks are typically addressed sequentially by a pipeline of classifiers. A syntactic parser is run in the first stage, and then given the predicates, the semantic roles are identified and classified (Gildea and Jurafsky, 2002).
An appealing and largely unexplored idea is to jointly process syntactic dependencies and semantic roles. A joint process could capture some interactions that pipeline systems are unable to model. We expect joint models to improve on syntax based on semantic cues and also the reverse. Despite this potential advantage and the interest in joint processing stimulated by the CoNLL-2008 and 2009 Shared Tasks (Surdeanu et al., 2008; Hajic et al., 2009), very few joint models have been proposed to date, few have achieved attention and fewer have obtained competitive results.
This thesis presents three contributions on this topic. The first contribution is to frame semantic role labeling as a linear assignment task. Under this framework we avoid assigning repeated roles to the arguments of a predicate. Our proposal follows previous work on enforcing constraints on the SRL analysis (Punyakanok et al., 2004; Surdeanu et al., 2007). But in our case, we enforce only a relevant subset of these constraints. We solve this problem with the efficient O(n^3) Hungarian algorithm. Our next contributions will rely on this assignment framework.
The second contribution of this thesis is a joint model that combines syntactic parsing and SRL (Lluís et al., 2013). We solve it by using dual-decomposition techniques. A strong point of our model is that it generates a joint solution relying on largely unmodified syntactic and SRL parsers. We train each component independently and the dual-decomposition method finds the optimal joint solution at decoding time. Our model has some optimality and efficiency guarantees. We show experiments comparing the pipeline and joint approaches on different test sets extracted from the CoNLL-2009 Shared Task. We observe some improvements both in syntax and semantics when our syntactic component is a first-order parser. Our results for the English language are competitive with respect to other state-of-the-art joint proposals such as Henderson et al., (2013).
The third contribution of this thesis is a model that finds semantic roles together with syntactic paths linking predicates and arguments (Lluís et al., 2014). We frame SRL as a shortest-path problem. Our method instead of conditioning over complete syntactic paths is based on the assumption that paths can be factorized. We rely on this factorization to efficiently solve our problem. The approach represents a novel way of exploiting syntactic variability in SRL. In experiments we observe improvements in the robustness of classifiers.L'anàlisi sintàctica de dependències i l'etiquetatge de rols semàntics són dues tasques principals dins el camp del Processament del Llenguatge Natural. Aquestes dues tasques estan estretament relacionades i poden considerar-se de manera genèrica com la construcció d'una anàlisi a partir d'una seqüència donada. En el context de les aproximacions basades en grans volums de dades, les dues tasques es tracten habitualment de manera seqüencial mitjançant una sèrie de classificadors en cadena. Un analitzador sintàctic s'aplica en primer lloc i a continuació i donats un predicats, els rols semàntics són identificats i classificats (Gildea i Jurafsky, 2002). Processar conjuntament les dependències sintàctiques i els rols semàntics és una idea que pot resultar atractiva però que tot i això s'ha explorat poc. Un procés conjunt podria capturar algunes interaccions que els sistemes seqüencials són incapaços de modelar. En un model conjunt esperem que la semàntica ens proporcioni pistes per tal de millorar la sintaxi així com també que es produeixin millores en el sentit contrari. Tot i aquests avantatges potencials i l'interès en els models conjunts que va despertar la tasca compartida de les "Conference on Computational Natural Language Learning" (CoNLL) 2008 i 2009 (Surdeanu et al., 2008; Hajic et al., 2009) fins al dia d'avui s'han proposat pocs models conjunts, pocs d'aquests han aconseguit tenir un ampli ressò i encara menys han presentat resultats competitius. La tesi presenta tres contribucions en aquest camp. La primera contribució és modelar l'etiquetatge de rols semàntics com un problema d'assignació lineal. Sota aquest marc evitem assignar rols repetits als arguments d'un predicat. Aquesta proposta va en la línia del treball previ sobre aplicació de restriccions en l'etiquetatge de rols semàntics (Punyakanok et al., 2004; Surdeanu et al., 2007). En el nostre cas però, apliquem només un subconjunt de les restriccions més rellevants presentades en treballs anteriors. El problema de l'assignació el resolem amb l'eficient algorisme Hongarès O(n^3). Les següents contribucions d'aquesta tesi utilitzen aquest mateix marc basat en l'assignació. La segona contribució de la tesi és un model conjunt que combina l'anàlisi sintàctica amb l'etiquetatge de rols semàntics (Lluís et al., 2013). Resolem aquest problema utilitzant el mètode anomenat "dual decomposition". Un punt destacable del nostre model és que genera la solució conjunta basant-se en analitzadors sintàctics i de rols semàntics pràcticament sense modificar. Entrenem cada component per separat i el mètode de "dual decomposition" ens permet obtenir la solució conjunta òptima durant la fase descodificació. El nostre model presenta algunes garanties d'optimalitat i eficiència. Mostrem experiments comparant les aproximacions seqüencials i conjuntes amb diferents conjunts de dades extrets de la tasca compartida del CoNLL-2009. Hem observat algunes millores tant en sintaxi com en semàntica en els casos en que el nostre component sintàctic és un analitzador de primer ordre. Els resultats que obtenim per a l'anglès són competitius respecte a altres sistemes conjunts de l'estat de l'art tals com Henderson et al. (2013). La tercera contribució de la tesi és un model que cerca rols semàntics juntament amb camins sintàctics que relacionen els predicats amb els seus arguments (Lluís et al., 2014). Considerem l'etiquetatge de rols com un problema de camins mínims. El nostre mètode enlloc de condicionar sobre camins sintàctics complets, es basa en l'assumpció que els camins poden ser factoritzats. Aquesta factorització és la que ens permet solucionar el problema de manera eficient. Aquesta aproximació representa una nova manera d'explotar variabilitat sintàctica durant l'etiquetatge de rols semàntics. En els experiments observem millores en la robustesa dels classificadors
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Learning with Joint Inference and Latent Linguistic Structure in Graphical Models
Constructing end-to-end NLP systems requires the processing of many types of linguistic information prior to solving the desired end task. A common approach to this problem is to construct a pipeline, one component for each task, with each system\u27s output becoming input for the next. This approach poses two problems. First, errors propagate, and, much like the childhood game of telephone , combining systems in this manner can lead to unintelligible outcomes. Second, each component task requires annotated training data to act as supervision for training the model. These annotations are often expensive and time-consuming to produce, may differ from each other in genre and style, and may not match the intended application.
In this dissertation we present a general framework for constructing and reasoning on joint graphical model formulations of NLP problems. Individual models are composed using weighted Boolean logic constraints, and inference is performed using belief propagation. The systems we develop are composed of two parts: one a representation of syntax, the other a desired end task (semantic role labeling, named entity recognition, or relation extraction). By modeling these problems jointly, both models are trained in a single, integrated process, with uncertainty propagated between them. This mitigates the accumulation of errors typical of pipelined approaches.
Additionally we propose a novel marginalization-based training method in which the error signal from end task annotations is used to guide the induction of a constrained latent syntactic representation. This allows training in the absence of syntactic training data, where the latent syntactic structure is instead optimized to best support the end task predictions. We find that across many NLP tasks this training method offers performance comparable to fully supervised training of each individual component, and in some instances improves upon it by learning latent structures which are more appropriate for the task
Graded Decompositional Semantic Prediction
Compared to traditional approaches, decompositional semantic labeling (DSL) is compelling but introduces complexities for data collection, quality assessment, and modeling. To shed light on these issues and lower barriers to the adoption of DSL or related approaches I bring existing models and novel variations into a shared, familiar framework, facilitating empirical investigation
Graphical Models with Structured Factors, Neural Factors, and Approximation-aware Training
This thesis broadens the space of rich yet practical models for structured prediction. We introduce a general framework for modeling with four ingredients: (1) latent variables, (2) structural constraints, (3) learned (neural) feature representations of the inputs, and (4) training that takes the approximations made during inference into account. The thesis builds up to this framework through an empirical study of three NLP tasks: semantic role labeling, relation extraction, and dependency parsing -- obtaining state-of-the-art results on the former two. We apply the resulting graphical models with structured and neural factors, and approximation-aware learning to jointly model part-of-speech tags, a syntactic dependency parse, and semantic roles in a low-resource setting where the syntax is unobserved. We present an alternative view of these models as neural networks with a topology inspired by inference on graphical models that encode our intuitions about the data
Statistical Bistratal Dependency Parsing
We present an inexact search algorithm for the problem of predicting a two-layered dependency graph. The algorithm is based on a k-best version of the standard cubictime search algorithm for projective dependency parsing, which is used as the backbone of a beam search procedure. This allows us to handle the complex nonlocal feature dependencies occurring in bistratal parsing if we model the interdependency between the two layers. We apply the algorithm to the syntactic– semantic dependency parsing task of the CoNLL-2008 Shared Task, and we obtain a competitive result equal to the highest published for a system that jointly learns syntactic and semantic structure