Ghent University-IBCN participation in TAC-KBP 2014 slot filling and cold start tasks

C

Low-rank regularization for high-dimensional sparse conjunctive feature spaces in information extraction

One of the challenges in Natural Language Processing (NLP) is the unstructured nature of texts, in which useful information is not easily identifiable. Information Extraction (IE) aims to alleviate it by enabling automatic extraction of structured information from such text sources. The resulting structured information will facilitate easier querying, organizing, and analyzing of data from texts. In this thesis, we are interested in two IE related tasks: (i) named entity classification and (ii) template filling. Specifically, this thesis examines the problem of learning classifiers of text spans and explore its application for extracting named entities and template slot-fillers. In general, our goal is to construct a method to learn classifiers that: (i) require less supervision, (ii) work well with high-dimensional sparse feature spaces and (iii) are able to classify unseen items (i.e. named entities/slot-fillers not observed in training data). The key idea of our contribution is the utilization of unseen conjunctive features. A conjunctive feature is a combination of features from different feature sets. For example, to classify a phrase, one might have one feature set for the context and another set for the phrase itself. When learning a classifier, only a factor of these conjunctive features will be observed in the training set, leaving the rest (i.e. unseen features) unusable for predicting items in test time. We hypothesize that utilizing such unseen conjunctions is useful to address all of the aspects of the goal. We develop a general regularization framework specifically designed for sparse conjunctive feature spaces. Our strategy is based on employing tensors to represent the conjunctive feature space, and forcing the model to induce low-dimensional embeddings of the feature vectors via low-rank regularization on the tensor parameters. Such compressed representation will help prediction by generalizing to novel examples where most of the conjunctions will be unseen in the training set. We conduct experiments on learning named entity classifiers and template filling, focusing on extracting unseen items. We show that when learning classifiers under minimal supervision, our approach is more effective in controlling model capacity than standard techniques for linear classification.Uno de los retos en Procesamiento del Lenguaje Natural (NLP, del inglés Natural Language Processing) es la naturaleza no estructurada del texto, que hace que la información útil y relevante no sea fácilmente identificable. Los métodos de Extracción de Información (IE, del inglés Information Extraction) afrontan este problema mediante la extracción automática de información estructurada de dichos textos. La estructura resultante facilita la búsqueda, la organización y el análisis datos textuales. Esta tesis se centra en dos tareas relacionadas dentro de IE: (i) clasificación de entidades nombradas (NEC, del inglés Named Entity Classification), y (ii) rellenado de plantillas (en inglés, template filling). Concretamente, esta tesis estudia el problema de aprender clasificadores de secuencias textuales y explora su aplicación a la extracción de entidades nombradas y de valores para campos de plantillas. El objetivo general es desarrollar un método para aprender clasificadores que: (i) requieran poca supervisión; (ii) funcionen bien en espacios de características de alta dimensión y dispersión; y (iii) sean capaces de clasificar elementos nunca vistos (por ejemplo entidades o valores de campos que no hayan sido vistos en fase de entrenamiento). La idea principal de nuestra contribución es la utilización de características conjuntivas que no aparecen en el conjunto de entrenamiento. Una característica conjuntiva es una conjunción de características elementales. Por ejemplo, para clasificar la mención de una entidad en una oración, se utilizan características de la mención, del contexto de ésta, y a su vez conjunciones de los dos grupos de características. Cuando se aprende un clasificador en un conjunto de entrenamiento concreto, sólo se observará una fracción de estas características conjuntivas, dejando el resto (es decir, características no vistas) sin ser utilizado para predecir elementos en fase de evaluación y explotación del modelo. Nuestra hipótesis es que la utilización de estas conjunciones nunca vistas pueden ser potencialmente muy útiles, especialmente para reconocer entidades nuevas. Desarrollamos un marco de regularización general específicamente diseñado para espacios de características conjuntivas dispersas. Nuestra estrategia se basa en utilizar tensores para representar el espacio de características conjuntivas y obligar al modelo a inducir "embeddings" de baja dimensión de los vectores de características vía regularización de bajo rango en los parámetros de tensor. Dicha representación comprimida ayudará a la predicción, generalizando a nuevos ejemplos donde la mayoría de las conjunciones no han sido vistas durante la fase de entrenamiento. Presentamos experimentos sobre el aprendizaje de clasificadores de entidades nombradas, y clasificadores de valores en campos de plantillas, centrándonos en la extracción de elementos no vistos. Demostramos que al aprender los clasificadores bajo mínima supervisión, nuestro enfoque es más efectivo en el control de la capacidad del modelo que las técnicas estándar para la clasificación linea

Low-rank regularization for high-dimensional sparse conjunctive feature spaces in information extraction

Versió amb dues seccions retallades, per drets de l'editorOne of the challenges in Natural Language Processing (NLP) is the unstructured nature of texts, in which useful information is not easily identifiable. Information Extraction (IE) aims to alleviate it by enabling automatic extraction of structured information from such text sources. The resulting structured information will facilitate easier querying, organizing, and analyzing of data from texts. In this thesis, we are interested in two IE related tasks: (i) named entity classification and (ii) template filling. Specifically, this thesis examines the problem of learning classifiers of text spans and explore its application for extracting named entities and template slot-fillers. In general, our goal is to construct a method to learn classifiers that: (i) require less supervision, (ii) work well with high-dimensional sparse feature spaces and (iii) are able to classify unseen items (i.e. named entities/slot-fillers not observed in training data). The key idea of our contribution is the utilization of unseen conjunctive features. A conjunctive feature is a combination of features from different feature sets. For example, to classify a phrase, one might have one feature set for the context and another set for the phrase itself. When learning a classifier, only a factor of these conjunctive features will be observed in the training set, leaving the rest (i.e. unseen features) unusable for predicting items in test time. We hypothesize that utilizing such unseen conjunctions is useful to address all of the aspects of the goal. We develop a general regularization framework specifically designed for sparse conjunctive feature spaces. Our strategy is based on employing tensors to represent the conjunctive feature space, and forcing the model to induce low-dimensional embeddings of the feature vectors via low-rank regularization on the tensor parameters. Such compressed representation will help prediction by generalizing to novel examples where most of the conjunctions will be unseen in the training set. We conduct experiments on learning named entity classifiers and template filling, focusing on extracting unseen items. We show that when learning classifiers under minimal supervision, our approach is more effective in controlling model capacity than standard techniques for linear classification.Uno de los retos en Procesamiento del Lenguaje Natural (NLP, del inglés Natural Language Processing) es la naturaleza no estructurada del texto, que hace que la información útil y relevante no sea fácilmente identificable. Los métodos de Extracción de Información (IE, del inglés Information Extraction) afrontan este problema mediante la extracción automática de información estructurada de dichos textos. La estructura resultante facilita la búsqueda, la organización y el análisis datos textuales. Esta tesis se centra en dos tareas relacionadas dentro de IE: (i) clasificación de entidades nombradas (NEC, del inglés Named Entity Classification), y (ii) rellenado de plantillas (en inglés, template filling). Concretamente, esta tesis estudia el problema de aprender clasificadores de secuencias textuales y explora su aplicación a la extracción de entidades nombradas y de valores para campos de plantillas. El objetivo general es desarrollar un método para aprender clasificadores que: (i) requieran poca supervisión; (ii) funcionen bien en espacios de características de alta dimensión y dispersión; y (iii) sean capaces de clasificar elementos nunca vistos (por ejemplo entidades o valores de campos que no hayan sido vistos en fase de entrenamiento). La idea principal de nuestra contribución es la utilización de características conjuntivas que no aparecen en el conjunto de entrenamiento. Una característica conjuntiva es una conjunción de características elementales. Por ejemplo, para clasificar la mención de una entidad en una oración, se utilizan características de la mención, del contexto de ésta, y a su vez conjunciones de los dos grupos de características. Cuando se aprende un clasificador en un conjunto de entrenamiento concreto, sólo se observará una fracción de estas características conjuntivas, dejando el resto (es decir, características no vistas) sin ser utilizado para predecir elementos en fase de evaluación y explotación del modelo. Nuestra hipótesis es que la utilización de estas conjunciones nunca vistas pueden ser potencialmente muy útiles, especialmente para reconocer entidades nuevas. Desarrollamos un marco de regularización general específicamente diseñado para espacios de características conjuntivas dispersas. Nuestra estrategia se basa en utilizar tensores para representar el espacio de características conjuntivas y obligar al modelo a inducir "embeddings" de baja dimensión de los vectores de características vía regularización de bajo rango en los parámetros de tensor. Dicha representación comprimida ayudará a la predicción, generalizando a nuevos ejemplos donde la mayoría de las conjunciones no han sido vistas durante la fase de entrenamiento. Presentamos experimentos sobre el aprendizaje de clasificadores de entidades nombradas, y clasificadores de valores en campos de plantillas, centrándonos en la extracción de elementos no vistos. Demostramos que al aprender los clasificadores bajo mínima supervisión, nuestro enfoque es más efectivo en el control de la capacidad del modelo que las técnicas estándar para la clasificación linealPostprint (published version

Effective distant supervision for end-to-end knowledge base population systems

The growing amounts of textual data require automatic methods for structuring relevant information so that it can be further processed by computers and systematically accessed by humans. The scenario dealt with in this dissertation is known as Knowledge Base Population (KBP), where relational information about entities is retrieved from a large text collection and stored in a database, structured according to a pre-specified schema. Most of the research in this dissertation is placed in the context of the KBP benchmark of the Text Analysis Conference (TAC KBP), which provides a test-bed to examine all steps in a complex end-to-end relation extraction setting. In this dissertation a new state of the art for the TAC KBP benchmark was achieved by focussing on the following research problems: (1) The KBP task was broken down into a modular pipeline of sub-problems, and the most pressing issues were identified and quantified at all steps. (2) The quality of semi-automatically generated training data was increased by developing noise-reduction methods, decreasing the influence of false-positive training examples. (3) A focus was laid on fine-grained entity type modelling, entity expansion, entity matching and tagging, to maintain as much recall as possible on the relational argument level. (4) A new set of effective methods for generating training data, encoding features and training relational classifiers was developed and compared with previous state-of-the-art methods.Die wachsende Menge an Textdaten erfordert Methoden, relevante Informationen so zu strukturieren, dass sie von Computern weiterverarbeitet werden können, und dass Menschen systematisch auf sie zugreifen können. Das in dieser Dissertation behandelte Szenario ist unter dem Begriff Knowledge Base Population (KBP) bekannt. Hier werden relationale Informationen über Entitäten aus großen Textbeständen automatisch zusammengetragen und gemäß einem vorgegebenen Schema strukturiert. Ein Großteil der Forschung der vorliegenden Dissertation ist im Kontext des TAC KBP Vergleichstests angesiedelt. Dieser stellt ein Testumfeld dar, um alle Schritte eines anfragebasierten Relationsextraktions-Systems zu untersuchen. Die in der vorliegenden Dissertation entwickelten Verfahren setzen einen neuen Standard für TAC KBP. Dies wurde durch eine Schwerpunktsetzung auf die folgenden Forschungsfragen erreicht: Erstens wurden die wichtigsten Unterprobleme von KBP identifiziert und die jeweiligen Effekte genau quantifiziert. Zweitens wurde die Qualität von halbautomatischen Trainingsdaten durch Methoden erhöht, die den Einfluss von falsch positiven Trainingsbeispielen verringern. Drittens wurde ein Schwerpunkt auf feingliedrige Typmodellierung, die Expansion von Entitätennamen und das Auffinden von Entitäten gelegt, um eine größtmögliche Abdeckung von relationalen Argumenten zu erreichen. Viertens wurde eine Reihe von neuen leistungsstarken Methoden entwickelt und untersucht, um Trainingsdaten zu erzeugen, Klassifizierungsmerkmale zu kodieren und relationale Klassifikatoren zu trainieren

Effective distant supervision for end-to-end knowledge base population systems

The growing amounts of textual data require automatic methods for structuring relevant information so that it can be further processed by computers and systematically accessed by humans. The scenario dealt with in this dissertation is known as Knowledge Base Population (KBP), where relational information about entities is retrieved from a large text collection and stored in a database, structured according to a pre-specified schema. Most of the research in this dissertation is placed in the context of the KBP benchmark of the Text Analysis Conference (TAC KBP), which provides a test-bed to examine all steps in a complex end-to-end relation extraction setting. In this dissertation a new state of the art for the TAC KBP benchmark was achieved by focussing on the following research problems: (1) The KBP task was broken down into a modular pipeline of sub-problems, and the most pressing issues were identified and quantified at all steps. (2) The quality of semi-automatically generated training data was increased by developing noise-reduction methods, decreasing the influence of false-positive training examples. (3) A focus was laid on fine-grained entity type modelling, entity expansion, entity matching and tagging, to maintain as much recall as possible on the relational argument level. (4) A new set of effective methods for generating training data, encoding features and training relational classifiers was developed and compared with previous state-of-the-art methods.Die wachsende Menge an Textdaten erfordert Methoden, relevante Informationen so zu strukturieren, dass sie von Computern weiterverarbeitet werden können, und dass Menschen systematisch auf sie zugreifen können. Das in dieser Dissertation behandelte Szenario ist unter dem Begriff Knowledge Base Population (KBP) bekannt. Hier werden relationale Informationen über Entitäten aus großen Textbeständen automatisch zusammengetragen und gemäß einem vorgegebenen Schema strukturiert. Ein Großteil der Forschung der vorliegenden Dissertation ist im Kontext des TAC KBP Vergleichstests angesiedelt. Dieser stellt ein Testumfeld dar, um alle Schritte eines anfragebasierten Relationsextraktions-Systems zu untersuchen. Die in der vorliegenden Dissertation entwickelten Verfahren setzen einen neuen Standard für TAC KBP. Dies wurde durch eine Schwerpunktsetzung auf die folgenden Forschungsfragen erreicht: Erstens wurden die wichtigsten Unterprobleme von KBP identifiziert und die jeweiligen Effekte genau quantifiziert. Zweitens wurde die Qualität von halbautomatischen Trainingsdaten durch Methoden erhöht, die den Einfluss von falsch positiven Trainingsbeispielen verringern. Drittens wurde ein Schwerpunkt auf feingliedrige Typmodellierung, die Expansion von Entitätennamen und das Auffinden von Entitäten gelegt, um eine größtmögliche Abdeckung von relationalen Argumenten zu erreichen. Viertens wurde eine Reihe von neuen leistungsstarken Methoden entwickelt und untersucht, um Trainingsdaten zu erzeugen, Klassifizierungsmerkmale zu kodieren und relationale Klassifikatoren zu trainieren

Amélioration de la précision de systèmes d'extraction de relations en utilisant un filtre générique basé sur l'apprentissage statistique

RÉSUMÉ L’extraction de relations contribue à l’amélioration de la recherche sémantique, recherche basée sur la compréhension du sens des termes de recherche. Puisque la recherche d’information est principalement axée sur des mots-clés, l’extraction de relations offre un éventail de possibilités en identifiant les liens entre les entités. L’extraction de relations permet entre autres de transformer de l’information non structurée en information structurée. Les bases de connaissances,telles que Google Knowledge Graph et DBpedia, permettent un accès plus précis et plus direct à l’information. Le slot filling, qui consiste à peupler une base de connaissances à partir de textes, a été une tâche très active depuis quelques années faisant l’objet de plusieurs campagnes évaluant la capacité d’extraire automatiquement des relations prédéfinies d’un corpus de documents. Malgré quelques progrès, les résultats de ces compétitions demeurent modestes. Nous nous concentrons sur la tâche de slot filling dans le cadre de la campagne d’évaluation TAC KBP 2013. Cette tâche vise l’extraction de 41 relations prédéfinies basées sur les infobox de Wikipédia (par exemple: title, date of birth, countries of residence, etc.)liées à des entités nommées spécifiques (personnes et organisations). Une entité nommée (l’entité requête) et une relation sont soumises à un système (extracteur de relations) qui doit automatiquement trouver, parmi un corpus de plus de deux millions de documents, toute entité liée à l’entité requête par la relation donnée. Le système doit également retourner un segment textuel justifiant cette relation. Ce mémoire présente un filtre basé sur l’apprentissage statistique dont l’objectif principal est d’améliorer la précision d’extracteurs de relations tout en minimisant l’impact sur le rappel. Notre approche consiste à filtrer la sortie des extracteurs de relations en utilisant un classifieur. Notre filtre est annexé à la sortie de l’extracteur de relations, pouvant ainsi être facilement testé sur n’importe quel système. Notre classifieur est basé sur un large éventail de caractéristiques (features), incluant des caractéristiques statistiques, lexicales, morphosyntaxiques, syntaxiques et sémantiques extraites en majorité des phrases justificatives soumises par les systèmes. Nous proposons également une méthode efficace permettant d’extraire les patrons les plus fréquents (ex.: catégories orphosyntaxiques, dépendances syntaxiques) afin d’en dériver des caractéristiques booléennes utiles pour notre tâche de filtrage. Les caractéristiques utilisées pour l’entraînement des classifieurs sont soit génériques. Ainsi, notre méthode peut être utilisée pour la classification de toute relation prédéfinie. Nous avons testé le filtre sur 14 systèmes ayant participé à la tâche de slot filling. Le filtre permet d’améliorer la précision pour chacun de ces systèmes. Nos résultats démontrent également que le filtre permet d’améliorer la précision du meilleur système de plus de 20% (points de pourcentage) et d’améliorer le F-score pour 20 relations.----------ABSTRACT Relation extraction is becoming a very important challenge for enhanced semantic search. In fact, while traditional information retrieval is mainly focused on keywords, relation extraction opens a whole range of possibilities by identifying the links between concepts and entities. Unstructured data can be transformed into structured data by using effective relation extraction to populate a knowledge base (ex: Google Knowlegde Graph and DBpedia). Slot filling, which mainly consists in the population of a knowledge base, has been a very active task in recent years and has been subject to several evaluation campaigns that assess the ability of automatically extracting previously known relations from corpora. Despite some progress, the results of these competitions remain limited. In this thesis, we focus on the English slot filling track within TAC KBP 2013 evaluation campaign. This track targets the extraction of 41 pre-identified Wikipedia infobox relations (e.g. title, date of birth, countries of residence, etc.) related to specific named entities (persons and organizations). A named entity and a relation are submitted to a system (relation extractor), which must automatically find, within a corpus containing over 2 million documents, every other entity that is linked to the query entity with this particular relation, and must return a textual segment that justifies this result. This thesis presents a machine learning filter whose main objective is to enhance the precision of relation extractors while minimizing the impact on recall. Our approach consists in the filtering of relation extractors’ output using a binary classifier. Our filter is appended to the end of the relation extractor’s pipeline, thus allowing the filter to be tested and operated on any system. Another objective of this research is the identification of the most important features for the filtering step. Our classifier is based on a wide array of features including statistical, lexical, morphosyntactic, syntactic and semantic features. We also present a method for extracting the most frequent patterns (ex: part-of-speech, syntactic dependencies) between the query and the answer within the justification sentence from which we create boolean features indicating the presence of such patterns. The features used for training our classifiers are mostly generic and could be utilized to classify any pre-defined relation. We experimented the classifier on 14 systems participating in the English slot filling track of TAC KBP 2013 campaign. The filter allowed an increase in precision for every tested system. Our results also show that the classifier is able to improve the precision of the best system by more than 20% (in percentage points) and improve the F1-score for 20 relations

Protocoles d'évaluation pour l'extraction d'information libre

On voudrait apprendre à "lire automatiquement". L'extraction d'information consiste à transformer des paragraphes de texte écrits en langue naturelle en une liste d'éléments d'information autosuffisants, de façon à pouvoir comparer et colliger l'information extraite de plusieurs sources. Les éléments d'information sont ici représentés comme des relations entre entités : (Athéna ; est la fille de ; Zeus). L'extraction d'information libre (EIL) est un paradigme récent, visant à extraire un grand nombre de relations contenues dans le texte analysé, découvertes au fur et à mesure, par opposition à un nombre restreint de relations prédéterminées comme il est plus courant. Cette thèse porte sur l'évaluation des méthodes d'EIL. Dans les deux premiers chapitres, on évalue automatiquement les extractions d'un système d'EIL, en les comparant à des références écrites à la main, mettant respectivement l'accent sur l'informativité de l'extraction, puis sur son exhaustivité. Dans les deux chapitres suivants, on étudie et propose des alternatives à la fonction de confiance, qui juge des productions d'un système. En particulier, on y analyse et remet en question les méthodologies suivant lesquelles cette fonction est évaluée : d'abord comme modèle de validation de requêtes, puis en comparaison du cadre bien établi de la complétion de bases de connaissances.Information extraction consists in the processing of natural language documents into a list of self-sufficient informational elements, which allows for cross collection into Knowledge Bases, and automatic processing. The facts that result from this process are in the form of relationships between entities : (Athena ; is the daughter of ; Zeus). Open Information Extraction (OIE) is a recent paradigm the purpose of which is to extract an order of magnitude more relations from the input corpus than classical IE methods, what is achieved by encoding or learning more general patterns, in a less supervised fashion. In this thesis, I study and propose new evaluation protocols for the task of Open Information Extraction, with links to that of Knowledge Base Completion. In the first two chapters, I propose to automatically score the output of an OIE system, against a manually established reference, with particular attention paid to the informativity and exhaustivity of the extractions. I then turn my focus to the confidence function that qualifies all extracted elements, to evaluate it in a variety of settings, and propose alternative models

Deep learning methods for knowledge base population

Knowledge bases store structured information about entities or concepts of the world and can be used in various applications, such as information retrieval or question answering. A major drawback of existing knowledge bases is their incompleteness. In this thesis, we explore deep learning methods for automatically populating them from text, addressing the following tasks: slot filling, uncertainty detection and type-aware relation extraction. Slot filling aims at extracting information about entities from a large text corpus. The Text Analysis Conference yearly provides new evaluation data in the context of an international shared task. We develop a modular system to address this challenge. It was one of the top-ranked systems in the shared task evaluations in 2015. For its slot filler classification module, we propose contextCNN, a convolutional neural network based on context splitting. It improves the performance of the slot filling system by 5.0% micro and 2.9% macro F1. To train our binary and multiclass classification models, we create a dataset using distant supervision and reduce the number of noisy labels with a self-training strategy. For model optimization and evaluation, we automatically extract a labeled benchmark for slot filler classification from the manual shared task assessments from 2012-2014. We show that results on this benchmark are correlated with slot filling pipeline results with a Pearson's correlation coefficient of 0.89 (0.82) on data from 2013 (2014). The combination of patterns, support vector machines and contextCNN achieves the best results on the benchmark with a micro (macro) F1 of 51% (53%) on test. Finally, we analyze the results of the slot filling pipeline and the impact of its components. For knowledge base population, it is essential to assess the factuality of the statements extracted from text. From the sentence "Obama was rumored to be born in Kenya", a system should not conclude that Kenya is the place of birth of Obama. Therefore, we address uncertainty detection in the second part of this thesis. We investigate attention-based models and make a first attempt to systematize the attention design space. Moreover, we propose novel attention variants: External attention, which incorporates an external knowledge source, k-max average attention, which only considers the vectors with the k maximum attention weights, and sequence-preserving attention, which allows to maintain order information. Our convolutional neural network with external k-max average attention sets the new state of the art on a Wikipedia benchmark dataset with an F1 score of 68%. To the best of our knowledge, we are the first to integrate an uncertainty detection component into a slot filling pipeline. It improves precision by 1.4% and micro F1 by 0.4%. In the last part of the thesis, we investigate type-aware relation extraction with neural networks. We compare different models for joint entity and relation classification: pipeline models, jointly trained models and globally normalized models based on structured prediction. First, we show that using entity class prediction scores instead of binary decisions helps relation classification. Second, joint training clearly outperforms pipeline models on a large-scale distantly supervised dataset with fine-grained entity classes. It improves the area under the precision-recall curve from 0.53 to 0.66. Third, we propose a model with a structured prediction output layer, which globally normalizes the score of a triple consisting of the classes of two entities and the relation between them. It improves relation extraction results by 4.4% F1 on a manually labeled benchmark dataset. Our analysis shows that the model learns correct correlations between entity and relation classes. Finally, we are the first to use neural networks for joint entity and relation classification in a slot filling pipeline. The jointly trained model achieves the best micro F1 score with a score of 22% while the neural structured prediction model performs best in terms of macro F1 with a score of 25%

Slot Filling

Slot filling (SF) is the task of automatically extracting facts about particular entities from unstructured text, and populating a knowledge base (KB) with these facts. These structured KBs enable applications such as structured web queries and question answering. SF is typically framed as a query-oriented setting of the related task of relation extraction. Throughout this thesis, we reflect on how SF is a task with many distinct problems. We demonstrate that recall is a major limiter on SF system performance. We contribute an analysis of typical SF recall loss, and find a substantial amount of loss occurs early in the SF pipeline. We confirm that accurate NER and coreference resolution are required for high-recall SF. We measure upper bounds using a naïve graph-based semi-supervised bootstrapping technique, and find that only 39% of results are reachable using a typical feature space. We expect that this graph-based technique will be directly useful for extraction, and this leads us to frame SF as a label propagation task. We focus on a detailed graph representation of the task which reflects the behaviour and assumptions we want to model based on our analysis, including modifying the label propagation process to model multiple types of label interaction. Analysing the graph, we find that a large number of errors occur in very close proximity to training data, and identify that this is of major concern for propagation. While there are some conflicts caused by a lack of sufficient disambiguating context—we explore adding additional contextual features to address this—many of these conflicts are caused by subtle annotation problems. We find that lack of a standard for how explicit expressions of relations must be in text makes consistent annotation difficult. Using a strict definition of explicitness results in 20% of correct annotations being removed from a standard dataset. We contribute several annotation-driven analyses of this problem, exploring the definition of slots and the effect of the lack of a concrete definition of explicitness: annotation schema do not detail how explicit expressions of relations need to be, and there is large scope for disagreement between annotators. Additionally, applications may require relatively strict or relaxed evidence for extractions, but this is not considered in annotation tasks. We demonstrate that annotators frequently disagree on instances, dependent on differences in annotator world knowledge and thresholds on making probabilistic inference. SF is fundamental to enabling many knowledge-based applications, and this work motivates modelling and evaluating SF to better target these tasks