Hierarchical Quantized Representations for Script Generation

Scripts define knowledge about how everyday scenarios (such as going to a restaurant) are expected to unfold. One of the challenges to learning scripts is the hierarchical nature of the knowledge. For example, a suspect arrested might plead innocent or guilty, and a very different track of events is then expected to happen. To capture this type of information, we propose an autoencoder model with a latent space defined by a hierarchy of categorical variables. We utilize a recently proposed vector quantization based approach, which allows continuous embeddings to be associated with each latent variable value. This permits the decoder to softly decide what portions of the latent hierarchy to condition on by attending over the value embeddings for a given setting. Our model effectively encodes and generates scripts, outperforming a recent language modeling-based method on several standard tasks, and allowing the autoencoder model to achieve substantially lower perplexity scores compared to the previous language modeling-based method.Comment: EMNLP 201

Event Representations with Tensor-based Compositions

Robust and flexible event representations are important to many core areas in language understanding. Scripts were proposed early on as a way of representing sequences of events for such understanding, and has recently attracted renewed attention. However, obtaining effective representations for modeling script-like event sequences is challenging. It requires representations that can capture event-level and scenario-level semantics. We propose a new tensor-based composition method for creating event representations. The method captures more subtle semantic interactions between an event and its entities and yields representations that are effective at multiple event-related tasks. With the continuous representations, we also devise a simple schema generation method which produces better schemas compared to a prior discrete representation based method. Our analysis shows that the tensors capture distinct usages of a predicate even when there are only subtle differences in their surface realizations.Comment: Accepted at AAAI 201

Open Domain Event Extraction Using Neural Latent Variable Models

We consider open domain event extraction, the task of extracting unconstraint types of events from news clusters. A novel latent variable neural model is constructed, which is scalable to very large corpus. A dataset is collected and manually annotated, with task-specific evaluation metrics being designed. Results show that the proposed unsupervised model gives better performance compared to the state-of-the-art method for event schema induction.Comment: accepted by ACL 201

CaTeRS: Causal and Temporal Relation Scheme for Semantic Annotation of Event Structures

Abstract Learning commonsense causal and temporal relation between events is one of the major steps towards deeper language understanding. This is even more crucial for understanding stories and script learning. A prerequisite for learning scripts is a semantic framework which enables capturing rich event structures. In this paper we introduce a novel semantic annotation framework, called Causal and Temporal Relation Scheme (CaTeRS), which is unique in simultaneously capturing a comprehensive set of temporal and causal relations between events. By annotating a total of 1,600 sentences in the context of 320 five-sentence short stories sampled from ROCStories corpus, we demonstrate that these stories are indeed full of causal and temporal relations. Furthermore, we show that the CaTeRS annotation scheme enables high inter-annotator agreement for broad-coverage event entity annotation and moderate agreement on semantic link annotation

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

Generative event schema induction with entity disambiguation

Conference of 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, ACL-IJCNLP 2015 ; Conference Date: 26 July 2015 Through 31 July 2015; Conference Code:114195International audienceThis paper presents a generative model to event schema induction. Previous methods in the literature only use head words to represent entities. However, elements other than head words contain useful information. For instance, an armed man is more discriminative than man. Our model takes into account this information and precisely represents it using probabilistic topic distributions. We illustrate that such information plays an important role in parameter estimation. Mostly, it makes topic distributions more coherent and more discriminative. Experimental results on benchmark dataset empirically confirm this enhancement

Making Sense of Social Events by Event monitoring, Visualization and Underlying Community Profiling

With the prevalence of intelligent devices, social networks have been playing an increasingly important role in our daily life. Various social networks (e.g., Twitter, Facebook) provide convenient platforms for users to explore the world. In this thesis, we study the problem of multi-perspective analysis of social events detected from social networks. In particular, we aim to make sense of the social events from the following three perspectives: 1) what are these social events about; 2) how do these events evolve along timeline; 3) who are involved in the discussions on these events. We mainly work on two categories of social data: the user-generated contents such as tweets and Facebook posts, and the users' interactions such as the follow and reply behaviours among users. On one hand, the posts reveal valuable information that describes the evolutions of miscellaneous social events, which is crucial for people to understand the world. On the other hand, users' interactions demonstrate users' relationships among each other and thus provide opportunities for analysing the underlying communities behind the social events. However, it is not practical to manually detect social events, monitor event evolutions or profile the underlying communities from the massive amount of social data generated everyday. Hence, how to efficiently and effectively extract, manage and analyse the useful information from the social data for multi-perspective social events understanding is of great importance. The social data is dynamic source of information which enables people to stay informed of what is happening now and who are the active and influential users discussing these social events. For one thing, social data is generated by people worldwide at all time, which may make fast identification of events even before the mainstream media. Moreover, the continuous stream of social data reflects the event evolutions and characterizes the events with changing opinions at different stages. This provides an opportunity to people for timely responses to urgent events. For another, users are often not isolated in social networks. The interactions between users can be utilized to discover the communities who discuss each social event. Underlying community profiling provides answers to the questions like who are interested in these events, and which group of people are the most influential users in spreading certain event topics. These answers deepen our understanding of the social events by considering not only the events themselves but also the users behind these events. The first research task in this thesis is to monitor and index the evolving events from social textual contents. The social data cover a wide variety of events which typically evolve over time. Although event detection has been actively studied, most existing approaches do not track the evolution of events, nor do they address the issue of efficient monitoring in the presence of a large number of events. In this task, we detect events based on the user-generated textual contents and design four event operations to capture the dynamics of events. Moreover, we propose a novel event indexing structure, called Multi-layer Inverted List, to manage dynamic event databases for the acceleration of large-scale event search and update. The second research task is to explore multiple features for social events tracking and visualization. In addition to textual contents utilized in the first task, social data contains various features, such as images and timestamps. The benefits of incorporating different features into event detection are twofold. First, these features provide supplemental information that facilitates the event detection model. Second, different features describe the detected events from different aspects, which enables users to have a better understanding with more vivid visualizations. To improve the event detection performance, we propose a novel generative probabilistic model which jointly models five different features. The event evolution tracking is achieved by applying the maximum-weighted bipartite graph matching on the events discovered in consecutive periods. Events are then visualized by the representative images selected based on our three defined criteria. The third research task is to detect and profile the underlying social communities in social events. The social data not only contains user-generated contents which describe the events evolutions, but also comprises various information on the users who discuss these events, such as user attributes, user behaviours, and so on. Comprehensively utilizing this user information can help to group similar users into communities, and enrich the social event analysis from the community perspective. Motivated by the rich semantics about user behaviours hidden in social data, we extend the community definition as a group of users who are not only densely connected, but also having similar behaviours. Moreover, in addition to detecting the communities, we further profile each of the detected communities for social events analysis. A novel community profiling model is designed to detect and characterize a community by both content profile (what a community is about) and diffusion profile (how it interacts with others)