MergedTrie: Efficient textual indexing

The accessing and processing of textual information (i.e. the storing and querying of a set of strings) is especially important for many current applications (e.g. information retrieval and social networks), especially when working in the fields of Big Data or IoT, which require the handling of very large string dictionaries. Typical data structures for textual indexing are Hash Tables and some variants of Tries such as the Double Trie (DT). In this paper, we propose an extension of the DT that we have called MergedTrie. It improves the DT compression by merging both Tries into a single and by segmenting the indexed term into two fixed length parts in order to balance the new Trie. Thus, a higher overlapping of both prefixes and suffixes is obtained. Moreover, we propose a new implementation of Tries that achieves better compression rates than the Double-Array representation usually chosen for implementing Tries. Our proposal also overcomes the limitation of static implementations that does not allow insertions and updates in their compact representations. Finally, our MergedTrie implementation experimentally improves the efficiency of the Hash Tables, the DTs, the Double-Array, the Crit-bit, the Directed Acyclic Word Graphs (DAWG), and the Acyclic Deterministic Finite Automata (ADFA) data structures, requiring less space than the original text to be indexed.This study has been partially funded by the SEQUOIA-UA (TIN2015-63502-C3-3-R) and the RESCATA (TIN2015-65100-R) projects of the Spanish Ministry of Economy and Competitiveness (MINECO)

Reasoning-Driven Question-Answering For Natural Language Understanding

Natural language understanding (NLU) of text is a fundamental challenge in AI, and it has received significant attention throughout the history of NLP research. This primary goal has been studied under different tasks, such as Question Answering (QA) and Textual Entailment (TE). In this thesis, we investigate the NLU problem through the QA task and focus on the aspects that make it a challenge for the current state-of-the-art technology. This thesis is organized into three main parts: In the first part, we explore multiple formalisms to improve existing machine comprehension systems. We propose a formulation for abductive reasoning in natural language and show its effectiveness, especially in domains with limited training data. Additionally, to help reasoning systems cope with irrelevant or redundant information, we create a supervised approach to learn and detect the essential terms in questions. In the second part, we propose two new challenge datasets. In particular, we create two datasets of natural language questions where (i) the first one requires reasoning over multiple sentences; (ii) the second one requires temporal common sense reasoning. We hope that the two proposed datasets will motivate the field to address more complex problems. In the final part, we present the first formal framework for multi-step reasoning algorithms, in the presence of a few important properties of language use, such as incompleteness, ambiguity, etc. We apply this framework to prove fundamental limitations for reasoning algorithms. These theoretical results provide extra intuition into the existing empirical evidence in the field

Populating knowledge bases with temporal information

Recent progress in information extraction has enabled the automatic construction of large knowledge bases. Knowledge bases contain millions of entities (e.g. persons, organizations, events, etc.), their semantic classes, and facts about them. Knowledge bases have become a great asset for semantic search, entity linking, deep analytics, and question answering. However, a common limitation of current knowledge bases is the poor coverage of temporal knowledge. First of all, so far, knowledge bases have focused on popular events and ignored long tail events such as political scandals, local festivals, or protests. Secondly, they do not cover the textual phrases denoting events and temporal facts at all. The goal of this dissertation, thus, is to automatically populate knowledge bases with this kind of temporal knowledge. The dissertation makes the following contributions to address the afore mentioned limitations. The first contribution is a method for extracting events from news articles. The method reconciles the extracted events into canonicalized representations and organizes them into fine-grained semantic classes. The second contribution is a method for mining the textual phrases denoting the events and facts. The method infers the temporal scopes of these phrases and maps them to a knowledge base. Our experimental evaluations demonstrate that our methods yield high quality output compared to state-of- the-art approaches, and can indeed populate knowledge bases with temporal knowledge.Der Fortschritt in der Informationsextraktion ermöglicht heute das automatischen Erstellen von Wissensbasen. Derartige Wissensbasen enthalten Entitäten wie Personen, Organisationen oder Events sowie Informationen über diese und deren semantische Klasse. Automatisch generierte Wissensbasen bilden eine wesentliche Grundlage für das semantische Suchen, das Verknüpfen von Entitäten, die Textanalyse und für natürlichsprachliche Frage-Antwortsysteme. Eine Schwäche aktueller Wissensbasen ist jedoch die unzureichende Erfassung von temporalen Informationen. Wissenbasen fokussieren in erster Linie auf populäre Events und ignorieren weniger bekannnte Events wie z.B. politische Skandale, lokale Veranstaltungen oder Demonstrationen. Zudem werden Textphrasen zur Bezeichung von Events und temporalen Fakten nicht erfasst. Ziel der vorliegenden Arbeit ist es, Methoden zu entwickeln, die temporales Wissen au- tomatisch in Wissensbasen integrieren. Dazu leistet die Dissertation folgende Beiträge: 1. Die Entwicklung einer Methode zur Extrahierung von Events aus Nachrichtenartikeln sowie deren Darstellung in einer kanonischen Form und ihrer Einordnung in detaillierte semantische Klassen. 2. Die Entwicklung einer Methode zur Gewinnung von Textphrasen, die Events und Fakten in Wissensbasen bezeichnen sowie einer Methode zur Ableitung ihres zeitlichen Verlaufs und ihrer Dauer. Unsere Experimente belegen, dass die von uns entwickelten Methoden zu qualitativ deutlich besseren Ausgabewerten führen als bisherige Verfahren und Wissensbasen tatsächlich um temporales Wissen erweitern können