Domain-sensitive Temporal Tagging for Event-centric Information Retrieval

Temporal and geographic information is of major importance in virtually all contexts. Thus, it also occurs frequently in many types of text documents in the form of temporal and geographic expressions. Often, those are used to refer to something that was, is, or will be happening at some specific time and some specific place – in other words, temporal and geographic expressions are often used to refer to events. However, so far, event-related information needs are not well served by standard information retrieval approaches, which motivates the topic of this thesis: event-centric information retrieval. An important characteristic of temporal and geographic expressions – and thus of two components of events – is that they can be normalized so that their meaning is unambiguous and can be placed on a timeline or pinpointed on a map. In many research areas in which natural language processing is involved, e.g., in information retrieval, document summarization, and question answering, applications can highly benefit from having access to normalized information instead of only the words as they occur in documents. In this thesis, we present several frameworks for searching and exploring document collections with respect to occurring temporal, geographic, and event information. While we rely on an existing tool for extracting and normalizing geographic expressions, we study the task of temporal tagging, i.e., the extraction and normalization of temporal expressions. A crucial issue is that so far most research on temporal tagging dealt with English news-style documents. However, temporal expressions have to be handled in different ways depending on the domain of the documents from which they are extracted. Since we do not want to limit our research to one domain and one language, we develop the multilingual, cross-domain temporal tagger HeidelTime. It is the only publicly available temporal tagger for several languages and easy to extend to further languages. In addition, it achieves state-of-the-art evaluation results for all addressed domains and languages, and lays the foundations for all further contributions developed in this thesis. To achieve our goal of exploiting temporal and geographic expressions for event-centric information retrieval from a variety of text documents, we introduce the concept of spatio-temporal events and several concepts to "compute" with temporal, geographic, and event information. These concepts are used to develop a spatio-temporal ranking approach, which does not only consider textual, temporal, and geographic query parts but also two different types of proximity information. Furthermore, we adapt the spatio-temporal search idea by presenting a framework to directly search for events. Additionally, several map-based exploration frameworks are introduced that allow a new way of exploring event information latently contained in huge document collections. Finally, an event-centric document similarity model is developed that calculates document similarity on multilingual corpora solely based on extracted and normalized event information

MultiVENT: Multilingual Videos of Events with Aligned Natural Text

Everyday news coverage has shifted from traditional broadcasts towards a wide range of presentation formats such as first-hand, unedited video footage. Datasets that reflect the diverse array of multimodal, multilingual news sources available online could be used to teach models to benefit from this shift, but existing news video datasets focus on traditional news broadcasts produced for English-speaking audiences. We address this limitation by constructing MultiVENT, a dataset of multilingual, event-centric videos grounded in text documents across five target languages. MultiVENT includes both news broadcast videos and non-professional event footage, which we use to analyze the state of online news videos and how they can be leveraged to build robust, factually accurate models. Finally, we provide a model for complex, multilingual video retrieval to serve as a baseline for information retrieval using MultiVENT

Event-based Access to Historical Italian War Memoirs

The progressive digitization of historical archives provides new, often domain specific, textual resources that report on facts and events which have happened in the past; among these, memoirs are a very common type of primary source. In this paper, we present an approach for extracting information from Italian historical war memoirs and turning it into structured knowledge. This is based on the semantic notions of events, participants and roles. We evaluate quantitatively each of the key-steps of our approach and provide a graph-based representation of the extracted knowledge, which allows to move between a Close and a Distant Reading of the collection.Comment: 23 pages, 6 figure

NewsReader: Using knowledge resources in a cross-lingual reading machine to generate more knowledge from massive streams of news

Abstract In this article, we describe a system that reads news articles in four different languages and detects what happened, who is involved, where and when. This event-centric information is represented as episodic situational knowledge on individuals in an interoperable RDF format that allows for reasoning on the implications of the events. Our system covers the complete path from unstructured text to structured knowledge, for which we defined a formal model that links interpreted textual mentions of things to their representation as instances. The model forms the skeleton for interoperable interpretation across different sources and languages. The real content, however, is defined using multilingual and cross-lingual knowledge resources, both semantic and episodic. We explain how these knowledge resources are used for the processing of text and ultimately define the actual content of the episodic situational knowledge that is reported in the news. The knowledge and model in our system can be seen as an example how the Semantic Web helps NLP. However, our systems also generate massive episodic knowledge of the same type as the Semantic Web is built on. We thus envision a cycle of knowledge acquisition and NLP improvement on a massive scale. This article reports on the details of the system but also on the performance of various high-level components. We demonstrate that our system performs at state-of-the-art level for various subtasks in the four languages of the project, but that we also consider the full integration of these tasks in an overall system with the purpose of reading text. We applied our system to millions of news articles, generating billions of triples expressing formal semantic properties. This shows the capacity of the system to perform at an unprecedented scale

LEVERAGING TEXT MINING FOR THE DESIGN OF A LEGAL KNOWLEDGE MANAGEMENT SYSTEM

In today’s globalized world, companies are faced with numerous and continuously changing legal requirements. To ensure that these companies are compliant with legal regulations, law and consulting firms use open legal data published by governments worldwide. With this data pool growing rapidly, the complexity of legal research is strongly increasing. Despite this fact, only few research papers consider the application of information systems in the legal domain. Against this backdrop, we pro-pose a knowledge management (KM) system that aims at supporting legal research processes. To this end, we leverage the potentials of text mining techniques to extract valuable information from legal documents. This information is stored in a graph database, which enables us to capture the relation-ships between these documents and users of the system. These relationships and the information from the documents are then fed into a recommendation system which aims at facilitating knowledge transfer within companies. The prototypical implementation of the proposed KM system is based on 20,000 legal documents and is currently evaluated in cooperation with a Big 4 accounting company

Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples

Machine Learning has been a big success story during the AI resurgence. One particular stand out success relates to learning from a massive amount of data. In spite of early assertions of the unreasonable effectiveness of data, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully. In this paper, we discuss the indispensable role of knowledge for deeper understanding of content where (i) large amounts of training data are unavailable, (ii) the objects to be recognized are complex, (e.g., implicit entities and highly subjective content), and (iii) applications need to use complementary or related data in multiple modalities/media. What brings us to the cusp of rapid progress is our ability to (a) create relevant and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP techniques. Using diverse examples, we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data and continued incorporation of knowledge in learning techniques.Comment: Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI). arXiv admin note: substantial text overlap with arXiv:1610.0770

Answering Complex Questions by Joining Multi-Document Evidence with Quasi Knowledge Graphs

Direct answering of questions that involve multiple entities and relations is a challenge for text-based QA. This problem is most pronounced when answers can be found only by joining evidence from multiple documents. Curated knowledge graphs (KGs) may yield good answers, but are limited by their inherent incompleteness and potential staleness. This paper presents QUEST, a method that can answer complex questions directly from textual sources on-the-fly, by computing similarity joins over partial results from different documents. Our method is completely unsupervised, avoiding training-data bottlenecks and being able to cope with rapidly evolving ad hoc topics and formulation style in user questions. QUEST builds a noisy quasi KG with node and edge weights, consisting of dynamically retrieved entity names and relational phrases. It augments this graph with types and semantic alignments, and computes the best answers by an algorithm for Group Steiner Trees. We evaluate QUEST on benchmarks of complex questions, and show that it substantially outperforms state-of-the-art baselines

Dense Text Retrieval based on Pretrained Language Models: A Survey

Text retrieval is a long-standing research topic on information seeking, where a system is required to return relevant information resources to user's queries in natural language. From classic retrieval methods to learning-based ranking functions, the underlying retrieval models have been continually evolved with the ever-lasting technical innovation. To design effective retrieval models, a key point lies in how to learn the text representation and model the relevance matching. The recent success of pretrained language models (PLMs) sheds light on developing more capable text retrieval approaches by leveraging the excellent modeling capacity of PLMs. With powerful PLMs, we can effectively learn the representations of queries and texts in the latent representation space, and further construct the semantic matching function between the dense vectors for relevance modeling. Such a retrieval approach is referred to as dense retrieval, since it employs dense vectors (a.k.a., embeddings) to represent the texts. Considering the rapid progress on dense retrieval, in this survey, we systematically review the recent advances on PLM-based dense retrieval. Different from previous surveys on dense retrieval, we take a new perspective to organize the related work by four major aspects, including architecture, training, indexing and integration, and summarize the mainstream techniques for each aspect. We thoroughly survey the literature, and include 300+ related reference papers on dense retrieval. To support our survey, we create a website for providing useful resources, and release a code repertory and toolkit for implementing dense retrieval models. This survey aims to provide a comprehensive, practical reference focused on the major progress for dense text retrieval

Understanding the topics and opinions from social media content

Social media has become one indispensable part of people’s daily life, as it records and reflects people’s opinions and events of interest, as well as influences people’s perceptions. As the most commonly employed and easily accessed data format on social media, a great deal of the social media textual content is not only factual and objective, but also rich in opinionated information. Thus, besides the topics Internet users are talking about in social media textual content, it is also of great importance to understand the opinions they are expressing. In this thesis, I present my broadly applicable text mining approaches, in order to understand the topics and opinions of user-generated texts on social media, to provide insights about the thoughts of Internet users on entities, events, etc. Specifically, I develop approaches to understand the semantic differences between language-specific editions of Wikipedia, when discussing certain entities from the related topical aspects perspective and the aggregated sentiment bias perspective. Moreover, I employ effective features to detect the reputation-influential sentences for person and company entities in Wikipedia articles, which lead to the detected sentiment bias. Furthermore, I propose neural network models with different levels of attention mechanism, to detect the stances of tweets towards any given target. I also introduce an online timeline generation approach, to detect and summarise the relevant sub-topics in the tweet stream, in order to provide Internet users with some insights about the evolution of major events they are interested in