Extracting Temporal and Causal Relations between Events

Structured information resulting from temporal information processing is crucial for a variety of natural language processing tasks, for instance to generate timeline summarization of events from news documents, or to answer temporal/causal-related questions about some events. In this thesis we present a framework for an integrated temporal and causal relation extraction system. We first develop a robust extraction component for each type of relations, i.e. temporal order and causality. We then combine the two extraction components into an integrated relation extraction system, CATENA---CAusal and Temporal relation Extraction from NAtural language texts---, by utilizing the presumption about event precedence in causality, that causing events must happened BEFORE resulting events. Several resources and techniques to improve our relation extraction systems are also discussed, including word embeddings and training data expansion. Finally, we report our adaptation efforts of temporal information processing for languages other than English, namely Italian and Indonesian.Comment: PhD Thesi

Listening between the Lines: Learning Personal Attributes from Conversations

Open-domain dialogue agents must be able to converse about many topics while incorporating knowledge about the user into the conversation. In this work we address the acquisition of such knowledge, for personalization in downstream Web applications, by extracting personal attributes from conversations. This problem is more challenging than the established task of information extraction from scientific publications or Wikipedia articles, because dialogues often give merely implicit cues about the speaker. We propose methods for inferring personal attributes, such as profession, age or family status, from conversations using deep learning. Specifically, we propose several Hidden Attribute Models, which are neural networks leveraging attention mechanisms and embeddings. Our methods are trained on a per-predicate basis to output rankings of object values for a given subject-predicate combination (e.g., ranking the doctor and nurse professions high when speakers talk about patients, emergency rooms, etc). Experiments with various conversational texts including Reddit discussions, movie scripts and a collection of crowdsourced personal dialogues demonstrate the viability of our methods and their superior performance compared to state-of-the-art baselines.Comment: published in WWW'1

Cardinal Virtues: Extracting Relation Cardinalities from Text

Information extraction (IE) from text has largely focused on relations between individual entities, such as who has won which award. However, some facts are never fully mentioned, and no IE method has perfect recall. Thus, it is beneficial to also tap contents about the cardinalities of these relations, for example, how many awards someone has won. We introduce this novel problem of extracting cardinalities and discusses the specific challenges that set it apart from standard IE. We present a distant supervision method using conditional random fields. A preliminary evaluation results in precision between 3% and 55%, depending on the difficulty of relations.Comment: 5 pages, ACL 2017 (short paper

On the contribution of word embeddings to temporal relation classification

Temporal relation classification is a challenging task, especially when there are no explicit markers to characterise the relation between temporal entities. This occurs frequently in ntersentential relations, whose entities are not connected via direct syntactic relations making classification even more difficult. In these cases, resorting to features that focus on the semantic content of the event words may be very beneficial for inferring implicit relations. Specifically, while morpho-syntactic and context features are considered sufficient for classifying event-timex pairs, we believe that exploiting distributional semantic information about event words can benefit supervised classification of other types of pairs. In this work, we assess the impact of using word embeddings as features for event words in classifying temporal relations of event-event pairs and event-DCT (document creation time) pairs

Report on the Workshop on Personal Knowledge Graphs (PKG 2021) at AKBC 2021

The term personal knowledge graph (PKG) has been broadly used to refer to structured representation of information about a given user, primarily in the form of entities that are personally related to the user. The potential of personal knowledge graphs as a means of managing and organizing personal data, as well as a source of background knowledge for personalizing downstream services, has recently gained increasing attention from researchers in multiple fields, including that of Information Retrieval, Natural Language Processing, and the Semantic Web. The goal of the PKG’21 workshop was to create a forum for researchers and practitioners from diverse areas to present and discuss methods, tools, techniques, and experiences related to the construction and use of personal knowledge graphs, identify open questions, and create a shared research agenda. It successfully brought about a diverse workshop program, comprising an invited keynote, paper presentations, and breakout discussions, as a half-day event at the 3rd Automated Knowledge Base Construction (AKBC’21) conference. The workshop demonstrated that while the concept and research field of personal knowledge graphs is still in its early stages, there are many promising avenues of future development and research that already, and independently, have attracted the interest of several different communities.publishedVersio

CATENA: CAusal and Temporal relation Extraction from NAtural language texts

We present CATENA, a sieve-based system to perform temporal and causal relation extraction and classification from English texts, exploiting the interaction between the temporal and the causal model. We evaluate the performance of each sieve, showing that the rule-based, the machinelearned and the reasoning components all contribute to achieving state-of-the-art performance on TempEval-3 and TimeBank-Dense data. Although causal relations are much sparser than temporal ones, the architecture and the selected features are mostly suitable to serve both tasks. The effects of the interaction between the temporal and the causal components, although limited, yield promising results and confirm the tight connection between the temporal and the causal dimension of texts

HLT-FBK: a Complete Temporal Processing System for QA TempEval

The HLT-FBK system is a suite of SVMs-based classification models for extracting time expressions, events and temporal relations, each with a set of features obtained with the NewsReader NLP pipeline. HLT-FBK’s best system runs ranked 1st in all three domains, with a recall of 0.30 over all domains. Our attempts on increasing recall by considering all SRL predicates as events as well as utilizing event co-reference information in extracting temporal links result in significant improvements

FBK-HLT-time: a complete Italian Temporal Processing system for EVENTI-Evalita 2014

In this paper we present an end-to-end system for temporal processing of Italian texts based on a machine learning approach, specifically supervised classification. The system participated in all subtasks of the EVENTI task at Evalita 2014 (identification of time expressions, events, and temporal relations), including the pilot task on historical texts

Modular Isolation Units for Patients with Mild-to-Moderate Conditions in Response to Hospital Surges Resulting from the COVID-19 Pandemic

This paper presents a design proposal of an Isolation Recovery House (IRH), an adaptable modular isolation care unit specifically designed for patients with mild-to-moderate conditions as a response to an infectious disease outbreak. In particular, the study responds to the current COVID-19 pandemic, which urges the installation of isolation facilities as quickly as possible. The study offers a design solution that could expand the capacity for isolation facilities, especially in underdeveloped or developing countries, such as Indonesia, with many regions located further away from big cities. The design proposal assists existing hospitals in reducing excessive workload due to the surge in patients and control possible in-hospital transmission. The study began by investigating criteria for designing and constructing quickly-built isolation facilities that comply with the standards for isolation space, particularly COVID-19 patients. The criteria, namely quick construction, adaptability to various contexts, and meets the minimum isolation space design standards, formed the basis for proposing the IRH design. This paper argues that as a ready-to-implement design, IRH could be an option to improve health-care services during the pandemic

knowledgenet a benchmark dataset for knowledge base population

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