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

The alignment of formal, structured and unstructured process descriptions

Nowadays organizations are experimenting a drift on the way processes are managed. On the one hand, formal notations like Petri nets or Business Process Model and Notation (BPMN) enable the unambiguous reasoning and automation of designed processes. This way of eliciting processes by manual design, which stemmed decades ago, will still be an important actor in the future. On the other hand, regulations require organizations to store their process executions in structured representations, so that they are known and can be analyzed. Finally, due to the different nature of stakeholders within an organization (ranging from the most technical members, e.g., developers, to less technical), textual descriptions of processes are also maintained to enable that everyone in the organization understands their processes. In this paper I will describe techniques for facilitating the interconnection between these three process representations. This requires interdisciplinary research to connect several fields: business process management, formal methods, natural language processing and process mining.Peer ReviewedPostprint (author's final draft

Un algoritme per detectar la relació temporal de dues paraules.

La informació sobre la relació temporal entre dues paraules és una informació que nosaltres deduïm del text però és un camp actualment en investigació al nivell d'intel·ligència artificial. Aquest projecte està basat en el processament del llenguatge natural i té l'objectiu de implementar un sistema capaç de extreure les relacions temporals entre paraules. Aquest sistema utilitza una support vector machine amb un model prèviament entrenat. Aquest sistema ha donat bons resultats comparat amb altres sistemes que donen solució al mateix problema i ha definit un sistema base amb el qual poder seguir la investigació sobre l'extracció de relacions temporals mitjançant el processament del llenguatge natural

News event prediction using causality approach on South China Sea conflict

South China Sea (SCS) generates huge economic value in fishing and shipping lane as well as a high amount of natural resources. Due to its strategic location and high revenue generated, SCS became a place where several nearby countries competed for its territorial claims. Famous territorial disputes such as Spratly islands, Paracel island, Scarborough Shoal happened due to claim on SCS wealth. Newspapers are the main medium that disseminate the message to the public and update whenever SCS conflict happens. News related to SCS events or conflicts usually contain causal relationships between cause and effect. This causal relationship can be extracted and analyzed to obtain the trends of events and conflicts that have happened. In order to avoid any inevitable conflict among countries in SCS region, event prediction is important as it gives a better insight and foresee future events that might happen. In this paper, phrase similarity is used as important metrics for prediction models. First, it extracts news articles based on causality connectors such as "because", "after", "lead to", etc. into [removed] tuple. Then, three different embedding techniques, Doc2vec, InferSent and BERT were evaluated based on their best similarity score. The selected embedding technique is used to construct the prediction model and predict South China Sea conflict related events. A crude prediction is done based on similarity of past causes. The result shows that BERT has the highest average accuracy of 50.85% in getting the most similar phrase. By using the causal prediction model, a future possible event can be predicted and this helps to increase the awareness of national security among SCS nearby countries

Model-Agnostic process modelling

Modeling techniques in Business Process Management often suffer from low adoption due to the variety of profiles found in organizations. This project aims to provide a novel alternative to BPM documentation, ATD, based on annotated process descriptions in natural language

Extracting Semantic Relations Between Events

Events and relations between them are an important aspect of understanding natural language. This task can be broken into two, extracting events and extracting relations between them. The first task is well studied over various domains. In my thesis, I focus on the second task i.e. the extraction of relations between events. This task has been traditionally modeled as a pairwise classification task with handcrafted features. I suggest a neural network model, with ideas borrowed from models used to solve coreference resolution tasks, to solve this problem. I also suggest a structured prediction model for solving this task

Big Data and Causality

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Causality analysis continues to remain one of the fundamental research questions and the ultimate objective for a tremendous amount of scientific studies. In line with the rapid progress of science and technology, the age of big data has significantly influenced the causality analysis on various disciplines especially for the last decade due to the fact that the complexity and difficulty on identifying causality among big data has dramatically increased. Data mining, the process of uncovering hidden information from big data is now an important tool for causality analysis, and has been extensively exploited by scholars around the world. The primary aim of this paper is to provide a concise review of the causality analysis in big data. To this end the paper reviews recent significant applications of data mining techniques in causality analysis covering a substantial quantity of research to date, presented in chronological order with an overview table of data mining applications in causality analysis domain as a reference directory

Persian Causality Corpus (PerCause) and the Causality Detection Benchmark

Recognizing causal elements and causal relations in the text is among the challenging issues in natural language processing (NLP), specifically in low-resource languages such as Persian. In this research, we prepare a causality human-annotated corpus for the Persian language. This corpus consists of 4446 sentences and 5128 causal relations. Three labels of Cause, Effect, and Causal mark are specified to each relation, if possible. We used this corpus to train a system for detecting causal elements’ boundaries.Also, we present a causality detection benchmark for three machine-learning methods and two deep learning systems based on this corpus. Performance evaluations indicate that our best total result is obtained through the CRF classifier, which provides an F-measure of 0.76. In addition, the best accuracy (91.4) is obtained through the BiLSTM-CRF deep learning metho