14 research outputs found
Detecting and Explaining Causes From Text For a Time Series Event
Explaining underlying causes or effects about events is a challenging but
valuable task. We define a novel problem of generating explanations of a time
series event by (1) searching cause and effect relationships of the time series
with textual data and (2) constructing a connecting chain between them to
generate an explanation. To detect causal features from text, we propose a
novel method based on the Granger causality of time series between features
extracted from text such as N-grams, topics, sentiments, and their composition.
The generation of the sequence of causal entities requires a commonsense
causative knowledge base with efficient reasoning. To ensure good
interpretability and appropriate lexical usage we combine symbolic and neural
representations, using a neural reasoning algorithm trained on commonsense
causal tuples to predict the next cause step. Our quantitative and human
analysis show empirical evidence that our method successfully extracts
meaningful causality relationships between time series with textual features
and generates appropriate explanation between them.Comment: Accepted at EMNLP 201
Learning causality for Arabic - proclitics
The use of prefixed particles is a prevalent linguistic form to express causation in Arabic Language. However, such particles are complicated and highly ambiguous as they imply different meanings according to their position in the text. This ambiguity emphasizes the high demand for a large-scale annotated corpus that contains instances of these particles. In this paper, we present the process of building our corpus, which includes a collection of annotated sentences each containing an instance of a candidate causal particle. We use the corpus to construct and optimize predictive models for the task of causation recognition. The performance of the best models is significantly better than the baselines. Arabic is a less-resourced language and we hope this work would help in building better Information Extraction systems
One, no one and one hundred thousand events: Defining and processing events in an inter-disciplinary perspective
We present an overview of event definition and processing spanning 25 years of research in NLP. We first provide linguistic background to the notion of event, and then present past attempts to formalize this concept in annotation standards to foster the development of benchmarks for event extraction systems. This ranges from MUC-3 in 1991 to the Time and Space Track challenge at SemEval 2015. Besides, we shed light on other disciplines in which the notion of event plays a crucial role, with a focus on the historical domain. Our goal is to provide a comprehensive study on event definitions and investigate which potential past efforts in the NLP community may have in a different research domain. We present the results of a questionnaire, where the notion of event for historians is put in relation to the NLP perspective
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
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