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

Improving Syntactic Parsing of Clinical Text Using Domain Knowledge

Syntactic parsing is one of the fundamental tasks of Natural Language Processing (NLP). However, few studies have explored syntactic parsing in the medical domain. This dissertation systematically investigated different methods to improve the performance of syntactic parsing of clinical text, including (1) Constructing two clinical treebanks of discharge summaries and progress notes by developing annotation guidelines that handle missing elements in clinical sentences; (2) Retraining four state-of-the-art parsers, including the Stanford parser, Berkeley parser, Charniak parser, and Bikel parser, using clinical treebanks, and comparing their performance to identify better parsing approaches; and (3) Developing new methods to reduce syntactic ambiguity caused by Prepositional Phrase (PP) attachment and coordination using semantic information. Our evaluation showed that clinical treebanks greatly improved the performance of existing parsers. The Berkeley parser achieved the best F-1 score of 86.39% on the MiPACQ treebank. For PP attachment, our proposed methods improved the accuracies of PP attachment by 2.35% on the MiPACQ corpus and 1.77% on the I2b2 corpus. For coordination, our method achieved a precision of 94.9% and a precision of 90.3% for the MiPACQ and i2b2 corpus, respectively. To further demonstrate the effectiveness of the improved parsing approaches, we applied outputs of our parsers to two external NLP tasks: semantic role labeling and temporal relation extraction. The experimental results showed that performance of both tasks’ was improved by using the parse tree information from our optimized parsers, with an improvement of 3.26% in F-measure for semantic role labelling and an improvement of 1.5% in F-measure for temporal relation extraction

Identification and classification of facial familiarity in directed lying: an ERP Study

Recognizing familiar faces is essential to social functioning, but little is known about how people identify human faces and classify them in terms of familiarity. Face identification involves discriminating familiar faces from unfamiliar faces, whereas face classification involves making an intentional decision to classify faces as 'familiar' or 'unfamiliar.' This study used a directed-lying task to explore the differentiation between identification and classification processes involved in the recognition of familiar faces. To explore this issue, the participants in this study were shown familiar and unfamiliar faces. They responded to these faces (i.e., as familiar or unfamiliar) in accordance with the instructions they were given (i.e., to lie or to tell the truth) while their EEG activity was recorded. Familiar faces (regardless of lying vs. truth) elicited significantly less negative-going N400f in the middle and right parietal and temporal regions than unfamiliar faces. Regardless of their actual familiarity, the faces that the participants classified as 'familiar' elicited more negative-going N400f in the central and right temporal regions than those classified as 'unfamiliar.' The P600 was related primarily with the facial identification process. Familiar faces (regardless of lying vs. truth) elicited more positive-going P600f in the middle parietal and middle occipital regions. The results suggest that N400f and P600f play different roles in the processes involved in facial recognition. The N400f appears to be associated with both the identification (judgment of familiarity) and classification of faces, while it is likely that the P600f is only associated with the identification process (recollection of facial information). Future studies should use different experimental paradigms to validate the generalizability of the results of this study.published_or_final_versio

Apperceptive patterning: Artefaction, extensional beliefs and cognitive scaffolding

In “Psychopower and Ordinary Madness” my ambition, as it relates to Bernard Stiegler’s recent literature, was twofold: 1) critiquing Stiegler’s work on exosomatization and artefactual posthumanism—or, more specifically, nonhumanism—to problematize approaches to media archaeology that rely upon technical exteriorization; 2) challenging how Stiegler engages with Giuseppe Longo and Francis Bailly’s conception of negative entropy. These efforts were directed by a prevalent techno-cultural qualifier: the rise of Synthetic Intelligence (including neural nets, deep learning, predictive processing and Bayesian models of cognition). This paper continues this project but first directs a critical analytic lens at the Derridean practice of the ontologization of grammatization from which Stiegler emerges while also distinguishing how metalanguages operate in relation to object-oriented environmental interaction by way of inferentialism. Stalking continental (Kapp, Simondon, Leroi-Gourhan, etc.) and analytic traditions (e.g., Carnap, Chalmers, Clark, Sutton, Novaes, etc.), we move from artefacts to AI and Predictive Processing so as to link theories related to technicity with philosophy of mind. Simultaneously drawing forth Robert Brandom’s conceptualization of the roles that commitments play in retrospectively reconstructing the social experiences that lead to our endorsement(s) of norms, we compliment this account with Reza Negarestani’s deprivatized account of intelligence while analyzing the equipollent role between language and media (both digital and analog)

Processing temporal information in unstructured documents

Tese de doutoramento, Informática (Ciência da Computação), Universidade de Lisboa, Faculdade de Ciências, 2013Temporal information processing has received substantial attention in the last few years, due to the appearance of evaluation challenges focused on the extraction of temporal information from texts written in natural language. This research area belongs to the broader field of information extraction, which aims to automatically find specific pieces of information in texts, producing structured representations of that information, which can then be easily used by other computer applications. It has the potential to be useful in several applications that deal with natural language, given that many languages, among which we find Portuguese, extensively refer to time. Despite that, temporal processing is still incipient for many language, Portuguese being one of them. The present dissertation has various goals. On one hand, it addresses this current gap, by developing and making available resources that support the development of tools for this task, employing this language, and also by developing precisely this kind of tools. On the other hand, its purpose is also to report on important results of the research on this area of temporal processing. This work shows how temporal processing requires and benefits from modeling different kinds of knowledge: grammatical knowledge, logical knowledge, knowledge about the world, etc. Additionally, both machine learning methods and rule-based approaches are explored and used in the development of hybrid systems that are capable of taking advantage of the strengths of each of these two types of approach.O processamento de informação temporal tem recebido bastante atenção nos últimos anos, devido ao surgimento de desafios de avaliação focados na extração de informação temporal de textos escritos em linguagem natural. Esta área de investigação enquadra-se no campo mais lato da extração de informação, que visa encontrar automaticamente informação específica presente em textos, produzindo representações estruturadas da mesma, que podem depois ser facilmente utilizadas por outras aplicações computacionais. Tem o potencial de ser útil em diversas aplicações que lidam com linguagem natural, dado o caráter quase ubíquo da referência ao tempo cronólogico em muitas línguas, entre as quais o Português. Apesar de tudo, o processamento temporal encontra-se ainda incipiente para bastantes línguas, sendo o Português uma delas. A presente dissertação tem vários objetivos. Por um lado vem colmatar esta lacuna existente, desenvolvendo e disponibilizando recursos que suportam o desenvolvimento de ferramentas para esta tarefa, utilizando esta língua, e desenvolvendo também precisamente este tipo de ferramentas. Por outro serve também para relatar resultados importantes da pesquisa nesta área do processamento temporal. Neste trabalho, mostra- -se como o processamento temporal requer e beneficia da modelação de conhecimento de diversos níveis: gramatical, lógico, acerca do mundo, etc. Adicionalmente, são explorados tanto métodos de aprendizagem automática como abordagens baseadas em regras, desenvolvendo-se sistemas híbridos capazes de tirar partido das vantagens de cada um destes dois tipos de abordagem.Fundação para a Ciência e a Tecnologia (FCT, SFRH/BD/40140/2007

Responding to gratitude in elicited oral interaction. A taxonomy of communicative options

This study explores responses to gratitude as expressed in elicited oral interaction (mimetic-pretending open role-plays) produced by native speakers of American English. It first overviews the literature on this topic. It then presents a taxonomy of the head acts and supporting moves of the responses to gratitude instantiated in the corpus under examination, which considers their strategies and formulations. Finally, it reports on their frequency of occurrence and combinatorial options across communicative situations differing in terms of the social distance and power relationships between the interactants. The findings partly confirm what reported in the literature, but partly reveal the flexibility and adaptability of these reacting speech acts to the variable context in which they may be instantiated. On the one hand, the responses to gratitude identified tend to be encoded as simple utterances, and occasionally as complex combinations of head acts and/or supporting moves; also, their head acts show a preference for a small set of strategies and formulation types, while their supporting moves are much more varied in content and form, and thus situation-specific. On the other hand, the frequency of occurrence of the responses to gratitude, their dispersion across situations, and the range of their attested strategies and formulations are not in line with those reported in previous studies. I argue that these partly divergent findings are to be related to the different data collection and categorization procedures adopted, and the different communicative situations considered across studies. Overall, the study suggests that: responses to gratitude are a set of communicative events with fuzzy boundaries, which contains core (i.e. more prototypical) and peripheral (i.e. less prototypical) exemplars; although routinized in function, responses to gratitude are not completely conventionalized in their strategic or surface realizations; alternative research approaches may provide complementary insights into these reacting speech acts; and a higher degree of comparability across studies may be ensured if explicit pragmatic and semantic parameters are adopted in the classification of their shared object of study