Towards Better Text Understanding and Retrieval through Kernel Entity Salience Modeling

This paper presents a Kernel Entity Salience Model (KESM) that improves text understanding and retrieval by better estimating entity salience (importance) in documents. KESM represents entities by knowledge enriched distributed representations, models the interactions between entities and words by kernels, and combines the kernel scores to estimate entity salience. The whole model is learned end-to-end using entity salience labels. The salience model also improves ad hoc search accuracy, providing effective ranking features by modeling the salience of query entities in candidate documents. Our experiments on two entity salience corpora and two TREC ad hoc search datasets demonstrate the effectiveness of KESM over frequency-based and feature-based methods. We also provide examples showing how KESM conveys its text understanding ability learned from entity salience to search

Ontogenetic changes in the body plan of the sauropodomorph dinosaur Mussaurus patagonicus reveal shifts of locomotor stance during growth

Ontogenetic information is crucial to understand life histories and represents a true challenge in dinosaurs due to the scarcity of growth series available. Mussaurus patagonicus was a sauropodomorph dinosaur close to the origin of Sauropoda known from hatchling, juvenile and mature specimens, providing a sufficiently complete ontogenetic series to reconstruct general patterns of ontogeny. Here, in order to quantify how body shape and its relationship with locomotor stance (quadruped/biped) changed in ontogeny, hatchling, juvenile (~1 year old) and adult (8+ years old) individuals were studied using digital models. Our results show that Mussaurus rapidly grew from about 60 g at hatching to ~7 kg at one year old, reaching >1000 kg at adulthood. During this time, the body’s centre of mass moved from a position in the mid-thorax to a more caudal position nearer to the pelvis. We infer that these changes of body shape and centre of mass reflect a shift from quadrupedalism to bipedalism occurred early in ontogeny in Mussaurus. Our study indicates that relative development of the tail and neck was more influential in determining the locomotor stance in Sauropodomorpha during ontogeny, challenging previous studies, which have emphasized the influence of hindlimb vs. forelimb lengths on sauropodomorph stance

Opening dialogue and fostering collaboration: different ways of knowing in fisheries research

We set out to explore some of the impediments which hinder effective communication among fishers, fisheries researchers and managers using detailed ethnographic research amongst commercial handline fishers from two sites- one on the southern Cape coast and the other on the west coast of South Africa. Rather than assuming that the knowledge of fishers and scientists is inherently divergent and incompatible, we discuss an emerging relational approach to working with multiple ways of knowing and suggest that this approach might benefit future collaborative endeavours. Three major themes arising from the ethnographic fieldwork findings are explored: different classifications of species and things; bringing enumerative approaches into dialogue with relational approaches; and the challenge of articulating embodied ways of relating to fish and the sea. Although disconcertments arise when apparently incommensurable approaches are brought into dialogue, we suggest that working with multiple ways of knowing is both productive and indeed necessary in the current South African fisheries research and management contexts. The research findings and discussion on opening dialogue offered in this work suggest a need to rethink contemporary approaches to fisheries research in order to mobilise otherwise stagnant conversations, bringing different ways of knowing into productive conversation

Opening dialogue and fostering collaboration: different ways of knowing in fisheries research

We set out to explore some of the impediments which hinder effective communication among fishers, fisheries researchers and managers using detailed ethnographic research amongst commercial handline fishers from two sites- one on the southern Cape coast and the other on the west coast of South Africa. Rather than assuming that the knowledge of fishers and scientists is inherently divergent and incompatible, we discuss an emerging relational approach to working with multiple ways of knowing and suggest that this approach might benefit future collaborative endeavours. Three major themes arising from the ethnographic fieldwork findings are explored: different classifications of species and things; bringing enumerative approaches into dialogue with relational approaches; and the challenge of articulating embodied ways of relating to fish and the sea. Although disconcertments arise when apparently incommensurable approaches are brought into dialogue, we suggest that working with multiple ways of knowing is both productive and indeed necessary in the current South African fisheries research and management contexts. The research findings and discussion on opening dialogue offered in this work suggest a need to rethink contemporary approaches to fisheries research in order to mobilise otherwise stagnant conversations, bringing different ways of knowing into productive conversation

Methods for improving entity linking and exploiting social media messages across crises

Entity Linking (EL) is the task of automatically identifying entity mentions in texts and resolving them to a corresponding entity in a reference knowledge base (KB). There is a large number of tools available for different types of documents and domains, however the literature in entity linking has shown the quality of a tool varies across different corpus and depends on specific characteristics of the corpus it is applied to. Moreover the lack of precision on particularly ambiguous mentions often spoils the usefulness of automated disambiguation results in real world applications. In the first part of this thesis I explore an approximation of the difficulty to link entity mentions and frame it as a supervised classification task. Classifying difficult to disambiguate entity mentions can facilitate identifying critical cases as part of a semi-automated system, while detecting latent corpus characteristics that affect the entity linking performance. Moreover, despiteless the large number of entity linking tools that have been proposed throughout the past years, some tools work better on short mentions while others perform better when there is more contextual information. To this end, I proposed a solution by exploiting results from distinct entity linking tools on the same corpus by leveraging their individual strengths on a per-mention basis. The proposed solution demonstrated to be effective and outperformed the individual entity systems employed in a series of experiments. An important component in the majority of the entity linking tools is the probability that a mentions links to one entity in a reference knowledge base, and the computation of this probability is usually done over a static snapshot of a reference KB. However, an entity’s popularity is temporally sensitive and may change due to short term events. Moreover, these changes might be then reflected in a KB and EL tools can produce different results for a given mention at different times. I investigated the prior probability change over time and the overall disambiguation performance using different KB from different time periods. The second part of this thesis is mainly concerned with short texts. Social media has become an integral part of the modern society. Twitter, for instance, is one of the most popular social media platforms around the world that enables people to share their opinions and post short messages about any subject on a daily basis. At first I presented one approach to identifying informative messages during catastrophic events using deep learning techniques. By automatically detecting informative messages posted by users during major events, it can enable professionals involved in crisis management to better estimate damages with only relevant information posted on social media channels, as well as to act immediately. Moreover I have also performed an analysis study on Twitter messages posted during the Covid-19 pandemic. Initially I collected 4 million tweets posted in Portuguese since the begining of the pandemic and provided an analysis of the debate aroud the pandemic. I used topic modeling, sentiment analysis and hashtags recomendation techniques to provide isights around the online discussion of the Covid-19 pandemic