Nominal Entity Research Articles

Comparing supervised learning algorithms for Spatial Nominal Entity recognition

Abstract. Discourse may contain both named and nominal entities. Most common nouns or nominal mentions in natural language do not have a single, simple meaning but rather a number of related meanings. This form of ambiguity led to the development of a task in natural language processing known as Word Sense Disambiguation. Recognition and categorisation of named and nominal entities is an essential step for Word Sense Disambiguation methods. Up to now, named entity recognition and categorisation systems mainly focused on the annotation, categorisation and identification of named entities. This paper focuses on the annotation and the identification of spatial nominal entities. We explore the combination of Transfer Learning principle and supervised learning algorithms, in order to build a system to detect spatial nominal entities. For this purpose, different supervised learning algorithms are evaluated with three different context sizes on two manually annotated datasets built from Wikipedia articles and hiking description texts. The studied algorithms have been selected for one or more of their specific properties potentially useful in solving our problem. The results of the first phase of experiments reveal that the selected algorithms have similar performances in terms of ability to detect spatial nominal entities. The study also confirms the importance of the size of the window to describe the context, when word-embedding principle is used to represent the semantics of each word.

Emergency trauma theatre utilisation – the St George’s Hospital, London experience

Background The Royal College of Surgeons of England encouraged the creation of singly dedicated 24-hour emergency theatres in 1997; emergency operating was previously largely determined by theatre availability rather than clinical need. Shortly after this St George’s Hospital, London designated one of its three trauma theatres as an ‘Acute Emergency Theatre’ (AET) and implemented a policy to maintain access to the AET within one hour’s notification for all acute trauma emergencies. On review it was felt that the system underutilised scarce theatre space and limited the repertoire of surgeries that could be performed. A more efficient use of theatre space was therefore sought. The AET was changed from a fixed physical entity to a nominal entity; where by the AET could be any of the three major trauma theatres, as long as one of them was available within one hour.

Four Ways of Understanding - A Concept Note

Here in this short note I try to draw attention to the ‘four ways of understanding’ of any phenomenon, including the ‘social’. A phenomenon can be taken as the combination of name or action, entity or group process , becoming or unbecoming and stable or in transition. Any phenomenon that first appear as nominal stable entity when better understood its becoming, unbecoming, active and manifold aspects looms larger.

The acute emergency theatre – The concept as a nominal entity, rather than a physical entity – An audit on optimising theatre utilisation

The acute emergency theatre – The concept as a nominal entity, rather than a physical entity – An audit on optimising theatre utilisation

Datasets for generic relation extraction

AbstractA vast amount of usable electronic data is in the form of unstructured text. The relation extraction task aims to identify useful information in text (e.g. PersonW works for OrganisationX, GeneY encodes ProteinZ) and recode it in a format such as a relational database or RDF triplestore that can be more effectively used for querying and automated reasoning. A number of resources have been developed for training and evaluating automatic systems for relation extraction in different domains. However, comparative evaluation is impeded by the fact that these corpora use different markup formats and notions of what constitutes a relation. We describe the preparation of corpora for comparative evaluation of relation extraction across domains based on the publicly available ACE 2004, ACE 2005 and BioInfer data sets. We present a common document type using token standoff and including detailed linguistic markup, while maintaining all information in the original annotation. The subsequent reannotation process normalises the two data sets so that they comply with a notion of relation that is intuitive, simple and informed by the semantic web. For the ACE data, we describe an automatic process that automatically converts many relations involving nested, nominal entity mentions to relations involving non-nested, named or pronominal entity mentions. For example, the first entity is mapped from ‘one’ to ‘Amidu Berry’ in the membership relation described in ‘Amidu Berry, one half of PBS’. Moreover, we describe a comparably reannotated version of the BioInfer corpus that flattens nested relations, maps part-whole to part-part relations and maps n-ary to binary relations. Finally, we summarise experiments that compare approaches to generic relation extraction, a knowledge discovery task that uses minimally supervised techniques to achieve maximally portable extractors. These experiments illustrate the utility of the corpora.1

The modification of predicative bare nouns in French: a functional analysis1

AbstractThis paper offers a corpus‐based study of the (restricted) modification patterns of predicatively used bare nouns in French (e.g. Je suis bon catholique‘I am a good Catholic’). These are ‘truly’ bare in that they do not rely on a covert determiner. The aim of the paper is to determine which modification patterns are allowed and to provide a semantic‐functional explanation. Further, on the basis of this study, some conclusions can be drawn both about the categorial status of the bare predicative noun and about the internal architecture of type specification, the innermost functional layer within the NP. More specifically, the study of bare nouns leads to the identification of several lower‐level slots that are independent of the instantiation of a particular nominal entity in conceptual space. These are to be distinguished from higher‐level modifiers. To end, some additional syntactic contexts are provided in which bare nouns occur. These display identical restrictions with respect to modification, but differ in the array of lexical heads they allow for.

ExaminingCf-ranking Methods for Text Structuring and Pronominalization in Korean

Centering Theory assumes that entities realized in an utterance can be ranked according to their relative salience degree. This ranking called Cf-ranking (ranking of forward-looking centers) determines the likelihood that the entities realized in an utterance will be the center of the subsequent utterance, and it is one of the central issues in Centering literatures. This paper deals with these Cf-ranking issues in Korean at the level of text structuring and pronominalization for coherent text generation. For text structuring, we compare several Cf-ranking methods by examining various Centering-based metrics to evaluate local coherence of text, and for pronominalization, we compare them by examining previous rules for Centering-based pronoun generation rules and our pronominalization model. In almost all previous works, surface word order was not solely employed for Cf-ranking, instead it was additionally considered to supplement the main ranking scheme based on the fact that linear order does not perform well alone. However, this study shows that due to the characteristics of the Korean language, ranking by surface word order is better than any other ranking method in most Centering-based Metrics which depend on Cf-ranking, and it is also reliable in terms of pronominalization accuracy. Additionally we found that based on the Cf-ranking by surface word order, it is the most effective way for text structuring to maximize simply the number of utterance pairs whose first realized nominal entity in adjacent utterances is identical.

Generation of Zero Pronouns Based on the Centering Theory and Pairwise Salience of Entities

This paper investigates zero pronouns in Korean, especially focusing on the center transitions of adjacent utterances under the framework of Centering Theory. Four types of nominal entity (Epair, Einter, Eintra, and Enon) from Centering Theory are defined with the concept of inter-, intra-, and pairwise salience. For each entity type, a case study of zero phenomena is performed through analyzing corpus and building a pronominalization model. This study shows that the zero phenomena of entities which have been neglected in previous Centering works are explained via the center transition of the second previous utterance, and provides valuable results for pronominalization of such entities, such as p2-trans rule. We improve the accuracy of pronominalization model by optimal feature selection and show that our accuracy outperforms the accuracy of previous works.