Exploring Innovative Methodologies in time and place to analyze child protection documents as elements of practice

This paper aims to demonstrate how a derived approach to case file analysis, influenced by the work of Michel Foucault and Dorothy E.Smith, can offer innovative means by which to study the relations between discourse and practices in child welfare. The article explores text-based forms of organization in histories of child protection in Finland and in Northern Ireland. It is focused on case file records in different organizational child protection contexts in two jurisdictions. Building on a previous article (Author 1 & 2: 2011), we attempt to demonstrate the potential of how the relations between practices and discourses –a majorly important theme for understanding child welfare social work – can be effectively analysed using a combination of two approaches This article is based on three different empirical studies from our two jurisdictions Northern Ireland (UK) and Finland; one study used Foucault; the other Smith and the third study sought to combine the methods. This article seeks to report on ongoing work in developing, for child welfare studies, ‘a history that speaks back’ as we have described it.

Molecularly Imprinted Polymers for Corticosteroids: Impact of polymer format on recognition behaviour

A comparative study of different polymeric formats for the targeting of corticosteroids, focusing on the use of bulk monolith and precipitation polymerisation strategies, was performed and the effect on recognition behaviour was studied. Hydrocortisone-17-butyrate was selected as the template and methacrylic acid as the functional monomer, following 1H NMR investigation of the pre-polymerisation mixture. Three different cross-linkers were tested, ranging from moderate to highly hydrophobic. The synthesised bulk and precipitated imprinted polymers were physically characterised by nitrogen sorption and evaluated by means of HPLC and frontal chromatography against a range of template analogues. While some degree of selectivity for the template was achieved for all tested polymers, the ones based on the tri-functional cross-linking monomer TRIM exhibited the longest retention for all corticosteroids, especially in the precipitated format, which suggested 31 broader group selectivity.

Documents and Dependencies: an Exploration of Vector Space Models for Semantic Composition

In most previous research on distributional semantics, Vector Space Models (VSMs) of words are built either from topical information (e.g., documents in which a word is present), or from syntactic/semantic types of words (e.g., dependency parse links of a word in sentences), but not both. In this paper, we explore the utility of combining these two representations to build VSM for the task of semantic composition of adjective-noun phrases. Through extensive experiments on benchmark datasets, we find that even though a type-based VSM is effective for semantic composition, it is often outperformed by a VSM built using a combination of topic- and type-based statistics. We also introduce a new evaluation task wherein we predict the composed vector representation of a phrase from the brain activity of a human subject reading that phrase. We exploit a large syntactically parsed corpus of 16 billion tokens to build our VSMs, with vectors for both phrases and words, and make them publicly available.

Interpretable and Reconfigurable Clustering of Document Datasets by Deriving Word-based Rules

Clusters of text documents output by clustering algorithms are often hard to interpret. We describe motivating real-world scenarios that necessitate reconfigurability and high interpretability of clusters and outline the problem of generating clusterings with interpretable and reconfigurable cluster models. We develop two clustering algorithms toward the outlined goal of building interpretable and reconfigurable cluster models. They generate clusters with associated rules that are composed of conditions on word occurrences or nonoccurrences. The proposed approaches vary in the complexity of the format of the rules; RGC employs disjunctions and conjunctions in rule generation whereas RGC-D rules are simple disjunctions of conditions signifying presence of various words. In both the cases, each cluster is comprised of precisely the set of documents that satisfy the corresponding rule. Rules of the latter kind are easy to interpret, whereas the former leads to more accurate clustering. We show that our approaches outperform the unsupervised decision tree approach for rule-generating clustering and also an approach we provide for generating interpretable models for general clusterings, both by significant margins. We empirically show that the purity and f-measure losses to achieve interpretability can be as little as 3 and 5%, respectively using the algorithms presented herein.

Two-part Segmentation of Text Documents

We consider the problem of segmenting text documents that have a
two-part structure such as a problem part and a solution part. Documents
of this genre include incident reports that typically involve
description of events relating to a problem followed by those pertaining
to the solution that was tried. Segmenting such documents
into the component two parts would render them usable in knowledge
reuse frameworks such as Case-Based Reasoning. This segmentation
problem presents a hard case for traditional text segmentation
due to the lexical inter-relatedness of the segments. We develop
a two-part segmentation technique that can harness a corpus
of similar documents to model the behavior of the two segments
and their inter-relatedness using language models and translation
models respectively. In particular, we use separate language models
for the problem and solution segment types, whereas the interrelatedness
between segment types is modeled using an IBM Model
1 translation model. We model documents as being generated starting
from the problem part that comprises of words sampled from
the problem language model, followed by the solution part whose
words are sampled either from the solution language model or from
a translation model conditioned on the words already chosen in the
problem part. We show, through an extensive set of experiments on
real-world data, that our approach outperforms the state-of-the-art
text segmentation algorithms in the accuracy of segmentation, and
that such improved accuracy translates well to improved usability
in Case-based Reasoning systems. We also analyze the robustness
of our technique to varying amounts and types of noise and empirically
illustrate that our technique is quite noise tolerant, and
degrades gracefully with increasing amounts of noise