Rethinking ‘Advanced Search’: A New Approach to Complex Query Formulation

Knowledge workers such as patent agents, recruiters and media monitoring professionals undertake work tasks where search forms a core part of their duties. In these instances, the search task often involves the formulation of complex queries expressed as Boolean strings. However, creating effective Boolean queries remains an ongoing challenge, often compromised by errors and inefficiencies. In this demo paper, we present a new approach to query formulation in which concepts are expressed on a two-dimensional canvas and relationships are articulated using direct manipulation. This has the potential to eliminate many sources of error, makes the query semantics more transparent, and offers new opportunities for query refinement and optimisatio

The Potential of Learned Index Structures for Index Compression

Inverted indexes are vital in providing fast key-word-based search. For every term in the document collection, a list of identifiers of documents in which the term appears is stored, along with auxiliary information such as term frequency, and position offsets. While very effective, inverted indexes have large memory requirements for web-sized collections. Recently, the concept of learned index structures was introduced, where machine learned models replace common index structures such as B-tree-indexes, hash-indexes, and bloom-filters. These learned index structures require less memory, and can be computationally much faster than their traditional counterparts. In this paper, we consider whether such models may be applied to conjunctive Boolean querying. First, we investigate how a learned model can replace document postings of an inverted index, and then evaluate the compromises such an approach might have. Second, we evaluate the potential gains that can be achieved in terms of memory requirements. Our work shows that learned models have great potential in inverted indexing, and this direction seems to be a promising area for future research.Comment: Will appear in the proceedings of ADCS'1

Evaluating Variable-Length Multiple-Option Lists in Chatbots and Mobile Search

In recent years, the proliferation of smart mobile devices has lead to the gradual integration of search functionality within mobile platforms. This has created an incentive to move away from the "ten blue links'' metaphor, as mobile users are less likely to click on them, expecting to get the answer directly from the snippets. In turn, this has revived the interest in Question Answering. Then, along came chatbots, conversational systems, and messaging platforms, where the user needs could be better served with the system asking follow-up questions in order to better understand the user's intent. While typically a user would expect a single response at any utterance, a system could also return multiple options for the user to select from, based on different system understandings of the user's intent. However, this possibility should not be overused, as this practice could confuse and/or annoy the user. How to produce good variable-length lists, given the conflicting objectives of staying short while maximizing the likelihood of having a correct answer included in the list, is an underexplored problem. It is also unclear how to evaluate a system that tries to do that. Here we aim to bridge this gap. In particular, we define some necessary and some optional properties that an evaluation measure fit for this purpose should have. We further show that existing evaluation measures from the IR tradition are not entirely suitable for this setup, and we propose novel evaluation measures that address it satisfactorily.Comment: 4 pages, in Proceeding of SIGIR 201

A Visual Approach to Query Formulation for Systematic Search

Knowledge workers (such as healthcare information professionals, patent agents and legal researchers) need to create and execute search strategies that are accurate, repeatable and transparent. The traditional solution offered by most database vendors is to use proprietary line-by-line'query builders'. However, these offer limited support for error checking or query optimisation, and their output can often be compromised by errors and inefficiencies. Using the healthcare domain for context, we demonstrate a new approach to search strategy formulation in which concepts are expressed as objects on a two-dimensional canvas, and relationships are articulated using direct manipulation. This approach eliminates many sources of syntactic error, makes the query semantics more transparent, and offers new ways to optimise, save and share search strategies and best practice

A high specific strength, deformation-processed scandium-titanium composite

A 59% Sc–41% Ti deformation-processed metal-metal composite was produced by rolling to a true strain of 2.3 at 873 K followed by cold rolling to a total true strain of 3.6. Rolling reduced the original eutectoid microstructure to lamellae of α–Sc and α–Ti with average lamellar thicknesses of 150 nm (Sc) and 120 nm (Ti). The cold-rolled material had an ultimate tensile strength of 942 MPa and a specific strength of 259 J/g. The Sc matrix was oriented with the 〈0001〉 tilted 22° from the sheet normal direction toward the rolling direction, an unusual texture for an HCP metal with a low c/a ratio, which suggests Sc may deform primarily by basal slip

Extracting Sentiment from Healthcare Survey Data: An Evaluation of Sentiment Analysis Tools

Sentiment analysis is an emerging discipline with many analytical tools available. This project aimed to examine a number of tools regarding their suitability for healthcare data. A comparison between commercial and non-commercial tools was made using responses from an online survey which evaluated design changes made to a clinical information service. The commercial tools were Semantria and TheySay and the non-commercial tools were WEKA and Google Prediction API. Different approaches were followed for each tool to determine the polarity of each response (i.e. positive, negative or neutral). Overall, the non-commercial tools outperformed their commercial counterparts. However, due to the different features offered by the tools, specific recommendations are made for each. In addition, single-sentence responses were tested in isolation to determine the extent to which they more clearly express a single polarity. Further work can be done to establish the relationship between single-sentence responses and the sentiment they express

An Open-Access Platform for Transparent and Reproducible Structured Searching

Knowledge workers such as patent agents, recruiters and legal researchers undertake work tasks in which search forms a core part of their duties. In these instances, the search task often involves formulation of complex queries expressed as Boolean strings. However, creating effective Boolean queries remains an ongoing challenge, often compromised by errors and inefficiencies. In this paper, we demonstrate a new approach to structured searching in which concepts are expressed as objects on a two-dimensional canvas. Interactive query suggestions are provided via an NLP services API, and support is offered for optimising, translating and sharing search strategies as executable artefacts. This eliminates many sources of error, makes the query semantics more transparent, and offers an open-access platform for sharing reproducible search strategies and best practices

Easing Legal News Monitoring with Learning to Rank and BERT

While ranking approaches have made rapid advances in the Web search, systems that cater to the complex information needs in professional search tasks are not widely developed, common issues and solutions typically rely on dedicated search strategies backed by ad-hoc retrieval models. In this paper we present a legal search problem where professionals monitor news articles with constant queries on a periodic basis. Firstly, we demonstrate the effectiveness of using traditional retrieval models against the Boolean search of documents in chronological order. In an attempt to capture the complex information needs of users, a learning to rank approach is adopted with user specified relevance criteria as features. This approach, however, only achieves mediocre results compared to the traditional models. However, we find that by fine-tuning a contextualised language model (e.g. BERT), significantly improved retrieval performance can be achieved, providing a flexible solution to satisfying complex information needs without explicit feature engineering

Information search in a professional context – exploring a collection of professional search tasks

Effective Protection of Fundamental Rights in a pluralist worl

Currency Unions

A currency union is when several independent sovereign nations share a common currency. This has been a recurring phenomenon in monetary history. In this article I study the theoretical foundations of such unions, and discuss some important currency unions in history, most notably the case of the US. Finally I contrast the design of the EMU with economic theories and historical experiences of currency unions