3,049 research outputs found

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

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    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

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    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

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    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

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    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

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    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

    An Open-Access Platform for Transparent and Reproducible Structured Searching

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    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

    Does absorption against AGN reveal supermassive black hole accretion?

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    Galaxies often contain large reservoirs of molecular gas that shape their evolution. This can be through cooling of the gas - which leads to star formation, or accretion on to the central supermassive black hole - which fuels active galactic nucleus (AGN) activity and produces powerful feedback. Molecular gas has been detected in early-type galaxies on scales of just a few tens to hundreds of solar masses by searching for absorption against their compact radio cores. Using this technique, ALMA has found absorption in several brightest cluster galaxies, some of which show molecular gas moving towards their galaxy's core at hundreds of km s-1. In this paper, we constrain the location of this absorbing gas by comparing each galaxy's molecular emission and absorption. In four galaxies, the absorption properties are consistent with chance alignments between the continuum and a fraction of the molecular clouds visible in emission. In four others, the properties of the absorption are inconsistent with this scenario. In these systems, the absorption is likely produced by a separate population of molecular clouds in close proximity to the galaxy core and with high inward velocities and velocity dispersions. We thus deduce the existence of two types of absorber, caused by chance alignments between the radio core and: (i) a fraction of the molecular clouds visible in emission, and (ii) molecular clouds close to the AGN, in the process of accretion. We also present the first ALMA observations of molecular emission in S555, Abell 2390, RXC J1350.3+0940, and RXC J1603.6+1553 - with the latter three having Mmol > 1010 Mθ

    Easing Legal News Monitoring with Learning to Rank and BERT

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    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
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