scholarly journals Improving Legal Case Summarization Using Document-Specific Catchphrases

2021 ◽  
Author(s):  
Arpan Mandal ◽  
Paheli Bhattacharya ◽  
Sekhar Mandal ◽  
Saptarshi Ghosh
Keyword(s):  
Performance Metrics ◽  
Specific Information ◽  
Document Summarization ◽  
Domain Specific ◽  
Legal Case ◽  
Different Types ◽  
Legal Document

Legal case summarization is an important problem, and several domain-specific summarization algorithms have been applied for this task. These algorithms generally use domain-specific legal dictionaries to estimate the importance of sentences. However, none of the popular summarization algorithms use document-specific catchphrases, which provide a unique amalgamation of domain-specific and document-specific information. In this work, we assess the performance of two legal document summarization algorithms, when two different types of catchphrases are incorporated in the summarization process. Our experiments confirm that both the summarization algorithms show improvement across all performance metrics, with the incorporation of document-specific catchphrases.

scholarly journals Analyzing stereotypes of creating graphical user interfaces

2012 ◽  
Vol 2 (3) ◽  
Author(s):  
Michaela Bačíková ◽  
Jaroslav Porubän
Keyword(s):  
User Interfaces ◽  
Primary Source ◽  
Domain Analysis ◽  
Graphical User Interfaces ◽  
Specific Information ◽  
Basic Principles ◽  
Domain Specific ◽  
Different Types ◽  
General Goal ◽  
Domain Information

AbstractA graphical user interface (GUI, UI) is an important part of an application, with which users interact directly. It should be implemented in the best way with respect to understandability. If a user does not understand the terms in the UI, he or she cannot work with it; then the whole system is worthless. In order to serve well the UI should contain domain-specific terms and describe domain-specific processes. It is the primary source for domain analysis right after domain users and experts. Our general goal is to propose a method for an automatic domain analysis of user interfaces. First, however, the basic principles and stereotypes must be defined that are used when creating user interfaces and rules must be derived for creating an information extracting algorithm. In this paper these stereotypes are listed and analyzed and a set of rules for extracting domain information is created. A taxonomy of UIs and a taxonomy of components based on their domain-specific information is also proposed. Our DEAL method for extracting this information is outlined and a prototype of DEAL is presented. Also our goals for the future are listed: expanding the prototype for different components and different types of UIs.

scholarly journals An Analytical Study of Algorithmic and Expert Summaries of Legal Cases

2021 ◽  
Author(s):  
Aniket Deroy ◽  
Paheli Bhattacharya ◽  
Kripabandhu Ghosh ◽  
Saptarshi Ghosh
Keyword(s):  
Analytical Study ◽  
Challenging Problem ◽  
Automatic Summarization ◽  
Legal Cases ◽  
Domain Specific ◽  
Legal Case ◽  
Legal Document

Automatic summarization of legal case documents is an important and challenging problem, where algorithms attempt to generate summaries that match well with expert-generated summaries. This work takes the first step in analyzing expert-generated summaries and algorithmic summaries of legal case documents. We try to uncover how law experts write summaries for a legal document, how various generic as well as domain-specific extractive algorithms generate summaries, and how the expert summaries vary from the algorithmic summaries. We also analyze which important sentences of a legal case document are missed by most algorithms while generating summaries, in terms of the rhetorical roles of the sentences and the positions of the sentences in the legal document.

Domain-generality and the relative pronoun

2002 ◽  
Vol 25 (6) ◽  
pp. 676-677
Author(s):  
José Luis Bermudez
Keyword(s):  
Cognitive Function ◽  
Natural Language ◽  
Relative Clauses ◽  
Specific Information ◽  
Relative Pronoun ◽  
Domain Specific ◽  
Domain Generality ◽  
Different Types ◽  
Target Paper

The hypothesis in the target paper is that the cognitive function of language lies in making possible the integration of different types of domain-specific information. The case for this hypothesis must consist, at least in part, of a constructive proposal as to what feature or features of natural language allows this integration to take place. This commentary suggests that the vital linguistic element is the relative pronoun and the possibility it affords of forming relative clauses.

scholarly journals Creativity as a function of problem-solving expertise: posing new problems through investigations

ZDM ◽  
2021 ◽  
Author(s):  
Haim Elgrably ◽  
Roza Leikin
Keyword(s):  
Problem Solving ◽  
Dynamic Geometry ◽  
Problem Posing ◽  
Dynamic Geometry Environment ◽  
Mathematics Majors ◽  
Individual Interviews ◽  
University Mathematics ◽  
Domain Specific ◽  
Mathematics Test ◽  
Different Types

AbstractThis study was inspired by the following question: how is mathematical creativity connected to different kinds of expertise in mathematics? Basing our work on arguments about the domain-specific nature of expertise and creativity, we looked at how participants from two groups with two different types of expertise performed in problem-posing-through-investigations (PPI) in a dynamic geometry environment (DGE). The first type of expertise—MO—involved being a candidate or a member of the Israeli International Mathematical Olympiad team. The second type—MM—was comprised of mathematics majors who excelled in university mathematics. We conducted individual interviews with eight MO participants who were asked to perform PPI in geometry, without previous experience in performing a task of this kind. Eleven MMs tackled the same PPI task during a mathematics test at the end of a 52-h course that integrated PPI. To characterize connections between creativity and expertise, we analyzed participants’ performance on the PPI tasks according to proof skills (i.e., auxiliary constructions, the complexity of posed tasks, and correctness of their proofs) and creativity components (i.e., fluency, flexibility and originality of the discovered properties). Our findings demonstrate significant differences between PPI by MO participants and by MM participants as reflected in the more creative performance and more successful proving processes demonstrated by MO participants. We argue that problem posing and problem solving are inseparable when MO experts are engaged in PPI.

Knowledge Enhanced LSTM for Coreference Resolution on Biomedical Texts

2021 ◽  
Author(s):  
Yufei Li ◽  
Xiaoyong Ma ◽  
Xiangyu Zhou ◽  
Pengzhen Cheng ◽  
Kai He ◽  
...  
Keyword(s):  
Information Integration ◽  
Short Term Memory ◽  
Superior Performance ◽  
Supplementary Information ◽  
Specific Information ◽  
Coreference Resolution ◽  
Fine Grained ◽  
Domain Specific ◽  
Memory Network ◽  
Biomedical Texts

Abstract Motivation Bio-entity Coreference Resolution focuses on identifying the coreferential links in biomedical texts, which is crucial to complete bio-events’ attributes and interconnect events into bio-networks. Previously, as one of the most powerful tools, deep neural network-based general domain systems are applied to the biomedical domain with domain-specific information integration. However, such methods may raise much noise due to its insufficiency of combining context and complex domain-specific information. Results In this paper, we explore how to leverage the external knowledge base in a fine-grained way to better resolve coreference by introducing a knowledge-enhanced Long Short Term Memory network (LSTM), which is more flexible to encode the knowledge information inside the LSTM. Moreover, we further propose a knowledge attention module to extract informative knowledge effectively based on contexts. The experimental results on the BioNLP and CRAFT datasets achieve state-of-the-art performance, with a gain of 7.5 F1 on BioNLP and 10.6 F1 on CRAFT. Additional experiments also demonstrate superior performance on the cross-sentence coreferences. Supplementary information Supplementary data are available at Bioinformatics online.

PROTEIN STRUCTURE ALIGNMENT AND FAST SIMILARITY SEARCH USING LOCAL SHAPE SIGNATURES

2004 ◽  
Vol 02 (01) ◽  
pp. 215-239 ◽  
Author(s):  
TOLGA CAN ◽  
YUAN-FANG WANG
Keyword(s):  
Protein Structure ◽  
Protein Structures ◽  
Structure Alignment ◽  
Protein Structure Alignment ◽  
Specific Information ◽  
Alignment Algorithm ◽  
Screening Process ◽  
Domain Specific ◽  
Local Sequence ◽  
Shape Signatures

We present a new method for conducting protein structure similarity searches, which improves on the efficiency of some existing techniques. Our method is grounded in the theory of differential geometry on 3D space curve matching. We generate shape signatures for proteins that are invariant, localized, robust, compact, and biologically meaningful. The invariancy of the shape signatures allows us to improve similarity searching efficiency by adopting a hierarchical coarse-to-fine strategy. We index the shape signatures using an efficient hashing-based technique. With the help of this technique we screen out unlikely candidates and perform detailed pairwise alignments only for a small number of candidates that survive the screening process. Contrary to other hashing based techniques, our technique employs domain specific information (not just geometric information) in constructing the hash key, and hence, is more tuned to the domain of biology. Furthermore, the invariancy, localization, and compactness of the shape signatures allow us to utilize a well-known local sequence alignment algorithm for aligning two protein structures. One measure of the efficacy of the proposed technique is that we were able to perform structure alignment queries 36 times faster (on the average) than a well-known method while keeping the quality of the query results at an approximately similar level.

scholarly journals Improvement of Adaptive Load Balanced Gateway Discovery Protocol in Hybrid Integrated Internet-MANET

2017 ◽  
Vol 11 (3) ◽  
pp. 162
Author(s):  
Rafi U Zaman ◽  
Humaira M Alam ◽  
Khaleel Ur Rahman Khan ◽  
A. Venugopal Reddy
Keyword(s):  
Load Balancing ◽  
Performance Metrics ◽  
Mobile Node ◽  
Simulation Tool ◽  
5G Networks ◽  
End To End Delay ◽  
Hybrid Framework ◽  
Different Types ◽  
Gateway Discovery ◽  
Networking Architecture

<p class="0abstract">Internetworking of different types of networks is envisaged as one of the primary objectives of the future 5G networks. Integrated Internet-MANET is a heterogeneous networking architecture which is the result of interconnecting wired Internet and wireless MANET. Multiprotocol gateways are used to achieve this interconnection. There are two types of Integrated Internet-MANET architectures, two-tier and three-tier. A combination of two-tier and three tier architectures also exists, called the Hybrid Framework or Hybrid Integrated Internet-MANET. Some of the most important issues common to all Integrated Internet-MANET architecture are: efficient gateway discovery, mobile node registration and gateway load balancing. Adaptive WLB-AODV is an existing protocol which addresses the issues of Gateway load balancing and efficient Gateway discovery. In this paper, an improvement is proposed to Adaptive WLB-AODV, called Adaptive Modified-WLV-AODV by taking into account route latency. The proposed protocol has been implemented in Hybrid Integrated Internet-MANET and has been simulated using network simulation tool ns-2. Based on the simulation results, it is observed that the proposed protocol delivers better performance than the existing protocol in terms of performance metrics end-to-end delay and packet loss ratio.  The performance of the proposed protocol is further optimized using a genetic algorithm.</p>

scholarly journals DISSECT: DISentangle SharablE ConTent for Multimodal Integration and Crosswise-mapping

2020 ◽  
Author(s):  
Geoffrey Schau ◽  
Erik Burlingame ◽  
Young Hwan Chang
Keyword(s):  
Deep Learning ◽  
Complete Information ◽  
Specific Information ◽  
Multimodal Integration ◽  
Specific Data ◽  
Domain Specific ◽  
Cross Domain ◽  
Input Feature ◽  
Novel Approach ◽  
Latent Representations

AbstractDeep learning systems have emerged as powerful mechanisms for learning domain translation models. However, in many cases, complete information in one domain is assumed to be necessary for sufficient cross-domain prediction. In this work, we motivate a formal justification for domain-specific information separation in a simple linear case and illustrate that a self-supervised approach enables domain translation between data domains while filtering out domain-specific data features. We introduce a novel approach to identify domainspecific information from sets of unpaired measurements in complementary data domains by considering a deep learning cross-domain autoencoder architecture designed to learn shared latent representations of data while enabling domain translation. We introduce an orthogonal gate block designed to enforce orthogonality of input feature sets by explicitly removing non-sharable information specific to each domain and illustrate separability of domain-specific information on a toy dataset.

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