An evaluation of MorphInd's morphological annotation scheme for Indonesian

Corpora ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. 287-299
Author(s):  
Prihantoro  
Keyword(s):  
Comprehensive Evaluation ◽  
State Of The Art ◽  
Annotation Scheme ◽  
Text Corpus ◽  
Common Features ◽  
Part Of Speech

MorphInd 2 ( Larasati et al., 2011 ) is a state-of-the-art morphological analyser for Indonesian. To date, there has not been any comprehensive evaluation of the morphological annotation scheme which MorphInd implements. My evaluation of this annotation scheme reveals a number of significant drawbacks. Some analytical features encoded in MorphInd's tagset seem not to reflect features actually present in Indonesian morphology, while certain common features in the analysis of Indonesian are absent. Likewise, the Part of Speech (pos) hierarchy in the MorphInd tagset does not reflect the usual pos hierarchy used by Indonesian reference grammars. Moreover, the MorphInd output does not link morphological tags to the corresponding morpheme. Finally, a number of issues which might problematise text/corpus querying in the annotation's layout are observable, particularly relating to affixes, reduplication, and the affix–reduplication interface.

Persistent memory hash indexes

2021 ◽  
Vol 14 (5) ◽  
pp. 785-798
Author(s):  
Daokun Hu ◽  
Zhiwen Chen ◽  
Jianbing Wu ◽  
Jianhua Sun ◽  
Hao Chen
Keyword(s):  
Future Development ◽  
High Performance ◽  
Performance Metrics ◽  
Comprehensive Evaluation ◽  
State Of The Art ◽  
Hash Tables ◽  
Trade Offs ◽  
Depth Analysis ◽  
Persistent Memory ◽  
Memory Modules

Persistent memory (PM) is increasingly being leveraged to build hash-based indexing structures featuring cheap persistence, high performance, and instant recovery, especially with the recent release of Intel Optane DC Persistent Memory Modules. However, most of them are evaluated on DRAM-based emulators with unreal assumptions, or focus on the evaluation of specific metrics with important properties sidestepped. Thus, it is essential to understand how well the proposed hash indexes perform on real PM and how they differentiate from each other if a wider range of performance metrics are considered. To this end, this paper provides a comprehensive evaluation of persistent hash tables. In particular, we focus on the evaluation of six state-of-the-art hash tables including Level hashing, CCEH, Dash, PCLHT, Clevel, and SOFT, with real PM hardware. Our evaluation was conducted using a unified benchmarking framework and representative workloads. Besides characterizing common performance properties, we also explore how hardware configurations (such as PM bandwidth, CPU instructions, and NUMA) affect the performance of PM-based hash tables. With our in-depth analysis, we identify design trade-offs and good paradigms in prior arts, and suggest desirable optimizations and directions for the future development of PM-based hash tables.

scholarly journals ConnectIt

2020 ◽  
Vol 14 (4) ◽  
pp. 653-667
Author(s):  
Laxman Dhulipala ◽  
Changwan Hong ◽  
Julian Shun
Keyword(s):  
Experimental Evaluation ◽  
Comprehensive Evaluation ◽  
State Of The Art ◽  
Graph Connectivity ◽  
Connected Components ◽  
Sampling Strategies ◽  
Spanning Forest ◽  
Speed Up ◽  
Minimum Spanning Forest ◽  
Edge Sampling

Connected components is a fundamental kernel in graph applications. The fastest existing multicore algorithms for solving graph connectivity are based on some form of edge sampling and/or linking and compressing trees. However, many combinations of these design choices have been left unexplored. In this paper, we design the ConnectIt framework, which provides different sampling strategies as well as various tree linking and compression schemes. ConnectIt enables us to obtain several hundred new variants of connectivity algorithms, most of which extend to computing spanning forest. In addition to static graphs, we also extend ConnectIt to support mixes of insertions and connectivity queries in the concurrent setting. We present an experimental evaluation of ConnectIt on a 72-core machine, which we believe is the most comprehensive evaluation of parallel connectivity algorithms to date. Compared to a collection of state-of-the-art static multicore algorithms, we obtain an average speedup of 12.4x (2.36x average speedup over the fastest existing implementation for each graph). Using ConnectIt, we are able to compute connectivity on the largest publicly-available graph (with over 3.5 billion vertices and 128 billion edges) in under 10 seconds using a 72-core machine, providing a 3.1x speedup over the fastest existing connectivity result for this graph, in any computational setting. For our incremental algorithms, we show that our algorithms can ingest graph updates at up to several billion edges per second. To guide the user in selecting the best variants in ConnectIt for different situations, we provide a detailed analysis of the different strategies. Finally, we show how the techniques in ConnectIt can be used to speed up two important graph applications: approximate minimum spanning forest and SCAN clustering.

scholarly journals Disentangled Feature Learning Network for Vehicle Re-Identification

2020 ◽  
Author(s):  
Yan Bai ◽  
Yihang Lou ◽  
Yongxing Dai ◽  
Jun Liu ◽  
Ziqian Chen ◽  
...  
Keyword(s):  
State Of The Art ◽  
Feature Learning ◽  
Feature Representation ◽  
Public Security ◽  
The Public ◽  
Common Features ◽  
Learning Network ◽  
Single Feature ◽  
Art Performance

Vehicle Re-Identification (ReID) has attracted lots of research efforts due to its great significance to the public security. In vehicle ReID, we aim to learn features that are powerful in discriminating subtle differences between vehicles which are visually similar, and also robust against different orientations of the same vehicle. However, these two characteristics are hard to be encapsulated into a single feature representation simultaneously with unified supervision. Here we propose a Disentangled Feature Learning Network (DFLNet) to learn orientation specific and common features concurrently, which are discriminative at details and invariant to orientations, respectively. Moreover, to effectively use these two types of features for ReID, we further design a feature metric alignment scheme to ensure the consistency of the metric scales. The experiments show the effectiveness of our method that achieves state-of-the-art performance on three challenging datasets.

scholarly journals An Arabic Dataset for Disease Named Entity Recognition with Multi-Annotation Schemes

Data ◽  
2020 ◽  
Vol 5 (3) ◽  
pp. 60
Author(s):  
Nasser Alshammari ◽  
Saad Alanazi
Keyword(s):  
Language Processing ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Definite Article ◽  
Linguistic Features ◽  
Annotation Scheme ◽  
Named Entity ◽  
Part Of Speech ◽  
Annotation Process ◽  
Arabic Natural Language Processing

This article outlines a novel data descriptor that provides the Arabic natural language processing community with a dataset dedicated to named entity recognition tasks for diseases. The dataset comprises more than 60 thousand words, which were annotated manually by two independent annotators using the inside–outside (IO) annotation scheme. To ensure the reliability of the annotation process, the inter-annotator agreements rate was calculated, and it scored 95.14%. Due to the lack of research efforts in the literature dedicated to studying Arabic multi-annotation schemes, a distinguishing and a novel aspect of this dataset is the inclusion of six more annotation schemes that will bridge the gap by allowing researchers to explore and compare the effects of these schemes on the performance of the Arabic named entity recognizers. These annotation schemes are IOE, IOB, BIES, IOBES, IE, and BI. Additionally, five linguistic features, including part-of-speech tags, stopwords, gazetteers, lexical markers, and the presence of the definite article, are provided for each record in the dataset.

scholarly journals Uniformity Attentive Learning-Based Siamese Network for Person Re-Identification

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3603
Author(s):  
Dasol Jeong ◽  
Hasil Park ◽  
Joongchol Shin ◽  
Donggoo Kang ◽  
Joonki Paik
Keyword(s):  
Large Scale ◽  
Body Shape ◽  
State Of The Art ◽  
The State ◽  
Whole Body ◽  
Distinctive Features ◽  
Common Features ◽  
Siamese Network ◽  
Art Methods ◽  
Triplet Loss

Person re-identification (Re-ID) has a problem that makes learning difficult such as misalignment and occlusion. To solve these problems, it is important to focus on robust features in intra-class variation. Existing attention-based Re-ID methods focus only on common features without considering distinctive features. In this paper, we present a novel attentive learning-based Siamese network for person Re-ID. Unlike existing methods, we designed an attention module and attention loss using the properties of the Siamese network to concentrate attention on common and distinctive features. The attention module consists of channel attention to select important channels and encoder-decoder attention to observe the whole body shape. We modified the triplet loss into an attention loss, called uniformity loss. The uniformity loss generates a unique attention map, which focuses on both common and discriminative features. Extensive experiments show that the proposed network compares favorably to the state-of-the-art methods on three large-scale benchmarks including Market-1501, CUHK03 and DukeMTMC-ReID datasets.

scholarly journals What Can Be Accomplished with the State of the Art in Information Extraction? A Personal View

2018 ◽  
Vol 44 (4) ◽  
pp. 651-658
Author(s):  
Ralph Weischedel ◽  
Elizabeth Boschee
Keyword(s):  
Information Extraction ◽  
State Of The Art ◽  
The State ◽  
Personal View ◽  
Common Features ◽  
Research Challenges ◽  
The Common

Though information extraction (IE) research has more than a 25-year history, F1 scores remain low. Thus, one could question continued investment in IE research. In this article, we present three applications where information extraction of entities, relations, and/or events has been used, and note the common features that seem to have led to success. We also identify key research challenges whose solution seems essential for broader successes. Because a few practical deployments already exist and because breakthroughs on particular challenges would greatly broaden the technology’s deployment, further R&D investments are justified.

Topical Review: State of the Field of Child Self-Report of Acute Pain

2019 ◽  
Author(s):  
Lindsey L Cohen ◽  
Matthew R Donati ◽  
Sharon Shih ◽  
Soumitri Sil
Keyword(s):  
Acute Pain ◽  
Pain Assessment ◽  
Comprehensive Evaluation ◽  
Clinical Care ◽  
Preschool Age ◽  
Self Report ◽  
Common Features ◽  
Topical Review ◽  
Review State ◽  
State Of The Field

Abstract Objective Children experience acute pain with routine and emergent healthcare, and untreated pain can lead to a range of repercussions. Assessment is vital to diagnosing and treating acute pain. Given the internal nature of pain, self-report is predominant. This topical review reflects on the state of the field of pediatric acute pain self-report, and proposes a framework for acute pain assessment via self-report. Method We examine self-report of acute pain in preschool-age children through adolescents, and we detail a three-step process to optimize acute pain assessment. Results The first step is to decide between a pain screening or assessment. Several 0–10 self-report scales are available for pain screenings. Assessment requires specification of the goals and domains to target. Core criteria, common features, modulating factors, and consequences of acute pain provide a framework for a comprehensive pain assessment. Whereas there are some measures available to assess aspects of these domains, there are considerable gaps. Last, it is important to integrate the data to guide clinical care of acute pain. Conclusions Self-report of acute pain is dominated by single-item intensity scales, which are useful for pain screening but inadequate for pain assessment. We propose a three-step approach to acute pain assessment in children. However, there is a need for measure development for a comprehensive evaluation of the core criteria, common features, modulating factors, and consequences of pediatric acute pain. In addition, there is limited guidance in merging data found in multifaceted evaluations of pediatric acute pain.

scholarly journals A PDTB-styled end-to-end discourse parser

2012 ◽  
Vol 20 (2) ◽  
pp. 151-184 ◽  
Author(s):  
ZIHENG LIN ◽  
HWEE TOU NG ◽  
MIN-YEN KAN
Keyword(s):  
Comprehensive Evaluation ◽  
State Of The Art ◽  
Research Work ◽  
Pipeline Architecture ◽  
Multiple Components ◽  
Current State ◽  
Discourse Relations ◽  
Data Driven Approach ◽  
End To End ◽  
Discourse Connectives

AbstractSince the release of the large discourse-level annotation of the Penn Discourse Treebank (PDTB), research work has been carried out on certain subtasks of this annotation, such as disambiguating discourse connectives and classifying Explicit or Implicit relations. We see a need to construct a full parser on top of these subtasks and propose a way to evaluate the parser. In this work, we have designed and developed an end-to-end discourse parser-to-parse free texts in the PDTB style in a fully data-driven approach. The parser consists of multiple components joined in a sequential pipeline architecture, which includes a connective classifier, argument labeler, explicit classifier, non-explicit classifier, and attribution span labeler. Our trained parser first identifies all discourse and non-discourse relations, locates and labels their arguments, and then classifies the sense of the relation between each pair of arguments. For the identified relations, the parser also determines the attribution spans, if any, associated with them. We introduce novel approaches to locate and label arguments, and to identify attribution spans. We also significantly improve on the current state-of-the-art connective classifier. We propose and present a comprehensive evaluation from both component-wise and error-cascading perspectives, in which we illustrate how each component performs in isolation, as well as how the pipeline performs with errors propagated forward. The parser gives an overall system F1 score of 46.80 percent for partial matching utilizing gold standard parses, and 38.18 percent with full automation.

scholarly journals Mostly-unsupervised statistical segmentation of Japanese kanji sequences

2003 ◽  
Vol 9 (2) ◽  
pp. 127-149 ◽  
Author(s):  
RIE KUBOTA ANDO ◽  
LILLIAN LEE
Keyword(s):  
State Of The Art ◽  
Training Data ◽  
Syntactic Analysis ◽  
Annotation Scheme ◽  
Error Metrics ◽  
Japanese Word ◽  
Robust Statistical Method ◽  
Segmentation Algorithms ◽  
Statistical Segmentation ◽  
Multiple Granularities

Given the lack of word delimiters in written Japanese, word segmentation is generally considered a crucial first step in processing Japanese texts. Typical Japanese segmentation algorithms rely either on a lexicon and syntactic analysis or on pre-segmented data; but these are labor-intensive, and the lexico-syntactic techniques are vulnerable to the unknown word problem. In contrast, we introduce a novel, more robust statistical method utilizing unsegmented training data. Despite its simplicity, the algorithm yields performance on long kanji sequences comparable to and sometimes surpassing that of state-of-the-art morphological analyzers over a variety of error metrics. The algorithm also outperforms another mostly-unsupervised statistical algorithm previously proposed for Chinese. Additionally, we present a two-level annotation scheme for Japanese to incorporate multiple segmentation granularities, and introduce two novel evaluation metrics, both based on the notion of a compatible bracket, that can account for multiple granularities simultaneously.

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