Semi-automatic Annotation Tool Research Articles

An Investigation Into English Learners’ Speech Acts Using a Semi-Automatic Annotation Tool

This paper explores the speech acts most frequently performed by learners of English as a foreign language (EFL) through the Dialogue Annotation and Research Tool (DART), a semi-automatic annotation tool. The study used learner corpus research and an English as the first language (L1) speaker corpus as a reference to compare the speech acts performed by the EFL learners. The study involved 90 EFL learners of four nationalities. A spoken learner corpus was built with the dyadic interlanguage English conversation register. The most frequently performed speech acts were generally different for the English L1 speakers and the EFL learners. The speech acts labeled as correctSelf, referProcess, expressNonAwareness, and stateReason were in the top 10 for EFL learners, whereas they were not in the top 10 for the English L1 speakers. This difference was caused by three factors: the English proficiency of the EFL learners, the frequently used formulaic phrases, and the task requirements. Moreover, the corpus annotation has a problem with unrecognized speech acts, primarily due to the redundancies and fragments in the learner discourses. The findings of this study reveal the complementary relationship between grammatical and pragmatic performances.

The Use of Semi-automatic Annotation in Speech Acts Performed by Learners of English

Since rare research studied speech acts (SAs) from a macro perspective, this study used a semi-automatic annotation tool named Dialogue Annotation and Research Tool (DART) to examine the most frequently performed SAs by Chinese and Thai learners of English as a foreign language (EFL). It also attempted to reveal the reasons for the similarities and differences in the SA performances of EFL learners from discrete linguacultural backgrounds. This study involved 30 Chinese and 30 Thai EFL learners, totaling 60 participants. A total of 30 dyadic English interlanguage conversations were collected for this study. The learner corpus research with the contrastive interlanguage analysis was adopted for the analysis. The DART annotation revealed that both Chinese and Thai EFL learners most frequently performed the same six SAs. These SAs were labeled by DART as state, hesitate, reqInfo, answer, expressOpinion, and stateReason. Of the six listed SAs, both Chinese and Thai EFL learners most frequently performed the SA labeled state; both cohorts also presented the SA stateReason least often. Task requirements, the frequent use of certain types of formulaic language, and English proficiency levels were ascertained as factors causing the identical presentation of the six identified SAs. The English proficiency levels of Chinese EFL learners were determined as the principal reason for their discrete performance of SAs. Conversely, the influence of the Thai linguacultural background denoted the primary factor for discrepancies in the SA performance of Thai EFL learners.

Machine learning based quantitative characterization of microstructures

Identifying structure–property relationships of polycrystalline microstructures demands an accurate and precise quantification of their features. Measuring grain sizes is tedious and creates a non-transparent bias when being performed by different individuals, impairing comparability with references. Here, we present a novel use of region-based convolutional neural networks (R-CNNs) to quantify several microstructural characteristics and their distributions: Feret diameter, axis length, area, circumference, dihedral angle and coordination number. We utilize a two-step approach: (i) a semi-automatic annotation tool to generate training data for (ii) a fully automated R-CNN, quantitatively evaluating images. Using Al-doped ZnO as a model system, we trained two R-CNNs, one for ZnO and one for precipitated ZnAl2O4. The R-CNN performs well in evaluating grain size characteristics from images with low contrast and in differentiating uni-, and bimodal grain size distributions on the sub-micron, and nanoscale. An extended statistical analysis of the distributions is performed to extract microstructural parameters quantitatively. This innovative solution makes grain size measuring amenable, time-effective, less biased, consistent, and statistically more precise.

Semi-automation of gesture annotation by machine learning and human collaboration

Gesture and multimodal communication researchers typically annotate video data manually, even though this can be a very time-consuming task. In the present work, a method to detect gestures is proposed as a fundamental step towards a semi-automatic gesture annotation tool. The proposed method can be applied to RGB videos and requires annotations of part of a video as input. The technique deploys a pose estimation method and active learning. In the experiment, it is shown that if about 27% of the video is annotated, the remaining parts of the video can be annotated automatically with an F-score of at least 0.85. Users can run this tool with a small number of annotations first. If the predicted annotations for the remainder of the video are not satisfactory, users can add further annotations and run the tool again. The code has been released so that other researchers and practitioners can use the results of this research. This tool has been confirmed to work in conjunction with ELAN.

Defect attention template generation cycleGAN for weakly supervised surface defect segmentation

Surface defect segmentation is very important for the quality inspection of industrial production and is an important pattern recognition problem. Although deep learning (DL) has achieved remarkable results in surface defect segmentation, most of these results have been obtained by using massive images with pixel-level annotations, which are difficult to obtain at industrial sites. This paper proposes a weakly supervised defect segmentation method based on the dynamic templates generated by an improved cycle-consistent generative adversarial network (CycleGAN) trained by image-level annotations. To generate better templates for defects with weak signals, we propose a defect attention module by applying the defect residual for the discriminator to strengthen the elimination of defect regions and suppress changes in the background. A defect cycle-consistent loss is designed by adding structural similarity (SSIM) to the original L1 loss to include the grayscale and structural features; the proposed loss can better model the inner structure of defects. After obtaining the defect-free template, a defect segmentation map can easily be obtained through a simple image comparison and threshold segmentation. Experiments show that the proposed method is both efficient and effective, significantly outperforms other weakly supervised methods, and achieves performance that is comparable or even superior to that of supervised methods on three industrial datasets (intersection over union (IoU) on the DAGM 2007, KSD and CCSD datasets of 78.28%, 59.43%,and 68.83%, respectively). The proposed method can also be employed as a semiautomatic annotation tool combined with active learning.

Validation methods for geometric camera calibration

We have developed a semi-automatic annotation tool – “CVL Annotator” – for bounding box ground truth generation in videos. Our research is particularly motivated by the need for reference annotations of challenging nighttime traffic scenes with highly dynamic lighting conditions due to reflections, headlights and halos from oncoming traffic. Our tool incorporates a suite of different state-of-the-art tracking algorithms in order to minimize the amount of human input necessary to generate high-quality ground truth data. We focus our user interface on the premise of minimizing user interaction and visualizing all information relevant to the user at a glance. We perform a preliminary user study to measure the amount of time and clicks necessary to produce ground truth annotations of video traffic scenes and evaluate the accuracy of the final annotation results.

SAnE: Smart Annotation and Evaluation Tools for Point Cloud Data

Addressing the need for high-quality, time efficient, and easy to use annotation tools, we propose SAnE, a semiautomatic annotation tool for labeling point cloud data. The contributions of this paper are threefold: (1) we propose a denoising pointwise segmentation strategy enabling a fast implementation of one-click annotation, (2) we expand the motion model technique with our guided-tracking algorithm, and (3) we provide an interactive, yet robust, open-source point cloud annotation tool, targeting both skilled and crowdsourcing annotators. Using the KITTI dataset, we show that the SAnE speeds up the annotation process by a factor of 4 while achieving Intersection over Union (IoU) agreements of 84%. Furthermore, in experiments using crowdsourcing services, SAnE achieves more than 20% higher IoU accuracy compared to the existing annotation tool and its baseline, while reducing the annotation time by a factor of 3. This result shows the potential of SAnE, for providing fast and accurate annotation labels for large-scale datasets with a significantly reduced price. SAnE is open-sourced at https://github.com/hasanari/sane.

A semi-automatic annotation tool for unobtrusive gesture analysis

In a variety of research fields, including linguistics, human–computer interaction research, psychology, sociology and behavioral studies, there is a growing interest in the role of gestural behavior related to speech and other modalities. The analysis of multimodal communication requires high-quality video data and detailed annotation of the different semiotic resources under scrutiny. In the majority of cases, the annotation of hand position, hand motion, gesture type, etc. is done manually, which is a time-consuming enterprise requiring multiple annotators and substantial resources. In this paper we present a semi-automatic alternative, in which the focus lies on minimizing the manual workload while guaranteeing highly accurate annotations. First, we discuss our approach, which consists of several processing steps such as identifying the hands in images, calculating motion of the hands, segmenting the recording in gesture and non-gesture events, etc. Second, we validate our approach against existing corpora in terms of accuracy and usefulness. The proposed approach is designed to provide annotations according to the McNeill (Hand and mind: what gestures reveal about thought, University of Chicago Press, Chicago, 1992) gesture space and the output is compatible with annotation tools such as ELAN or ANVIL.

A semantic annotation framework for scientific publications

Considering the growing volume of scientific literature, techniques that enable automatic detection of informational entities existing in scientific research articles may contribute to the extension of scientific knowledge and practical usages. Although there have been several efforts to extract informative entities from patent and biomedical research articles, there are few attempts in other scientific literatures. In this paper, we introduce an automatic semantic annotation framework for research articles based on entity recognition techniques. Our approach includes tag set modeling for semantic annotation, semi-automatic annotation tool, manual annotation for training data preparation, and supervised machine learning to develop entity type recognition module. For experiments, we choose two different domains, such as information and communication technology and chemical engineering due to their high usages. In addition, we provide three application scenarios of how our annotation framework can be used and extended further. It is to guide potential researchers who are willing to link their own contents with external data.

Comparison of a semi-automatic annotation tool and a natural language processing application for the generation of clinical statement entries

Electronic medical records with encoded entries should enhance the semantic interoperability of document exchange. However, it remains a challenge to encode the narrative concept and to transform the coded concepts into a standard entry-level document. This study aimed to use a novel approach for the generation of entry-level interoperable clinical documents. Using HL7 clinical document architecture (CDA) as the example, we developed three pipelines to generate entry-level CDA documents. The first approach was a semi-automatic annotation pipeline (SAAP), the second was a natural language processing (NLP) pipeline, and the third merged the above two pipelines. We randomly selected 50 test documents from the i2b2 corpora to evaluate the performance of the three pipelines. The 50 randomly selected test documents contained 9365 words, including 588 Observation terms and 123 Procedure terms. For the Observation terms, the merged pipeline had a significantly higher F-measure than the NLP pipeline (0.89 vs 0.80, p<0.0001), but a similar F-measure to that of the SAAP (0.89 vs 0.87). For the Procedure terms, the F-measure was not significantly different among the three pipelines. The combination of a semi-automatic annotation approach and the NLP application seems to be a solution for generating entry-level interoperable clinical documents.

DDBJ launches a new archive database with analytical tools for next-generation sequence data

The DNA Data Bank of Japan (DDBJ) (http://www.ddbj.nig.ac.jp) has collected and released 1 701 110 entries/1 116 138 614 bases between July 2008 and June 2009. A few highlighted data releases from DDBJ were the complete genome sequence of an endosymbiont within protist cells in the termite gut and Cap Analysis Gene Expression tags for human and mouse deposited from the Functional Annotation of the Mammalian cDNA consortium. In this period, we started a novel user announcement service using Really Simple Syndication (RSS) to deliver a list of data released from DDBJ on a daily basis. Comprehensive visualization of a DDBJ release data was attempted by using a word cloud program. Moreover, a new archive for sequencing data from next-generation sequencers, the ‘DDBJ Read Archive’ (DRA), was launched. Concurrently, for read data registered in DRA, a semi-automatic annotation tool called the ‘DDBJ Read Annotation Pipeline’ was released as a preliminary step. The pipeline consists of two parts: basic analysis for reference genome mapping and de novo assembly and high-level analysis of structural and functional annotations. These new services will aid users’ research and provide easier access to DDBJ databases.

Towards a semi-automatic functional annotation tool based on decision-tree techniques

Due to the continuous improvements of high throughput technologies and experimental procedures, the number of sequenced genomes is increasing exponentially. Ultimately, the task of annotating these data relies on the expertise of biologists. The necessity for annotation to be supervised by human experts is the rate limiting step of the data analysis. To face the deluge of new genomic data, the need for automating, as much as possible, the annotation process becomes critical. We consider annotation of a protein with terms of the functional hierarchy that has been used to annotate Bacillus subtilis and propose a set of rules that predict classes in terms of elements of the functional hierarchy, i.e., a class is a node or a leaf of the hierarchy tree. The rules are obtained through two decision-trees techniques: first-order decision-trees and multilabel attribute-value decision-trees, by using as training data the proteins from two lactic bacteria: Lactobacillus sakei and Lactobacillus bulgaricus. We tested the two methods, first independently, then in a combined approach, and evaluated the obtained results using hierarchical evaluation measures. Results obtained for the two approaches on both genomes are comparable and show a good precision together with a high prediction rate. Using combined approaches increases the recall and the prediction rate. The combination of the two approaches is very encouraging and we will further refine these combinations in order to get rules even more useful for the annotators. This first study is a crucial step towards designing a semi-automatic functional annotation tool.

Using Machine Learning Techniques to Reduce Data Annotation Time

Manually annotating large databases in any domain is costly and time-consuming. We present a semi-automatic annotation tool for this purpose that uses Random Forests as bootstrapped classifiers. We describe an application of this tool on a large database of simulated driving data. The tool enables the user to verify automatically generated annotations, rather than annotating from scratch. This tool reduced the amount of time required to annotate one minute of video by a factor of six, down to approximately thirty-five seconds of annotation time per minute of video for a database of simulated driving data. The tool is limited in that its effectiveness is dependent upon the types of data collected, and the statistical boundaries between the different annotations.