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TempEval-3: Evaluating Events, Time Expressions, and Temporal Relations  [PDF]
Naushad UzZaman,Hector Llorens,James Allen,Leon Derczynski,Marc Verhagen,James Pustejovsky
Computer Science , 2012,
Abstract: We describe the TempEval-3 task which is currently in preparation for the SemEval-2013 evaluation exercise. The aim of TempEval is to advance research on temporal information processing. TempEval-3 follows on from previous TempEval events, incorporating: a three-part task structure covering event, temporal expression and temporal relation extraction; a larger dataset; and single overall task quality scores.
Clinical TempEval  [PDF]
Steven Bethard,Leon Derczynski,James Pustejovsky,Marc Verhagen
Computer Science , 2014,
Abstract: We describe the Clinical TempEval task which is currently in preparation for the SemEval-2015 evaluation exercise. This task involves identifying and describing events, times and the relations between them in clinical text. Six discrete subtasks are included, focusing on recognising mentions of times and events, describing those mentions for both entity types, identifying the relation between an event and the document creation time, and identifying narrative container relations.
ManTIME: Temporal expression identification and normalization in the TempEval-3 challenge  [PDF]
Michele Filannino,Gavin Brown,Goran Nenadic
Computer Science , 2013,
Abstract: This paper describes a temporal expression identification and normalization system, ManTIME, developed for the TempEval-3 challenge. The identification phase combines the use of conditional random fields along with a post-processing identification pipeline, whereas the normalization phase is carried out using NorMA, an open-source rule-based temporal normalizer. We investigate the performance variation with respect to different feature types. Specifically, we show that the use of WordNet-based features in the identification task negatively affects the overall performance, and that there is no statistically significant difference in using gazetteers, shallow parsing and propositional noun phrases labels on top of the morphological features. On the test data, the best run achieved 0.95 (P), 0.85 (R) and 0.90 (F1) in the identification phase. Normalization accuracies are 0.84 (type attribute) and 0.77 (value attribute). Surprisingly, the use of the silver data (alone or in addition to the gold annotated ones) does not improve the performance.
Annotating Synapses in Large EM Datasets  [PDF]
Stephen M. Plaza,Toufiq Parag,Gary B. Huang,Donald J. Olbris,Mathew A. Saunders,Patricia K. Rivlin
Computer Science , 2014,
Abstract: Reconstructing neuronal circuits at the level of synapses is a central problem in neuroscience and becoming a focus of the emerging field of connectomics. To date, electron microscopy (EM) is the most proven technique for identifying and quantifying synaptic connections. As advances in EM make acquiring larger datasets possible, subsequent manual synapse identification ({\em i.e.}, proofreading) for deciphering a connectome becomes a major time bottleneck. Here we introduce a large-scale, high-throughput, and semi-automated methodology to efficiently identify synapses. We successfully applied our methodology to the Drosophila medulla optic lobe, annotating many more synapses than previous connectome efforts. Our approaches are extensible and will make the often complicated process of synapse identification accessible to a wider-community of potential proofreaders.
iTAG: Automatically Annotating Textual Resources with Human Intentions  [cached]
Mark Kr?ll,Christian K?rner,Markus Strohmaier
Journal of Emerging Technologies in Web Intelligence , 2010, DOI: 10.4304/jetwi.2.4.333-342
Abstract: Annotations represent an increasingly popular means for organizing, categorizing and finding resources on the “social” web. Yet, only a small portion of the total resources available on the web are annotated. Work on automatic tag generation algorithms aims to tackle this problem by developing algorithms that attempt to approximate and support human tagging behavior. While existing algorithms largely focus on automatically describing the general topics covered by a resource (such as “career”, “education”), we suggest focusing on a different tagging dimension: i.e. automatically annotating resources with human intentions. Intent annotations aim to describe which goals are referenced in given textual resources (such as “find a job”, “get a degree”), thereby offering a new, interesting perspective on textual resources on the web. We describe a prototype – iTAG – for automatically annotating textual resources with human intent, and investigate the extent to which the automatic analysis of human intentions in textual resources is feasible. For evaluation purposes, we present results from an exploratory study that focused on annotating intent in transcript
An Ontology Based Approach for Automatically Annotating Document Segments
Maryam Hazman,Samhaa R. El-Beltagy,Ahmed Rafea
International Journal of Computer Science Issues , 2012,
Abstract: This paper presents an approach for automatically annotating document segments within information rich texts using a domain ontology. The work exploits the logical structure of input documents in order to achieve its task. The underlying assumption behind this work is that segments in such documents embody self contained informative units. Another assumption is that segment headings coupled with a documents hierarchical structure offer informal representations of segment content; and that matching segment headings to concepts in an ontology/thesaurus can result in the creation of formal labels/meta-data for these segments. A series of experiments was carried out using the presented approach on a set of Arabic agricultural extension documents. The results of carrying out these experiments demonstrate that the proposed approach is capable of automatically annotating segments with concepts that describe a segments content with a high degree of accuracy.
SmartAnnotator: An Interactive Tool for Annotating RGBD Indoor Images  [PDF]
Yu-Shiang Wong,Hung-Kuo Chu,Niloy J. Mitra
Computer Science , 2014,
Abstract: RGBD images with high quality annotations in the form of geometric (i.e., segmentation) and structural (i.e., how do the segments are mutually related in 3D) information provide valuable priors to a large number of scene and image manipulation applications. While it is now simple to acquire RGBD images, annotating them, automatically or manually, remains challenging especially in cluttered noisy environments. We present SmartAnnotator, an interactive system to facilitate annotating RGBD images. The system performs the tedious tasks of grouping pixels, creating potential abstracted cuboids, inferring object interactions in 3D, and comes up with various hypotheses. The user simply has to flip through a list of suggestions for segment labels, finalize a selection, and the system updates the remaining hypotheses. As objects are finalized, the process speeds up with fewer ambiguities to resolve. Further, as more scenes are annotated, the system makes better suggestions based on structural and geometric priors learns from the previous annotation sessions. We test our system on a large number of database scenes and report significant improvements over naive low-level annotation tools.
Annotating Cognates and Etymological Origin in Turkic Languages  [PDF]
Benjamin S. Mericli,Michael Bloodgood
Computer Science , 2015,
Abstract: Turkic languages exhibit extensive and diverse etymological relationships among lexical items. These relationships make the Turkic languages promising for exploring automated translation lexicon induction by leveraging cognate and other etymological information. However, due to the extent and diversity of the types of relationships between words, it is not clear how to annotate such information. In this paper, we present a methodology for annotating cognates and etymological origin in Turkic languages. Our method strives to balance the amount of research effort the annotator expends with the utility of the annotations for supporting research on improving automated translation lexicon induction.
Annotating Video with Open Educational Resources in a Flipped Classroom Scenario  [PDF]
Olivier Aubert,Joscha Jaeger
Computer Science , 2014,
Abstract: A wealth of Open Educational Resources is now available, and beyond the first and evident problem of finding them, the issue of articulating a set of resources is arising. When using audiovisual resources, among different possibilities, annotating a video resource with additional resources linked to specific fragments can constitute one of the articulation modalities. Annotating a video is a complex task, and in a pedagogical context, intermediary activities should be proposed in order to mitigate this complexity. In this paper, we describe a tool dedicated to supporting video annotation activities. It aims at improving learner engagement, by having students be more active when watching videos by offering a progressive annotation process, first guided by providing predefined resources, then more freely, to accompany users in the practice of annotating videos.
Collecting and Annotating the Large Continuous Action Dataset  [PDF]
Daniel Paul Barrett,Ran Xu,Haonan Yu,Jeffrey Mark Siskind
Computer Science , 2015,
Abstract: We make available to the community a new dataset to support action-recognition research. This dataset is different from prior datasets in several key ways. It is significantly larger. It contains streaming video with long segments containing multiple action occurrences that often overlap in space and/or time. All actions were filmed in the same collection of backgrounds so that background gives little clue as to action class. We had five humans replicate the annotation of temporal extent of action occurrences labeled with their class and measured a surprisingly low level of intercoder agreement. A baseline experiment shows that recent state-of-the-art methods perform poorly on this dataset. This suggests that this will be a challenging dataset to foster advances in action-recognition research. This manuscript serves to describe the novel content and characteristics of the LCA dataset, present the design decisions made when filming the dataset, and document the novel methods employed to annotate the dataset.
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