Automatic Tagging System Research Articles

Tagging Items with Emerging Tags: A Neural Topic Model Based Few-Shot Learning Approach

The tagging system has become a primary tool to organize information resources on the Internet, which benefits both users and the platforms. To build a successful tagging system, automatic tagging methods are desired. With the development of society, new tags keep emerging. The problem of tagging items with emerging tags is an open challenge for an automatic tagging system, and it has not been well studied in the literature. We define this problem as a tag-centered cold-start problem in this study and propose a novel neural topic model based few-shot learning method named NTFSL to solve the problem. In our proposed method, we innovatively fuse the topic modeling task with the few-shot learning task, endowing the model with the capability to infer effective topics to solve the tag-centered cold-start problem with the property of interpretability. Meanwhile, we propose a novel neural topic model for the topic modeling task to improve the quality of inferred topics, which helps enhance the tagging performance. Furthermore, we develop a novel inference method based on the variational auto-encoding framework for model inference. We conducted extensive experiments on two real-world datasets, and the results demonstrate the superior performance of our proposed model compared with state-of-the-art machine learning methods. Case studies also show the interpretability of the model.

Validation of an Automatic Tagging System for Identifying Respiratory and Hemodynamic Deterioration Events in the Intensive Care Unit.

ObjectivesPredictive models for critical events in the intensive care unit (ICU) might help providers anticipate patient deterioration. At the heart of predictive model development lies the ability to accurately label significant events, thereby facilitating the use of machine learning and similar strategies. We conducted this study to establish the validity of an automated system for tagging respiratory and hemodynamic deterioration by comparing automatic tags to tagging by expert reviewers.MethodsThis retrospective cohort study included 72,650 unique patient stays collected from Electronic Medical Records of the University of Massachusetts’ eICU. An enriched subgroup of stays was manually tagged by expert reviewers. The tags generated by the reviewers were compared to those generated by an automated system.ResultsThe automated system was able to rapidly and efficiently tag the complete database utilizing available clinical data. The overall agreement rate between the automated system and the clinicians for respiratory and hemodynamic deterioration tags was 89.4% and 87.1%, respectively. The automatic system did not add substantial variability beyond that seen among the reviewers.ConclusionsWe demonstrated that a simple rule-based tagging system could provide a rapid and accurate tool for mass tagging of a compound database. These types of tagging systems may replace human reviewers and save considerable resources when trying to create a validated, labeled database used to train artificial intelligence algorithms. The ability to harness the power of artificial intelligence depends on efficient clinical validation of targeted conditions; hence, these systems and the methodology used to validate them are crucial.

Stackoverflow tag prediction using tag associations and code analysis

Stackoverflow is a platform or forum for people to gain knowledge, get solutions and interact on topics related to different programming concepts. Stackoverflow is one of the biggest Question and Answer forums with more than a million users. There is a wide variety of questions covered on this platform that are segmented with appropriate tags. The user by default enters the tag manually. Due to an enormous number of tags, it becomes difficult to search for the correct tag, many often left unconsidered. An automatic tagging system can be employed that shows likely tags depending on the text entered. The objective dataset chosen by us consists of about 0.5 million such questions, which can be technical or non-technical, collected from various information-dense websites, primarily Stackoverflow. We introduce a tag association scheme (TagAssc) along with code-analysis and strategic tag sampling schemes to get better accuracy scores in predicting tags with limited computing resources.

Automatic Tagging System Based on Mivar Technologies

В данной статье рассматривается проблема создания системы автоматического тегирования изображений. В статье приведен обзор существующих задач и программных решений в области классификации объектов на изображении. Для решения задачи распознавания множества объектов на изображениях предлагается реализовать модули снятия контекстной неопределенности и управляющий модуль, производящий интеллектуальный запуск остальных модулей системы. Данные модули используют миварные технологии: миварную базу знаний и миварный метод логического вывода. В работе приведена модульная схема системы, описание работы модулей системы, а также полученные результаты.

A Bag of Systems Representation for Music Auto-Tagging

We present a content-based automatic tagging system for music that relies on a high-level, concise “Bag of Systems” (BoS) representation of the characteristics of a musical piece. The BoS representation leverages a rich dictionary of musical codewords, where each codeword is a generative model that captures timbral and temporal characteristics of music. Songs are represented as a BoS histogram over codewords, which allows for the use of traditional algorithms for text document retrieval to perform auto-tagging. Compared to estimating a single generative model to directly capture the musical characteristics of songs associated with a tag, the BoS approach offers the flexibility to combine different generative models at various time resolutions through the selection of the BoS codewords. Additionally, decoupling the modeling of audio characteristics from the modeling of tag-specific patterns makes BoS a more robust and rich representation of music. Experiments show that this leads to superior auto-tagging performance.

Extracting meaning from temporal nouns and temporal prepositions

This article provides a compositional semantics for temporal nouns and temporal prepositions that are annotated as temporal prepositional phrases or noun phrases by an automatic tagging system (e.g., last Monday, on Dec. 1 st , for three weeks or before Christmas ). Current temporal tagging systems rely on an ad-hoc-representation for temporal date and time expressions, but the more demanding tasks of temporal question-answering and automatic text summarization require a sound logical derivation and representation of temporal expressions. Our proposal draws from two formal accounts of temporal prepositional phrases by Pratt and Francez [2001] and von Stechow [2002b], and is realized within an automatic temporal tagging system for German newspaper articles.