Researchers analyze tagging patterns on Stack Exchange communities.
problem Understanding the structure and evolution of tags in Q&A platforms.
method Empirical analysis and development of a generative model for tag co-occurrence.
result The model can reproduce statistical properties of co-tagging graphs.
The paper evaluates machine learning for song similarity based on metadata and user tags.
problem Determining song similarity using metadata and user tags.
method Machine learning algorithms including tf-idf, Word2Vec, k-NN, and SVM were evaluated.
result tf-idf outperformed Word2Vec in modeling song metadata, and k-NN outperformed SVM and Linear Regression.
Improved POS tagging for Twitter data with limited annotations.
problem Low-quality user-generated text for POS tagging.
method Domain adaptation using neural networks with feature embeddings and pre-trained embeddings.
result 90% tagging accuracy on German Tweets.
Community detection in graphs has been extensively studied both in theory and in applications. However, detecting communities in hypergraphs is more challenging. In this paper, we propose a tensor decomposition approach for guaranteed learning of communities in a special class of hypergraphs modeling social tagging sys…
Twitmo analyzes geo-tagged Twitter data for topic modeling and visualization.
problem Analyzing public discourse on Twitter for various topics, parties, or individuals.
method Collects and preprocesses geo-tagged Tweets, applies LDA, CTM, STM, and visualizes results.
result Automatic pooling of Tweets into pseudo-documents improves topic coherence.
Deep neural nets analyze images and text for emotional tagging.
problem Predicting latent emotional states from multimodal data.
method Combining deep neural networks for text and image features.
result Multimodal model outperforms single-modal models.
Neural pedagogical agent updates user models in real-time for mobile education apps.
problem Real-time user modeling for dynamic mobile education platforms.
method Bidirectional recurrent neural networks with attention mechanism over embedded question-response pairs.
result Model outperforms existing approaches in predicting user response correctness.
Modern machine learning methods are critical to the development of large-scale personalized learning systems that cater directly to the needs of individual learners. The recently developed SPARse Factor Analysis (SPARFA) framework provides a new statistical model and algorithms for machine learning-based learning analy…
Tag2Vec learns tag representations in hybrid networks with semantic and hierarchical information.
problem Lack of semantic and hierarchical information in tag networks.
method Tag2Vec model that combines nodes and tags into hybrid networks, using parameterized random walks and hyperbolic Skip-gram model.
result Tag2Vec outperforms other models in learning rich semantic tag representations.
A new deep learning system improves tagging accuracy on various tasks.
problem Improving tagging accuracy on diverse sequence tagging tasks.
method Combines DNN and DRL for augmented tagging, evaluated on ATIS and CoNLL-2003 datasets.
result Outperforms state-of-the-art models by 1.9% on ATIS and 1.4% on CoNLL-2003 datasets.
Recommending items to users is a challenging task due to the large amount of missing information. In many cases, the data solely consist of ratings or tags voluntarily contributed by each user on a very limited subset of the available items, so that most of the data of potential interest is actually missing. Current ap…
Task describes tagging audio clips from 41 categories.
problem Tagging audio clips from diverse categories.
method Baseline system using AudioSet labels.
result Demonstrates feasibility of general-purpose audio tagging.
Paper proposes set-valued prediction for historical POS tagging.
problem Difficult POS tagging in historical corpora due to lack of native speakers and sparse data.
method Set-valued prediction approach to allow uncertainty in tagging.
result Set-valued prediction improves POS tagging precision and robustness.
Forward Automatic Differentiation (AD) is a technique for augmenting programs to compute derivatives. The essence of Forward AD is to attach perturbations to each number, and propagate these through the computation. When derivatives are nested, the distinct derivative calculations, and their associated perturbations, m…
This paper automates tagging programming challenge descriptions.
problem Tagging programming challenge descriptions is tedious for creators.
method Used machine and deep learning methods for automation.
result Deep learning methods outperform traditional IR approaches.
The paper extends influence functions to sequence tagging tasks for better model interpretability.
problem Lack of interpretability methods for sequence tagging models.
method Define and compute influence of training instance segments on test segment predictions.
result The segment influence method tracks with true influence and identifies annotation errors.
Convolutional autoencoders improve personalized recommendations from image-based data.
problem Lack of personalized recommendations in gastronomic platforms using image data.
method Used convolutional autoencoders to extract features from images and improve personalized recommendations.
result Convolutional autoencoders outperform standard deep features in image-based personalized recommendation systems.
Improved algorithm tags B meson flavours in collider experiments.
problem Tagging neutral B mesons' flavours in collider environments. method Probabilistic model combining vertex and track info with machine learning.
result Increases overall performance in flavour tagging.
Model tags biomedical texts with tree-structured tags.
problem Tagging unstructured texts with an arbitrary number of terms from an ontology.
method Neural tree decoding approach starting from the root node of an ontology.
result Outperforms state-of-the-art approaches on MeSH term assignment.
Deep neural network predicts semantic labels for source code.
problem Difficulty in labeling and understanding new programming languages and functionalities.
method Language-agnostic deep convolutional neural network trained on Stack Overflow code snippets.
result Mean area under ROC of 0.957 and top-1 accuracy of 86.6% on Github code documents.
Method learns audio embeddings with contextualized tags.
problem Align audio and tags for cross-modal tasks.
method Audio autoencoder, word embeddings, multi-head self-attention, contrastive loss.
result Multi-head self-attention improves audio representations.
Paper introduces a noisy-labeled audio tagging challenge.
problem Acoustic mismatch and noisy labels in audio tagging.
method Large dataset with minimal supervision, convolutional neural network baseline.
result Demonstrates effectiveness of minimal supervision in noisy conditions.
To date, there have been massive Semi-Structured Documents (SSDs) during the evolution of the Internet. These SSDs contain both unstructured features (e.g., plain text) and metadata (e.g., tags). Most previous works focused on modeling the unstructured text, and recently, some other methods have been proposed to model …
New method predicts target genres from source genres, unifying music tag systems.
problem Automatic genre inference fails to handle music genre diversity and subjectivity.
method Knowledge-based, statistical, and hybrid translation models.
result Hybrid translation model is most effective for multilabel classification.
Generative methods for creating new items for user groups.
problem Creating new items for groups of users with varying preferences.
method Formalized joint problem, used VAE latent space for item generation and user group prediction.
result Generated items similar to highly desirable unobserved items.
In this paper we consider two sequence tagging tasks for medieval Latin: part-of-speech tagging and lemmatization. These are both basic, yet foundational preprocessing steps in applications such as text re-use detection. Nevertheless, they are generally complicated by the considerable orthographic variation which is ty…
We study the cluster categories arising from marked surfaces (with punctures and non-empty boundaries). By constructing skewed-gentle algebras, we show that there is a bijection between tagged curves and string objects. Applications include interpreting dimensions of Ext1 as intersection numbers of ta…
Quantum analysis tags news for sentiment and entities.
problem Identifying bias in news reporting.
method Continuous data collection, NER and sentiment analysis.
result A corpus of tagged news articles for public use.
This paper creates a tagging system for paintings using historical descriptions.
problem Tagging accuracy for paintings over time with varying expert descriptions.
method A neural network with frequent itemsets as tags, followed by clustering.
result Improved tagging accuracy for paintings over time.
MuLan links music audio to natural language tags.
problem Traditional music tagging systems use rigid attributes; MuLan aims to link audio directly to natural language.
method Joint audio-text embedding model trained on 44 million music recordings and text annotations.
result MuLan's embeddings enable zero-shot functionalities and transfer learning.
This study uses Twitter to analyze traveler behavior in Manhattan.
problem Analyzing traveler behavior using social media data.
method Systematic method to extract displacement information from geo-tagged tweets.
result Twitter reveals unique demographics and travel behavior patterns.
GTI network learns linguistic features for multi-task sequence tagging.
problem Improving neural model performance on multi-task sequence tagging without explicit features.
method GTI network with neural gate modules to learn relations between tasks.
result GTI network outperforms baselines on chunking and NER tasks.
Paper proposes a new method for better POS tagging adaptation.
problem Difficulty of pre-trained units learning target-specific patterns.
method Augment target-network with normalised, weighted, and randomly initialized units.
result Achieves state-of-the-art performances on POS tagging.
USTAR combines multiple social media modalities to model user-guided activities.
problem Lack of comprehensive spatiotemporal activity models using all social media modalities.
method Online learning method embedding locations, time, text, and users into a single space, incorporating NGTSM and GTSM records, and using collaborative filtering.
result USTAR significantly improves region and keyword retrieval compared to state-of-the-art methods.
New method tags malware for better understanding and identification.
problem Lack of detailed descriptions and relationships between malware samples.
method Deep learning model generates human-interpretable descriptions and similarity index.
result Correctly identifies more than 95% of eleven tag descriptions with a low false positive rate.
Deep learning from manga posters improves anime and manga recommendations.
problem Predicting user preferences for cold-start anime and manga recommendations.
method BALSE model using Illustration2Vec to extract tag information from posters.
result BALSE model significantly improves recommendation quality, especially for less-known mangas.
Study interprets deep learning for LHC jet tagging.
problem Understanding deep learning models in LHC jet tagging.
method Recursive neural networks, comparative study of jet tagging tasks.
result Interesting observations on the latent space of jet tagging models.
Fine-grained event tagging system for SEC 8-K filings improves precision to 96%.
problem Coarse SEC item codes mislabel routine and significant events.
method Two-stage system tagging 8-K disclosures against a 119-event taxonomy.
result LLM judge finds precision rises to 96% with quality scores.
A new jet constituent-based method for top quark tagging achieves high background rejection.
problem Tagging highly energetic jets resulting from top quark decays.
method Sequential approach using ordered jet constituents as inputs, avoiding loss of information.
result Achieves a background rejection of 45 at a 50% efficiency operating point.
ManifoldMind uses adaptive-curvature probabilistic spheres for trustworthy recommendations in semantic hierarchies.
problem Sparse and abstract recommendation domains where users explore diverse conceptual paths.
method Adaptive-curvature probabilistic spheres, soft multi-hop inference, and curvature-aware semantic kernel.
result Superior NDCG, calibration, and diversity compared to baselines on public benchmarks.
Feature learning and deep learning have drawn great attention in recent years as a way of transforming input data into more effective representations using learning algorithms. Such interest has grown in the area of music information retrieval (MIR) as well, particularly in music audio classification tasks such as auto…
CrossFilter tackles noisy labels in audio tagging.
problem Noisy labels in large audio datasets.
method CrossFilter framework using multiple representations and multi-task learning.
result Improves audio tagging performance on FSDKaggle2018 and FSDKaggle2019 datasets.
A multi-task learning model for slot tagging in biomedical domains.
problem Limited labeled data, memory constraints, and domain-specific slot types.
method Multi-task learning using deep bidirectional transformers.
result Outperforms previous state-of-the-art systems in efficiency and effectiveness.
New method uses artificial data to improve biomedical text tagging.
problem Lack of labeled data for biomedical tasks.
method Fine-tuning a pre-trained encoder with additional artificial labeled instances.
result Achieved state-of-the-art performance in PICO annotation.
New neural network layer handles OOV words in NLP tasks without pre-training.
problem Handling out-of-vocabulary words in natural language processing.
method Contextual-compositional neural network layer that attends to character sequence and context.
result Improves performance on 23 languages in joint tagging tasks.
Paper shows comparing single performance scores is insufficient for non-deterministic systems, proposing to compare score distributions.
problem Insufficient comparison of non-deterministic sequence tagging systems.
method Compare score distributions based on multiple executions of LSTM-networks.
result LSTM-networks produce superior and more stable performance when compared using score distributions.
Deep models learn interactions across sets, achieving state-of-the-art performance.
problem Modeling interactions across multiple sets of objects.
method Permutation-equivariant parameter-sharing scheme for deep learning.
result State-of-the-art performance on matrix completion benchmarks and extrapolation tasks.
The paper improves part-of-speech tagging with multi-task learning and character-level word representations.
problem Improving part-of-speech tagging accuracy.
method Developed a new character-level word representation using feedforward neural network, pretraining with existing word vectors, and an additional prediction of neighbour labels as an auxiliary loss.
result The methods significantly improved POS tagging performance on English and Russian languages.