Current state-of-the-art nonparametric Bayesian text clustering methods model documents through multinomial distribution on bags of words. Although these methods can effectively utilize the word burstiness representation of documents and achieve decent performance, they do not explore the sequential information of text…
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48 results for “sequential documents”
A new kernel Stein test assesses fit for variable-length sequential data.
problem Evaluating goodness of fit for varying-dimensional data like text documents of different lengths.
method Extends kernel Stein discrepancy (KSD) to variable-dimension settings by identifying appropriate Stein operators and proposing a novel KSD goodness-of-fit test.
result The proposed test performs well on discrete sequential data benchmarks.
Recurrent Neural Networks (RNNs) have been proven to be effective in modeling sequential data and they have been applied to boost a variety of tasks such as document classification, speech recognition and machine translation. Most of existing RNN models have been designed for sequences assumed to be identically and ind…
PyCFRL helps ensure fair reinforcement learning policies from offline data.
problem Ensuring fairness in reinforcement learning policies for disadvantaged groups.
method Sequential data preprocessing to learn counterfactually fair policies.
result PyCFRL implements a novel algorithm for fair RL policy learning.
D-ETM models document topics over time using embeddings and variational inference.
problem Capturing evolving topic patterns in sequential documents.
method Combines D-LDA and word embeddings, using random walk priors and variational inference.
result D-ETM outperforms D-LDA on document completion tasks, learning more diverse and coherent topics.
In (Yang et al. 2016), a hierarchical attention network (HAN) is created for document classification. The attention layer can be used to visualize text influential in classifying the document, thereby explaining the model's prediction. We successfully applied HAN to a sequential analysis task in the form of real-time m…
Interactive machine comprehension models learn through seeking relevant information.
problem Existing MRC models struggle with real-world applications due to static datasets.
method Reframe MRC datasets as interactive, partially observable environments.
result Interactive models can seek relevant information through sequential decision making.
An Efficient Shared-memory Parallel Sinkhorn-Knopp Algorithm to Compute the Word Mover's Distancecs.LG
This paper speeds up WMD computation for multiple queries efficiently.
problem Efficiently computing the semantic dissimilarity between text documents.
method Adapting the Sinkhorn-Knopp algorithm to compute WMD of one document against many targets in parallel.
result 67x speedup on 96 cores compared to sequential and naive parallel methods.
We introduce a new Bayesian model for hierarchical clustering based on a prior over trees called Kingman's coalescent. We develop novel greedy and sequential Monte Carlo inferences which operate in a bottom-up agglomerative fashion. We show experimentally the superiority of our algorithms over others, and demonstrate o…
In a context of document co-clustering, we define a new similarity measure which iteratively computes similarity while combining fuzzy sets in a three-partite graph. The fuzzy triadic similarity (FT-Sim) model can deal with uncertainty offers by the fuzzy sets. Moreover, with the development of the Web and the high ava…
We present the mixture-of-parents maximum entropy Markov model (MoP-MEMM), a class of directed graphical models extending MEMMs. The MoP-MEMM allows tractable incorporation of long-range dependencies between nodes by restricting the conditional distribution of each node to be a mixture of distributions given the parent…
In this paper, we propose TopicRNN, a recurrent neural network (RNN)-based language model designed to directly capture the global semantic meaning relating words in a document via latent topics. Because of their sequential nature, RNNs are good at capturing the local structure of a word sequence - both semantic and syn…
This research proposes a CL model for RNNs to handle sequential data without forgetting.
problem Learning in dynamic environments without forgetting previous knowledge for sequential data.
method A Recurrent Neural Network (RNN) model with Elastic Weight Consolidation (EWC) for CL.
result The proposed model outperforms EWC and RNNs on CL benchmarks for sequential data.
RuleKit aids in creating interpretable models for various data types.
problem Creating interpretable models for different data types.
method Sequential covering induction algorithm for classification, regression, and survival problems.
result Facilitates verification of hypotheses about data dependencies.
Recent approaches based on artificial neural networks (ANNs) have shown promising results for short-text classification. However, many short texts occur in sequences (e.g., sentences in a document or utterances in a dialog), and most existing ANN-based systems do not leverage the preceding short texts when classifying …
Framework uses deep learning to analyze large documents and identify their logical structure.
problem Analyzing large, multi-themed documents with diverse topics.
method Deep learning techniques to model and extract logical and semantic structure.
result Framework effectively identifies and classifies different sections of documents.
Improved LSTM cell for high-frequency trading forecasts.
problem Precise stock price forecasting with minimal lags.
method Revised long short-term memory (LSTM) cell with optimal gate/state selection.
result Lower forecasting error compared to other recurrent neural networks.
PyHHMM is a Python library for HHMMs with advanced features.
problem Handling heterogeneous observation models and missing data in HMMs.
method Object-oriented Python implementation with advanced features.
result PyHHMM supports a heterogeneous observation model and missing data inference.
A new method for document network embedding interprets and generalizes well.
problem Lack of interpretability and generalization to new documents in existing methods.
method Introduces Topic-Word Attention (TWA) and Inductive Document Network Embedding (IDNE) to generate document representations.
result Achieves state-of-the-art performance on various networks and produces meaningful representations.
LSTM and gradient boosting models fail to outperform random chance in predicting MNQ futures.
problem Predicting intraday direction in MNQ futures using LSTM and gradient boosting.
method Comparing LSTM and gradient boosting models on 944 trading days of MNQ futures data.
result No model achieves statistically significant accuracy above random chance.
A new model CDTM improves text classification by concentrating document topics.
problem Unsupervised text classification with diverse topic distributions.
method Imposes an exponential entropy penalty on document topic distribution to encourage concentration.
result More coherent topics and concentrated, sparse document-topic distributions.
New document embedding method finds more relevant tech docs.
problem Computing document similarities for technical content.
method Hybrid approach using graph techniques to extract keyphrases and score sentences.
result Proposed methods outperform baselines up to 27% in NDCG.
HyperStream processes streaming data with workflow creation.
problem Processing large-scale, real-time data challenges.
method Python-based workflow engine for flexible, robust data processing.
result Overcomes limitations of other computational engines.
Improved self-supervised learning for document images.
problem Performance of self-supervised pre-training on document images is poor.
method Proposed context-aware alternatives and a novel multi-modal method.
result Novel method outperforms other self-supervised methods on document image classification.
MarlRank uses multi-agent reinforcement learning to improve document ranking.
problem Neglecting mutual information among documents in ranking models.
method Formulated as a multi-agent Markov Decision Process (MDP), each document predicts relevance considering its own and similar documents features and actions.
result Significant performance gains over state-of-the-art baselines on LETOR benchmark datasets.
DocParser parses document structures from renderings like PDFs and scans.
problem Parsing complete hierarchical document structures from renderings.
method End-to-end system with novel weak supervision approach.
result Significant improvement in document structure parsing performance.
CCAligned creates a massive web document dataset for cross-lingual research.
problem Identifying comparable documents across different languages.
method Using URL signals to label web documents and mining Common Crawl corpus.
result Release of a dataset with over 392 million URL pairs from 8144 language pairs.
Understanding large, structured documents like scholarly articles, requests for proposals or business reports is a complex and difficult task. It involves discovering a document's overall purpose and subject(s), understanding the function and meaning of its sections and subsections, and extracting low level entities an…
Paper develops a new unsupervised scoring function for cross-lingual document alignment.
problem Aligning documents across different languages for NLP tasks.
method Uses cross-lingual sentence embeddings to compute semantic distances and guides document alignment.
result The proposed scoring function outperforms current methods by 7-22% on various language pairs.
Adaptive AI delegation framework for dynamic decision authority allocation.
problem Dynamic allocation of decision authority to AI-generated recommendations under evolving evidence quality and uncertainty.
method Formulated as a Governance-Aware POMDP, using Bayesian inference for informational state estimation and sequential optimization for authority allocation.
result Sequential Bayesian governance provides the strongest general-purpose policy across AI-quality regimes, adapting to evolving evidence.
Generative Topic Embedding: a Continuous Representation of Documents (Extended Version with Proofs)cs.CL
Word embedding maps words into a low-dimensional continuous embedding space by exploiting the local word collocation patterns in a small context window. On the other hand, topic modeling maps documents onto a low-dimensional topic space, by utilizing the global word collocation patterns in the same document. These two …
Most research in reading comprehension has focused on answering questions based on individual documents or even single paragraphs. We introduce a neural model which integrates and reasons relying on information spread within documents and across multiple documents. We frame it as an inference problem on a graph. Mentio…
Topic Extraction of Crawled Documents Collection using Correlated Topic Model in MapReduce Frameworkcs.IR
A scalable topic model for large document collections using MapReduce.
problem Scalability issues in topic modeling for large document collections.
method Correlated Topic Model with variational Expectation-Maximization in MapReduce framework.
result Comparable topic coherences with LDA in MapReduce framework.
A new method for measuring document similarity using hierarchical optimal transport.
problem Inability to measure semantic similarities and scalability issues in past document similarity measures.
method Model documents as distributions over topics, topics as distributions over words, solve optimal transport problem on topics.
result Hierarchical optimal transport provides better interpretability and scalability with comparable performance.
IE-GP framework learns sequentially arriving data with adaptive kernels.
problem Sequential data adaptation in Gaussian process models.
method Incremental ensemble of Gaussian process learners with adaptive weights.
result IE-GP framework effectively learns and adapts to sequential data.
We develop a nested hierarchical Dirichlet process (nHDP) for hierarchical topic modeling. The nHDP is a generalization of the nested Chinese restaurant process (nCRP) that allows each word to follow its own path to a topic node according to a document-specific distribution on a shared tree. This alleviates the rigid, …
Traditional Relational Topic Models provide a way to discover the hidden topics from a document network. Many theoretical and practical tasks, such as dimensional reduction, document clustering, link prediction, benefit from this revealed knowledge. However, existing relational topic models are based on an assumption t…
A new model analyzes document structure and customer shopping patterns.
problem Understanding document structure and customer shopping patterns.
method Variational EM algorithm for multilayer correlated topic modeling.
result MCTM successfully captures document structure and customer shopping patterns.
Study improves document processing in banking with multimodal analytics.
problem Raising operational efficiency in banking through document-intensive processes.
method Comparative analysis of text classifiers and multimodal model (LayoutXLM) on company register extracts.
result Incorporating layout information in a model substantially increases performance.
Document clustering and topic modeling are two closely related tasks which can mutually benefit each other. Topic modeling can project documents into a topic space which facilitates effective document clustering. Cluster labels discovered by document clustering can be incorporated into topic models to extract local top…
Improved TF-IDF for word relevance in health-care social media documents.
problem Determining word relevance in informal documents.
method Semantic Sensitive TF-IDF (STF-IDF) method.
result Decreased TF-IDF mean error rate by 50% to 13.7%.
This paper improves topic modeling by embedding words and topics together.
problem Topic models struggle with short documents and approximate inference.
method Model each document as a mixture of word embeddings and each topic as a mixture of topic embeddings.
result The method optimizes topic embeddings to minimize semantic differences between words and topics.
Develops scalable autoencoder for document networks.
problem Sparse and skewed latent node representations in document relational networks.
method Combines graph Poisson factor analysis with Weibull-based graph inference networks.
result Extracts high-quality hierarchical latent document representations.
Label-aware Document Representation via Hybrid Attention for Extreme Multi-Label Text Classificationcs.LG
Extreme multi-label text classification (XMTC) aims at tagging a document with most relevant labels from an extremely large-scale label set. It is a challenging problem especially for the tail labels because there are only few training documents to build classifier. This paper is motivated to better explore the semanti…
Top2Vec finds topic vectors from documents and words without needing stop words or custom settings.
problem Topic modeling weaknesses, including needing known topics, stop words, and custom settings.
method Joint document and word semantic embedding to find topic vectors automatically.
result Top2Vec finds more informative and representative topics than probabilistic models.
Contrastive learning helps linear models understand document topics.
problem Document classification with limited labeled data.
method Contrastive learning applied to document topic modeling.
result Linear models can recover topic posterior information from contrastive learning representations.
Paper develops an AI-driven framework for systematic investing.
problem Manual prompts limit model adaptability and data snooping biases.
method Closed-loop system with self-evolving AI, out-of-sample validation, and economic rationale.
result Long-short portfolios on factor signals outperform with Sharpe ratio 3.11 and return 59.53%.
Proposes new listwise learning-to-rank models to address rating ties and document relevance.
problem Rating ties and document relevance in existing listwise learning-to-rank models.
method Models ranking as selecting documents from a candidate set based on unique rating levels. Uses a new loss function and adapted RNN model for refining prediction scores.
result Models notably outperform state-of-the-art learning-to-rank models on four public datasets.