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…
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48 results for “document comprehension”
Salzmann's legacy in mathematics documented.
problem Mathematical challenges faced by Salzmann.
method Comprehensive review of Salzmann's work and bibliography.
result Salzmann's far-reaching influence in mathematics.
Code search and comprehension have become more difficult in recent years due to the rapid expansion of available source code. Current tools lack a way to label arbitrary code at scale while maintaining up-to-date representations of new programming languages, libraries, and functionalities. Comprehensive labeling of sou…
AI helps simplify complex ship finance processes.
problem Complexity in ship finance due to data and regulatory requirements.
method Integrates large language models for document comprehension, information extraction, and workflow automation.
result AI-assisted systems can support maritime finance professionals in managing complex information and reporting requirements.
In text classification, dictionaries can be used to define human-comprehensible features. We propose an improvement to dictionary features called smoothed dictionary features. These features recognize document contexts instead of n-grams. We describe a principled methodology to solicit dictionary features from a teache…
Existing machine reading comprehension (MRC) models do not scale effectively to real-world applications like web-level information retrieval and question answering (QA). We argue that this stems from the nature of MRC datasets: most of these are static environments wherein the supporting documents and all necessary inf…
We present a new dataset for form understanding in noisy scanned documents (FUNSD) that aims at extracting and structuring the textual content of forms. The dataset comprises 199 real, fully annotated, scanned forms. The documents are noisy and vary widely in appearance, making form understanding (FoUn) a challenging t…
FinReflectKG builds a comprehensive financial knowledge graph from SEC filings, improving extraction quality.
problem Lack of large-scale, open-source financial knowledge graph datasets.
method Intelligent document parsing, table-aware chunking, schema-guided iterative extraction, reflection-driven feedback loop.
result Reflection-agent-based mode achieves best balance of efficiency, accuracy, and reliability.
Autoencoders have been successful in learning meaningful representations from image datasets. However, their performance on text datasets has not been widely studied. Traditional autoencoders tend to learn possibly trivial representations of text documents due to their confounding properties such as high-dimensionality…
Hybrid Topic-Semantic Labeling and Graph Embeddings for Unsupervised Legal Document Clusteringstat.ML
Hybrid approach combines topic and graph embeddings for legal document clustering.
problem Challenges in classifying legal texts due to domain-specific language and limited labeled data.
method Combines unsupervised topic and graph embeddings with a supervised model.
result Improves clustering quality over text-only or graph-only embeddings.
Python library for causal discovery from observational data.
problem Revealing causal relations from observational data.
method Comprehensive collection of causal discovery methods in Python.
result Ease of use for non-specialists and modular building blocks for developers.
We consider the large-scale query-document retrieval problem: given a query (e.g., a question), return the set of relevant documents (e.g., paragraphs containing the answer) from a large document corpus. This problem is often solved in two steps. The retrieval phase first reduces the solution space, returning a subset …
Text documents can be described by a number of abstract concepts such as semantic category, writing style, or sentiment. Machine learning (ML) models have been trained to automatically map documents to these abstract concepts, allowing to annotate very large text collections, more than could be processed by a human in …
SOL is an open-source library for scalable online learning algorithms, and is particularly suitable for learning with high-dimensional data. The library provides a family of regular and sparse online learning algorithms for large-scale binary and multi-class classification tasks with high efficiency, scalability, porta…
TinyXRA assesses financial risks from 10-K reports using a lightweight transformer model.
problem Comprehensive risk assessment from financial reports, distinguishing between upside and downside risk.
method Lightweight transformer model with dynamic attention, incorporating skewness, kurtosis, and Sortino ratio.
result State-of-the-art predictive accuracy and transparent risk assessments.
Recently, the development and implementation of phishing attacks require little technical skills and costs. This uprising has led to an ever-growing number of phishing attacks on the World Wide Web. Consequently, proactive techniques to fight phishing attacks have become extremely necessary. In this paper, we propose H…
New hashing method improves document retrieval precision.
problem Efficiently retrieving similar documents from large text databases.
method Pairwise supervised hashing with Bernoulli VAE and unbiased gradient estimator.
result Superior performance compared to existing methods.
Generative and reproducible benchmarks for comprehensive evaluation of machine learning classifierscs.LG
DIGEN benchmark provides synthetic datasets for ML algorithm evaluation.
problem Understanding and comparing machine learning algorithms' performance.
method Synthetic datasets generated using 40 mathematical functions to evaluate machine learning algorithms.
result DIGEN resource facilitates understanding why algorithms perform poorly and provides ideas for improvement.
Analyzing a comprehensive news dataset, we document that joint news coverage triggers attention contagion, causing temporarily inflated valuations for affected stocks. Tracing SEC EDGAR visits from unique IPs, we provide direct evidence of attention spillovers between stocks. Stocks with greater joint news coverage exh…
Retrieving indexed documents, not by their topical content but their writing style opens the door for a number of applications in information retrieval (IR). One application is to retrieve textual content of a certain author X, where the queried IR system is provided beforehand with a set of reference texts of X. Autho…
Rule-based models are often used for data analysis as they combine interpretability with predictive power. We present RuleKit, a versatile tool for rule learning. Based on a sequential covering induction algorithm, it is suitable for classification, regression, and survival problems. The presence of a user-guided induc…
Robust Volume Minimization-Based Matrix Factorization for Remote Sensing and Document Clusteringstat.ML
This paper considers \emph{volume minimization} (VolMin)-based structured matrix factorization (SMF). VolMin is a factorization criterion that decomposes a given data matrix into a basis matrix times a structured coefficient matrix via finding the minimum-volume simplex that encloses all the columns of the data matrix.…
Urban2Vec: Incorporating Street View Imagery and POIs for Multi-Modal Urban Neighborhood Embeddingcs.LG
Understanding intrinsic patterns and predicting spatiotemporal characteristics of cities require a comprehensive representation of urban neighborhoods. Existing works relied on either inter- or intra-region connectivities to generate neighborhood representations but failed to fully utilize the informative yet heterogen…
Codebook for Institutional Grammar 2.0 simplifies policy encoding.
problem Facilitating consistent policy encoding for diverse analytical needs.
method Revised Institutional Grammar with multiple levels of expressiveness.
result Enhanced flexibility and specificity in policy encoding.
mvlearn simplifies multiview machine learning for non-specialists.
problem Handling multiview data sets from disparate sources.
method Python library implementing multiview machine learning methods.
result Enables non-specialists to use multiview machine learning methods easily.
Understanding and extracting of information from large documents, such as business opportunities, academic articles, medical documents and technical reports, poses challenges not present in short documents. Such large documents may be multi-themed, complex, noisy and cover diverse topics. We describe a framework that c…
Global Memory Augmentation (GMAT) improves Transformer performance on long documents.
problem Large memory requirements of Transformer pairwise dot-product attention for long sequences.
method Integrates a dense global memory of length M into sparse Transformer blocks.
result Significant improvement on various tasks, including synthetic tasks, masked language modeling, and reading comprehension.
LLMs perform well in financial sentiment analysis without fine-tuning.
problem Challenges in financial terminology, emotions, and ambiguous expressions.
method In-context learning methods for financial document-sentiment pairs.
result LLMs can generalize in-context demonstrations to new financial documents.
MushroomRL is an open-source Python library developed to simplify the process of implementing and running Reinforcement Learning (RL) experiments. Compared to other available libraries, MushroomRL has been created with the purpose of providing a comprehensive and flexible framework to minimize the effort in implementin…
Living review of ML for particle physics, updated frequently.
problem Keeping up with rapid advancements in ML for particle physics.
method Creating a living document to list and update citations of ML applications.
result Provides a comprehensive list of ML citations for particle physics.
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.
Electronic Health Records (EHRs) have been heavily used to predict various downstream clinical tasks such as readmission or mortality. One of the modalities in EHRs, clinical notes, has not been fully explored for these tasks due to its unstructured and inexplicable nature. Although recent advances in deep learning (DL…
EagerPy simplifies writing code for multiple deep learning frameworks.
problem Code duplication and framework lock-in when using different deep learning libraries.
method Integrates multiple frameworks into a single Python framework.
result Automatic compatibility across PyTorch, TensorFlow, JAX, and NumPy.
Document network embedding aims at learning representations for a structured text corpus i.e. when documents are linked to each other. Recent algorithms extend network embedding approaches by incorporating the text content associated with the nodes in their formulations. In most cases, it is hard to interpret the learn…
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.
Python package for functional data analysis.
problem Handling and analysis of functional data.
method Comprehensive tools for representation, preprocessing, and exploratory analysis of functional data.
result Scikit-fda package provides a comprehensive set of tools for functional data analysis.
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…
We propose in this paper a new, hybrid document embedding approach in order to address the problem of document similarities with respect to the technical content. To do so, we employ a state-of-the-art graph techniques to first extract the keyphrases (composite keywords) of documents and, then, use them to score the se…
Ranking problems, also known as preference learning problems, define a widely spread class of statistical learning problems with many applications, including fraud detection, document ranking, medicine, credit risk screening, image ranking or media memorability. In this article, we systematically review different types…
When estimating the relevancy between a query and a document, ranking models largely neglect the mutual information among documents. A common wisdom is that if two documents are similar in terms of the same query, they are more likely to have similar relevance score. To mitigate this problem, in this paper, we propose …
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 …
PEHRT harmonizes EHR data for translational research.
problem Barriers in using EHR data for translational research.
method Common pipeline including open-source code, visualization tools, and detailed documentation.
result PEHRT harmonizes EHR data to standardized ontologies and generates robust embeddings.
Trieste optimizes black-box functions using TensorFlow for efficient decision-making.
problem Optimizing complex, black-box functions efficiently.
method Bayesian optimization and active learning with TensorFlow integration.
result Trieste enables efficient exploration of black-box functions.
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…
Cross-lingual document alignment aims to identify pairs of documents in two distinct languages that are of comparable content or translations of each other. In this paper, we exploit the signals embedded in URLs to label web documents at scale with an average precision of 94.5% across different language pairs. We mine …
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.