This paper presents a novel latent variable recurrent neural network architecture for jointly modeling sequences of words and (possibly latent) discourse relations between adjacent sentences. A recurrent neural network generates individual words, thus reaping the benefits of discriminatively-trained vector representati…
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48 results for “discourse relation classification”
Adversarial model improves implicit relation classification without explicit connectives.
problem Lack of explicit connectives makes implicit discourse relation classification challenging.
method Feature imitation framework with adversarial training.
result State-of-the-art performance on PDTB benchmark.
This study identifies sentence relationships in legal transcripts.
problem Improving understanding of legal case proceedings through sentence relationships.
method Combining machine learning and rule-based approach to classify sentence relationships.
result First study to use discourse relationships for legal court case transcripts.
HDSF detects fake news by learning hierarchical discourse-level structures.
problem Detecting fake news articles with minimal annotated corpora.
method Automatically learns and constructs discourse-level structures for fake and real news articles.
result Real and fake news present substantial differences in hierarchical discourse-level structures.
Text documents are structured on multiple levels of detail: individual words are related by syntax, but larger units of text are related by discourse structure. Existing language models generally fail to account for discourse structure, but it is crucial if we are to have language models that reward coherence and gener…
Research finds correlations between Bitcoin online discourse and price/volume movements.
problem Mapping sentiment to Bitcoin price and volume movements.
method Collected and analyzed data from Bitcointalk.org, news sources, and Reddit communities.
result Weak to moderate correlations between online sentiment and Bitcoin price/volume movements.
Improved text generation with discourse-aware soft prompts.
problem Efficient fine-tuning methods don't generalize across all generation tasks.
method Hierarchical blocking and attention sparsity on prefix parameters.
result Structured design of prefix parameters yields more coherent generations.
This work speeds up unsupervised sentence learning using paragraph coherence.
problem Training fast unsupervised sentence encoders.
method Discourse-based objective function for neural network training.
result Models trained with this method are faster and perform well.
System detects financial news temporality combining NLP and ML.
problem Separate context from predictions in financial news.
method Combines NLP and ML, extracts dominant tenses.
result High detection precision compared to baseline.
Word embeddings are ubiquitous in NLP and information retrieval, but it is unclear what they represent when the word is polysemous. Here it is shown that multiple word senses reside in linear superposition within the word embedding and simple sparse coding can recover vectors that approximately capture the senses. The …
Language of Persuasion and Misrepresentation in Business Communication: A Textual Detection Approachcs.CL
The study detects deceptive language in business communication using AI.
problem Deceptive language in business communication.
method Combining classical rhetoric, communication psychology, and linguistic theory with computational textual analysis and transformer models.
result Detection accuracies of over 99% achieved in controlled settings.
New approach to topic modelling with covariates for large text corpora.
problem Complex topic modelling in large text corpora.
method Combining convex NMF with regression for tractable estimation.
result Faster, interpretable, and better inferential justification than generative models.
Cosine loss improves CNN performance on small datasets.
problem Training CNNs from scratch on small datasets without pre-training.
method Used cosine loss instead of cross-entropy loss.
result Accuracy on CUB-200-2011 dataset is 30% higher with cosine loss.
This paper reviews different word embeddings for sentiment classification using deep learning.
problem Handling large textual data with simple ML algorithms.
method Word embedding strategies implemented on an Amazon Review Dataset.
result Different word embeddings improve accuracy in sentiment classification.
Model predicts upcoming discourse referents using linguistic and script knowledge.
problem Predicting upcoming discourse referents based on linguistic knowledge.
method Built a computational model that predicts referents using linguistic knowledge and scripts.
result Script knowledge significantly improves model estimates of human predictions.
Deep model captures interactions in online forums.
problem Modeling discursive interactions in online forums.
method Coupled Distributed Topics model with deep architecture and GPU-based inference.
result Model outperforms existing methods in online discourse analysis.
Bitcoin price prediction models fail to outperform a simple 'today's price' baseline, especially at longer horizons.
problem Lack of robust models that consistently outperform a naive price predictor at various horizons.
method Surveyed peer-reviewed papers, categorized by evaluation methodology, contrasted with social media discourse, and proposed methodological standards.
result No peer-reviewed study has shown robust superiority over the naive baseline across multiple market regimes at short-to-medium horizons.
Study examines active travel in Chicago communities, revealing mixed perceptions.
problem Transport disadvantage and lack of active mobility in underserved communities.
method Focus groups, qualitative discourse analysis, quantitative text-mining (topic modeling, sentiment analysis).
result Residents view active travel as both necessity and symbol of privilege, influenced by local culture.
Study analyzes global public sentiment on DeFi from 2012-2022.
problem Global public sentiment on DeFi is understudied.
method Sentiment analysis, spatial econometrics, clustering, topic modeling.
result Economic development significantly influences DeFi engagement, especially after 2015.
Forbidden knowledge in machine learning -- Reflections on the limits of research and publicationcs.LG
Paper discusses ethical norms for machine learning to prevent misuse.
problem Harmful misuse of machine learning applications.
method Proposes review parameters for ethical framework.
result Ethical guidelines for sharing sensitive machine learning information.
The study redefines algorithmic fairness as a sociotechnical concept.
problem Systemic discrimination in automated decision-making.
method Literature review and sociotechnical analysis.
result Algorithmic fairness should be viewed through a sociotechnical lens.
Designs ranking models to consider long-term consequences, improving online discourse.
problem Ranking models fail to foresee long-term negative impacts.
method Introduces Markov decision processes and weighted sampling for optimal consequential rankings; develops gradient-based algorithm for practical implementation.
result Optimal consequential rankings can be approximated efficiently using parameterized models.
Tweet Influence on Market Trends: Analyzing the Impact of Social Media Sentiment on Biotech Stocksq-fin.ST
Study examines how social media sentiment impacts biotech stocks.
problem Understanding the impact of social media on biotech stock prices.
method VADER sentiment analysis, ARIMA, and VAR models were used to forecast stock market performance.
result Complex interplay between tweet sentiment and stock market performance was identified.
FX Market Volatilityq-fin.MF
Paper examines two methods for FX market volatility modeling.
problem FX market volatility modeling problem.
method Classical econometric GCH and mathematical approaches (SSA, dynamical systems stability analysis).
result Both mathematical tools show promising results in FX market volatility modeling.
Study submanifolds with relative nullity in space forms using splitting tensor.
problem Characterize submanifolds with relative nullity in space forms.
method Use splitting tensor and Codazzi equation to express second fundamental form.
result Derive new strong consequences in hyperbolic and Euclidean spaces.
Paper proposes a new method for sentence embeddings using weighted word vectors.
problem Improving sentence embeddings for natural language processing tasks.
method A simple sentence embedding method using weighted average of word vectors followed by soft projection.
result Demonstrates effectiveness on clinical semantic textual similarity task.
Study on trade-offs between accuracy and interpretability in machine learning.
problem Lack of formal study on statistical cost of interpretability.
method Modeling interpretability as a constraint in empirical risk minimization for binary classification.
result Explains conditions under which accuracy trade-off occurs with interpretability constraints.
The New Yorker publishes a weekly captionless cartoon. More than 5,000 readers submit captions for it. The editors select three of them and ask the readers to pick the funniest one. We describe an experiment that compares a dozen automatic methods for selecting the funniest caption. We show that negative sentiment, hum…
Paper tackles noisy relation classification by sentence-level reinforcement learning.
problem Noisy distant supervision in relation classification.
method Two-module approach: instance selector using reinforcement learning, relation classifier making sentence-level predictions.
result Jointly trained model optimizes instance selection and relation classification, effectively handling noisy data.
Study tackles hate speech against journalists on social media.
problem Hate speech against journalists on social media remains prevalent despite efforts.
method Defined journalist-specific hate speech, annotated tweets, trained deep learning models, and proposed an ensemble model.
result Proposed ensemble model outperforms individual models in detecting journalist-targeted hate speech.
Paper proposes a framework to analyze DV on social media.
problem Lack of actionable knowledge from DV social media data.
method Develops a novel framework to model and discover themes related to DV.
result Provides actionable knowledge from DV social media content.
A new dataset for few-shot relation classification challenges current models.
problem Few-shot relation classification is an open problem requiring further research.
method Adapted state-of-the-art few-shot learning methods for relation classification.
result Current models struggle with relation classification, especially compared to humans.
This paper evaluates AI ethics guidelines and their implementation.
problem Lack of comprehensive and effective AI ethics guidelines.
method Comprehensive evaluation and comparison of released ethics guidelines.
result Identifies overlaps and omissions in AI ethics guidelines.
Model criticism tool evaluates text coherence and structure in generated long-form text.
problem Evaluate the high-level structure of generated text for coherence, coreference, and topicality.
method Apply model criticism in latent space to compare real and generated data distributions.
result Transformer-based models struggle with maintaining structural coherence and coreference.
System detects relevant financial news and predictions from unstructured text.
problem Manual extraction of relevant financial information from news is cumbersome and error-prone.
method Topic modeling with LDA, co-reference resolution, multi-paragraph segmentation, and temporal analysis.
result ROUGE-L values for relevant text and predictions/forecasts were 0.662 and 0.982, respectively.
Framework for few-shot relation classification with minimal training data.
problem Few-shot relation classification with limited training data.
method Meta-learning framework that combines instance and support knowledge.
result Framework outperforms state-of-the-art results and achieves competitive performance with large training data.
New framework replicates private equity performance using AI and liquid strategies.
problem Inadequate trust and transparency in private equity markets.
method Advanced graphical models and asymmetric risk adjustments.
result Liquid, scalable solution that closely mimics private equity performance.
New approach detects fake news stance using deep learning and similarity features.
problem Detecting fake news through stance detection in news articles.
method Combines deep neural representations with string similarity features for headline and article analysis.
result Model outperforms previous state-of-the-art, especially with pre-training and combined representations.
GMNN combines conditional random fields and graph neural networks for relational data.
problem Semi-supervised object classification in relational data.
method Combines conditional random fields and graph neural networks. Uses variational EM algorithm for training.
result GMNN achieves state-of-the-art results on object classification, link classification, and unsupervised node representation learning.
Relational learning deals with data that are characterized by relational structures. An important task is collective classification, which is to jointly classify networked objects. While it holds a great promise to produce a better accuracy than non-collective classifiers, collective classification is computational cha…
CompGCN embeds nodes and relations in multi-relational graphs.
problem Handling multi-relational graphs with direction and labels.
method CompGCN uses entity-relation composition operations from KG embedding.
result CompGCN achieves superior results on node classification, link prediction, and graph classification.
In the wake of the still ongoing global financial crisis, bank interdependencies have come into focus in trying to assess linkages among banks and systemic risk. To date, such analysis has largely been based on numerical data. By contrast, this study attempts to gain further insight into bank interconnections by tappin…
Supervised machine learning models boast remarkable predictive capabilities. But can you trust your model? Will it work in deployment? What else can it tell you about the world? We want models to be not only good, but interpretable. And yet the task of interpretation appears underspecified. Papers provide diverse and s…
In many supervised learning tasks, the entities to be labeled are related to each other in complex ways and their labels are not independent. For example, in hypertext classification, the labels of linked pages are highly correlated. A standard approach is to classify each entity independently, ignoring the correlation…
Temporal networks are ubiquitous and evolve over time by the addition, deletion, and changing of links, nodes, and attributes. Although many relational datasets contain temporal information, the majority of existing techniques in relational learning focus on static snapshots and ignore the temporal dynamics. We propose…
Study finds significant price declines and capital reallocation from centralized to decentralized exchanges after FTX collapse.
problem Quantifying trust dynamics and redistribution between centralized and decentralized exchanges.
method Interdisciplinary approach combining causal inference and computational text analysis.
result Significant price declines and capital reallocation from centralized to decentralized exchanges following the FTX collapse.
Proposes a model to classify nodes in networks using weighted feedback relations.
problem Challenges in predicting node labels in sparse networks with implicit feedback.
method Weighted personalized two-stage matrix factorization model with Bayesian ranking loss.
result Significantly outperforms state-of-the-art models on various datasets.
Study shows pre-trained models can handle long-tailed relations well, improving classifier performance.
problem Challenges in long-tailed relation classification due to class imbalance.
method Used instance-balanced sampling to pre-train models and then improved classifier performance through attentive relation routing.
result Robust classifier with attentive relation routing achieves better long-tailed classification ability.