Search Results for author:
Found 131 papers, 51 papers with code
Self Question-answering: Aspect-based Sentiment Analysis by Role Flipped Machine Reading Comprehension
In this paper, we investigate the unified ABSA task from the perspective of Machine Reading Comprehension (MRC) by observing that the aspect and the opinion terms can serve as the query and answer in MRC interchangeably.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+3
kFolden: k-Fold Ensemble for Out-Of-Distribution Detection
Out-of-Distribution (OOD) detection is an important problem in natural language processing (NLP).
DIET: Customized Slimming for Incompatible Networks in Sequential Recommendation
In response to these challenges, we propose a customizeD slImming framework for incompatiblE neTworks(DIET).
MergeNet: Knowledge Migration across Heterogeneous Models, Tasks, and Modalities
The core mechanism of MergeNet lies in the parameter adapter, which operates by querying the source model's low-rank parameters and adeptly learning to identify and map parameters into the target model.
Similarity-based Neighbor Selection for Graph LLMs
Our research further underscores the significance of graph structure integration in LLM applications and identifies key factors for their success in node classification.
Unsupervised Generation of Pseudo Normal PET from MRI with Diffusion Model for Epileptic Focus Localization
In this study, we investigated unsupervised learning methods for unpaired MRI to PET translation for generating pseudo normal FDG PET for epileptic focus localization.
InfiAgent-DABench: Evaluating Agents on Data Analysis Tasks
In this paper, we introduce InfiAgent-DABench, the first benchmark specifically designed to evaluate LLM-based agents on data analysis tasks.
S2M: Converting Single-Turn to Multi-Turn Datasets for Conversational Question Answering
To solve this problem, we propose a novel method to convert single-turn datasets to multi-turn datasets.
Emage: Non-Autoregressive Text-to-Image Generation
Compared with autoregressive baselines that needs to run one thousand times, our model only runs 16 times to generate images of competitive quality with an order of magnitude lower inference latency.
Sim-GPT: Text Similarity via GPT Annotated Data
Due to the lack of a large collection of high-quality labeled sentence pairs with textual similarity scores, existing approaches for Semantic Textual Similarity (STS) mostly rely on unsupervised techniques or training signals that are only partially correlated with textual similarity, e. g., NLI-based datasets.
Rethinking Adversarial Training with Neural Tangent Kernel
To the best of our knowledge, it is the first work leveraging the observations of kernel dynamics to improve existing AT methods.
Sentiment Analysis through LLM Negotiations
A standard paradigm for sentiment analysis is to rely on a singular LLM and makes the decision in a single round under the framework of in-context learning.
Warfare:Breaking the Watermark Protection of AI-Generated Content
We propose Warfare, a unified methodology to achieve both attacks in a holistic way.
Are Human-generated Demonstrations Necessary for In-context Learning?
In this paper, we raise the fundamental question that whether human-generated demonstrations are necessary for ICL.
Instruction Tuning for Large Language Models: A Survey
This paper surveys research works in the quickly advancing field of instruction tuning (IT), a crucial technique to enhance the capabilities and controllability of large language models (LLMs).
Pushing the Limits of ChatGPT on NLP Tasks
In this work, we propose a collection of general modules to address these issues, in an attempt to push the limits of ChatGPT on NLP tasks.
TaDSE: Template-aware Dialogue Sentence Embeddings
We further introduce a novel analytic instrument of Semantic Compression method, for which we discover a correlation with uniformity and alignment.
Text Classification via Large Language Models
This is due to (1) the lack of reasoning ability in addressing complex linguistic phenomena (e. g., intensification, contrast, irony etc); (2) limited number of tokens allowed in in-context learning.
GPT-RE: In-context Learning for Relation Extraction using Large Language Models
In spite of the potential for ground-breaking achievements offered by large language models (LLMs) (e. g., GPT-3), they still lag significantly behind fully-supervised baselines (e. g., fine-tuned BERT) in relation extraction (RE).
GPT-NER: Named Entity Recognition via Large Language Models
GPT-NER bridges the gap by transforming the sequence labeling task to a generation task that can be easily adapted by LLMs e. g., the task of finding location entities in the input text "Columbus is a city" is transformed to generate the text sequence "@@Columbus## is a city", where special tokens @@## marks the entity to extract.
Backdoor Attacks with Input-unique Triggers in NLP
Backdoor attack aims at inducing neural models to make incorrect predictions for poison data while keeping predictions on the clean dataset unchanged, which creates a considerable threat to current natural language processing (NLP) systems.
Open World Classification with Adaptive Negative Samples
Open world classification is a task in natural language processing with key practical relevance and impact.
PK-ICR: Persona-Knowledge Interactive Context Retrieval for Grounded Dialogue
Identifying relevant persona or knowledge for conversational systems is critical to grounded dialogue response generation.
GNN-SL: Sequence Labeling Based on Nearest Examples via GNN
To better handle long-tail cases in the sequence labeling (SL) task, in this work, we introduce graph neural networks sequence labeling (GNN-SL), which augments the vanilla SL model output with similar tagging examples retrieved from the whole training set.
Rescue Implicit and Long-tail Cases: Nearest Neighbor Relation Extraction
Relation extraction (RE) has achieved remarkable progress with the help of pre-trained language models.
Ranking-Enhanced Unsupervised Sentence Representation Learning
In this work, we show that the semantic meaning of a sentence is also determined by nearest-neighbor sentences that are similar to the input sentence.
ShiftNAS: Towards Automatic Generation of Advanced Mulitplication-Less Neural Networks
Multiplication-less neural networks significantly reduce the time and energy cost on the hardware platform, as the compute-intensive multiplications are replaced with lightweight bit-shift operations.
$k$NN-NER: Named Entity Recognition with Nearest Neighbor Search
Inspired by recent advances in retrieval augmented methods in NLP~\citep{khandelwal2019generalization, khandelwal2020nearest, meng2021gnn}, in this paper, we introduce a $k$ nearest neighbor NER ($k$NN-NER) framework, which augments the distribution of entity labels by assigning $k$ nearest neighbors retrieved from the training set.
Physical Backdoor Attacks to Lane Detection Systems in Autonomous Driving
However, DNN models are vulnerable to different types of adversarial attacks, which pose significant risks to the security and safety of the vehicles and passengers.
Exploring and Adapting Chinese GPT to Pinyin Input Method
We find that a frozen GPT achieves state-of-the-art performance on perfect pinyin.
Faster Nearest Neighbor Machine Translation
The core idea of Faster $k$NN-MT is to use a hierarchical clustering strategy to approximate the distance between the query and a data point in the datastore, which is decomposed into two parts: the distance between the query and the center of the cluster that the data point belongs to, and the distance between the data point and the cluster center.
A General Framework for Defending Against Backdoor Attacks via Influence Graph
In this work, we propose a new and general framework to defend against backdoor attacks, inspired by the fact that attack triggers usually follow a \textsc{specific} type of attacking pattern, and therefore, poisoned training examples have greater impacts on each other during training.
Triggerless Backdoor Attack for NLP Tasks with Clean Labels
To deal with this issue, in this paper, we propose a new strategy to perform textual backdoor attacks which do not require an external trigger, and the poisoned samples are correctly labeled.
Interpreting Deep Learning Models in Natural Language Processing: A Review
Neural network models have achieved state-of-the-art performances in a wide range of natural language processing (NLP) tasks.
GNN-LM: Language Modeling based on Global Contexts via GNN
Inspired by the notion that ``{\it to copy is easier than to memorize}``, in this work, we introduce GNN-LM, which extends the vanilla neural language model (LM) by allowing to reference similar contexts in the entire training corpus.
Fingerprinting Multi-exit Deep Neural Network Models via Inference Time
Specifically, we design an effective method to generate a set of fingerprint samples to craft the inference process with a unique and robust inference time cost as the evidence for model ownership.
BadPre: Task-agnostic Backdoor Attacks to Pre-trained NLP Foundation Models
The key feature of our attack is that the adversary does not need prior information about the downstream tasks when implanting the backdoor to the pre-trained model.
A Novel Watermarking Framework for Ownership Verification of DNN Architectures
We present a novel watermarking scheme to achieve the intellectual property (IP) protection and ownership verification of DNN architectures.
NASPY: Automated Extraction of Automated Machine Learning Models
We present NASPY, an end-to-end adversarial framework to extract the networkarchitecture of deep learning models from Neural Architecture Search (NAS).
Towards Robust Point Cloud Models with Context-Consistency Network and Adaptive Augmentation
Extensive evaluations indicate the integration of the two techniques provides much more robustness than existing defense solutions for 3D models.
OpenViDial 2.0: A Larger-Scale, Open-Domain Dialogue Generation Dataset with Visual Contexts
In order to better simulate the real human conversation process, models need to generate dialogue utterances based on not only preceding textual contexts but also visual contexts.
Ranked #1 on
Multi-modal Dialogue Generation
on OpenViDial 2.0
An MRC Framework for Semantic Role Labeling
We formalize predicate disambiguation as multiple-choice machine reading comprehension, where the descriptions of candidate senses of a given predicate are used as options to select the correct sense.
Ranked #1 on
Semantic Role Labeling
on CoNLL 2005
Paraphrase Generation as Unsupervised Machine Translation
Then based on the paraphrase pairs produced by these UMT models, a unified surrogate model can be trained to serve as the final \sts model to generate paraphrases, which can be directly used for test in the unsupervised setup, or be finetuned on labeled datasets in the supervised setup.
ConRPG: Paraphrase Generation using Contexts as Regularizer
A long-standing issue with paraphrase generation is how to obtain reliable supervision signals.
$k$Folden: $k$-Fold Ensemble for Out-Of-Distribution Detection
For a task with $k$ training labels, $k$Folden induces $k$ sub-models, each of which is trained on a subset with $k-1$ categories with the left category masked unknown to the sub-model.
Layer-wise Model Pruning based on Mutual Information
The proposed pruning strategy offers merits over weight-based pruning techniques: (1) it avoids irregular memory access since representations and matrices can be squeezed into their smaller but dense counterparts, leading to greater speedup; (2) in a manner of top-down pruning, the proposed method operates from a more global perspective based on training signals in the top layer, and prunes each layer by propagating the effect of global signals through layers, leading to better performances at the same sparsity level.
ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information
Recent pretraining models in Chinese neglect two important aspects specific to the Chinese language: glyph and pinyin, which carry significant syntax and semantic information for language understanding.
Fingerprinting Image-to-Image Generative Adversarial Networks
Since the production of a commercial GAN requires substantial computational and human resources, the copyright protection of GANs is urgently needed.
Defending Against Backdoor Attacks in Natural Language Generation
The frustratingly fragile nature of neural network models make current natural language generation (NLG) systems prone to backdoor attacks and generate malicious sequences that could be sexist or offensive.
Modeling Text-visual Mutual Dependency for Multi-modal Dialog Generation
Specifically, we propose to model the mutual dependency between text-visual features, where the model not only needs to learn the probability of generating the next dialog utterance given preceding dialog utterances and visual contexts, but also the probability of predicting the visual features in which a dialog utterance takes place, leading the generated dialog utterance specific to the visual context.
Parameter Estimation for the SEIR Model Using Recurrent Nets
Next, based on this recurrent net that is able to generalize SEIR simulations, we are able to transform the objective to a differentiable one with respect to $\Theta_\text{SEIR}$, and straightforwardly obtain its optimal value.
Fast Nearest Neighbor Machine Translation
Fast $k$NN-MT constructs a significantly smaller datastore for the nearest neighbor search: for each word in a source sentence, Fast $k$NN-MT first selects its nearest token-level neighbors, which is limited to tokens that are the same as the query token.
Sentence Similarity Based on Contexts
The proposed framework is based on the core idea that the meaning of a sentence should be defined by its contexts, and that sentence similarity can be measured by comparing the probabilities of generating two sentences given the same context.
Dependency Parsing as MRC-based Span-Span Prediction
The proposed method has the following merits: (1) it addresses the fundamental problem that edges in a dependency tree should be constructed between subtrees; (2) the MRC framework allows the method to retrieve missing spans in the span proposal stage, which leads to higher recall for eligible spans.
BertGCN: Transductive Text Classification by Combining GCN and BERT
In this work, we propose BertGCN, a model that combines large scale pretraining and transductive learning for text classification.
Ranked #1 on
Text Classification
on 20 Newsgroups
Complex Spectral Mapping With Attention Based Convolution Recurrent Neural Network for Speech Enhancement
Speech enhancement has benefited from the success of deep learning in terms of intelligibility and perceptual quality.
OpenViDial: A Large-Scale, Open-Domain Dialogue Dataset with Visual Contexts
Based on this dataset, we propose a family of encoder-decoder models leveraging both textual and visual contexts, from coarse-grained image features extracted from CNNs to fine-grained object features extracted from Faster R-CNNs.
Self-Explaining Structures Improve NLP Models
The proposed model comes with the following merits: (1) span weights make the model self-explainable and do not require an additional probing model for interpretation; (2) the proposed model is general and can be adapted to any existing deep learning structures in NLP; (3) the weight associated with each text span provides direct importance scores for higher-level text units such as phrases and sentences.
Neural Semi-supervised Learning for Text Classification Under Large-Scale Pretraining
The goal of semi-supervised learning is to utilize the unlabeled, in-domain dataset U to improve models trained on the labeled dataset D. Under the context of large-scale language-model (LM) pretraining, how we can make the best use of U is poorly understood: is semi-supervised learning still beneficial with the presence of large-scale pretraining?
Ranked #1000000000 on
Text Classification
on IMDb
Summarize, Outline, and Elaborate: Long-Text Generation via Hierarchical Supervision from Extractive Summaries
The difficulty of generating coherent long texts lies in the fact that existing models overwhelmingly focus on predicting local words, and cannot make high level plans on what to generate or capture the high-level discourse dependencies between chunks of texts.
Pair the Dots: Jointly Examining Training History and Test Stimuli for Model Interpretability
Any prediction from a model is made by a combination of learning history and test stimuli.
Improving Robustness and Generality of NLP Models Using Disentangled Representations
We show that models trained with the proposed criteria provide better robustness and domain adaptation ability in a wide range of supervised learning tasks.
Receptive Multi-granularity Representation for Person Re-Identification
Current stripe-based feature learning approaches have delivered impressive accuracy, but do not make a proper trade-off between diversity, locality, and robustness, which easily suffers from part semantic inconsistency for the conflict between rigid partition and misalignment.
CorefQA: Coreference Resolution as Query-based Span Prediction
In this paper, we present CorefQA, an accurate and extensible approach for the coreference resolution task.
Ranked #3 on
Coreference Resolution
on CoNLL 2012
(using extra training data)
Analyzing COVID-19 on Online Social Media: Trends, Sentiments and Emotions
At the time of writing, the ongoing pandemic of coronavirus disease (COVID-19) has caused severe impacts on society, economy and people's daily lives.
SAC: Accelerating and Structuring Self-Attention via Sparse Adaptive Connection
In this way, the model is able to select the most salient nodes and reduce the quadratic complexity regardless of the sequence length.
Non-Autoregressive Neural Dialogue Generation
Unfortunately, under the framework of the \sts model, direct decoding from $\log p(y|x) + \log p(x|y)$ is infeasible since the second part (i. e., $p(x|y)$) requires the completion of target generation before it can be computed, and the search space for $y$ is enormous.
Description Based Text Classification with Reinforcement Learning
We observe significant performance boosts over strong baselines on a wide range of text classification tasks including single-label classification, multi-label classification and multi-aspect sentiment analysis.
LAVA NAT: A Non-Autoregressive Translation Model with Look-Around Decoding and Vocabulary Attention
Non-autoregressive translation (NAT) models generate multiple tokens in one forward pass and is highly efficient at inference stage compared with autoregressive translation (AT) methods.
Teaching Machines to Converse
The ability of a machine to communicate with humans has long been associated with the general success of AI.
Dice Loss for Data-imbalanced NLP Tasks
Many NLP tasks such as tagging and machine reading comprehension are faced with the severe data imbalance issue: negative examples significantly outnumber positive examples, and the huge number of background examples (or easy-negative examples) overwhelms the training.
Ranked #1 on
Chinese Named Entity Recognition
on OntoNotes 4
(using extra training data)
Chinese Named Entity Recognition
Machine Reading Comprehension
+5
Coreference Resolution as Query-based Span Prediction
In this paper, we present an accurate and extensible approach for the coreference resolution task.
Layer Pruning for Accelerating Very Deep Neural Networks
In this paper, we propose an adaptive pruning method.
A Unified MRC Framework for Named Entity Recognition
Instead of treating the task of NER as a sequence labeling problem, we propose to formulate it as a machine reading comprehension (MRC) task.
Ranked #2 on
Nested Mention Recognition
on ACE 2004
(using extra training data)
Chinese Named Entity Recognition
Entity Extraction using GAN
+4
Large-scale Pretraining for Neural Machine Translation with Tens of Billions of Sentence Pairs
In this paper, we investigate the problem of training neural machine translation (NMT) systems with a dataset of more than 40 billion bilingual sentence pairs, which is larger than the largest dataset to date by orders of magnitude.
Query-Based Named Entity Recognition
In this paper, we propose a new strategy for the task of named entity recognition (NER).
Entity Extraction using GAN
Machine Reading Comprehension
+3
Relation-Aware Pyramid Network (RapNet) for temporal action proposal
In this technical report, we describe our solution to temporal action proposal (task 1) in ActivityNet Challenge 2019.
Deep Adversarial Learning for NLP
Adversarial learning is a game-theoretic learning paradigm, which has achieved huge successes in the field of Computer Vision recently.
DSReg: Using Distant Supervision as a Regularizer
In this paper, we aim at tackling a general issue in NLP tasks where some of the negative examples are highly similar to the positive examples, i. e., hard-negative examples.
Is Word Segmentation Necessary for Deep Learning of Chinese Representations?
Based on these observations, we conduct comprehensive experiments to study why word-based models underperform char-based models in these deep learning-based NLP tasks.
Entity-Relation Extraction as Multi-Turn Question Answering
In this paper, we propose a new paradigm for the task of entity-relation extraction.
Ranked #1 on
Relation Extraction
on ACE 2005
(Sentence Encoder metric)
DenseBody: Directly Regressing Dense 3D Human Pose and Shape From a Single Color Image
Recovering 3D human body shape and pose from 2D images is a challenging task due to high complexity and flexibility of human body, and relatively less 3D labeled data.
Glyce: Glyph-vectors for Chinese Character Representations
However, due to the lack of rich pictographic evidence in glyphs and the weak generalization ability of standard computer vision models on character data, an effective way to utilize the glyph information remains to be found.
Ranked #1 on
Chinese Sentence Pair Classification
on LCQMC
Chinese Dependency Parsing
Chinese Named Entity Recognition
+21
Pixel-Anchor: A Fast Oriented Scene Text Detector with Combined Networks
Recently, semantic segmentation and general object detection frameworks have been widely adopted by scene text detecting tasks.
Cascaded CNN-resBiLSTM-CTC: An End-to-End Acoustic Model For Speech Recognition
Automatic speech recognition (ASR) tasks are resolved by end-to-end deep learning models, which benefits us by less preparation of raw data, and easier transformation between languages.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
A novel pyramidal-FSMN architecture with lattice-free MMI for speech recognition
Deep Feedforward Sequential Memory Network (DFSMN) has shown superior performance on speech recognition tasks.
Sound Audio and Speech Processing
Generating More Interesting Responses in Neural Conversation Models with Distributional Constraints
Neural conversation models tend to generate safe, generic responses for most inputs.
Deep Reinforcement Learning for NLP
Many Natural Language Processing (NLP) tasks (including generation, language grounding, reasoning, information extraction, coreference resolution, and dialog) can be formulated as deep reinforcement learning (DRL) problems.
Learning Discriminative Features with Multiple Granularities for Person Re-Identification
Instead of learning on semantic regions, we uniformly partition the images into several stripes, and vary the number of parts in different local branches to obtain local feature representations with multiple granularities.
Ranked #3 on
Person Re-Identification
on SYSU-30k
(using extra training data)
IcoRating: A Deep-Learning System for Scam ICO Identification
For the best setting, the proposed system is able to identify scam ICO projects with 0. 83 precision.
Neural Net Models of Open-domain Discourse Coherence
In this paper, we describe domain-independent neural models of discourse coherence that are capable of measuring multiple aspects of coherence in existing sentences and can maintain coherence while generating new sentences.
Data Noising as Smoothing in Neural Network Language Models
Data noising is an effective technique for regularizing neural network models.
Data Distillation for Controlling Specificity in Dialogue Generation
We show that from such a set of subsystems, one can use reinforcement learning to build a system that tailors its output to different input contexts at test time.
Learning to Decode for Future Success
We introduce a simple, general strategy to manipulate the behavior of a neural decoder that enables it to generate outputs that have specific properties of interest (e. g., sequences of a pre-specified length).
Adversarial Learning for Neural Dialogue Generation
In this paper, drawing intuition from the Turing test, we propose using adversarial training for open-domain dialogue generation: the system is trained to produce sequences that are indistinguishable from human-generated dialogue utterances.
Ranked #1 on
Dialogue Generation
on Amazon-5
Understanding Neural Networks through Representation Erasure
While neural networks have been successfully applied to many natural language processing tasks, they come at the cost of interpretability.
Learning through Dialogue Interactions by Asking Questions
A good dialogue agent should have the ability to interact with users by both responding to questions and by asking questions, and importantly to learn from both types of interaction.
Dialogue Learning With Human-In-The-Loop
An important aspect of developing conversational agents is to give a bot the ability to improve through communicating with humans and to learn from the mistakes that it makes.
A Simple, Fast Diverse Decoding Algorithm for Neural Generation
We further propose a variation that is capable of automatically adjusting its diversity decoding rates for different inputs using reinforcement learning (RL).
Deep Reinforcement Learning for Dialogue Generation
Recent neural models of dialogue generation offer great promise for generating responses for conversational agents, but tend to be shortsighted, predicting utterances one at a time while ignoring their influence on future outcomes.
Neural Net Models for Open-Domain Discourse Coherence
In this paper, we describe domain-independent neural models of discourse coherence that are capable of measuring multiple aspects of coherence in existing sentences and can maintain coherence while generating new sentences.
A Persona-Based Neural Conversation Model
We present persona-based models for handling the issue of speaker consistency in neural response generation.
Mutual Information and Diverse Decoding Improve Neural Machine Translation
We introduce an alternative objective function for neural MT that maximizes the mutual information between the source and target sentences, modeling the bi-directional dependency of sources and targets.
Learning multi-faceted representations of individuals from heterogeneous evidence using neural networks
Inferring latent attributes of people online is an important social computing task, but requires integrating the many heterogeneous sources of information available on the web.
A Diversity-Promoting Objective Function for Neural Conversation Models
Sequence-to-sequence neural network models for generation of conversational responses tend to generate safe, commonplace responses (e. g., "I don't know") regardless of the input.
Reflections on Sentiment/Opinion Analysis
In this paper, we described possible directions for deeper understanding, helping bridge the gap between psychology / cognitive science and computational approaches in sentiment/opinion analysis literature.
A Hierarchical Neural Autoencoder for Paragraphs and Documents
Natural language generation of coherent long texts like paragraphs or longer documents is a challenging problem for recurrent networks models.
Visualizing and Understanding Neural Models in NLP
While neural networks have been successfully applied to many NLP tasks the resulting vector-based models are very difficult to interpret.
Do Multi-Sense Embeddings Improve Natural Language Understanding?
Learning a distinct representation for each sense of an ambiguous word could lead to more powerful and fine-grained models of vector-space representations.
The NLP Engine: A Universal Turing Machine for NLP
It is commonly accepted that machine translation is a more complex task than part of speech tagging.
When Are Tree Structures Necessary for Deep Learning of Representations?
Recursive neural models, which use syntactic parse trees to recursively generate representations bottom-up, are a popular architecture.
Feature Weight Tuning for Recursive Neural Networks
This paper addresses how a recursive neural network model can automatically leave out useless information and emphasize important evidence, in other words, to perform "weight tuning" for higher-level representation acquisition.
Inferring User Preferences by Probabilistic Logical Reasoning over Social Networks
by building a probabilistic model that reasons over user attributes (the user's location or gender) and the social network (the user's friends and spouse), via inferences like homophily (I am more likely to like sushi if spouse or friends like sushi, I am more likely to like the Knicks if I live in New York).
What a Nasty day: Exploring Mood-Weather Relationship from Twitter
While it has long been believed in psychology that weather somehow influences human's mood, the debates have been going on for decades about how they are correlated.
Early Stage Influenza Detection from Twitter
In this paper, we investigate the real-time flu detection problem on Twitter data by proposing Flu Markov Network (Flu-MN): a spatio-temporal unsupervised Bayesian algorithm based on a 4 phase Markov Network, trying to identify the flu breakout at the earliest stage.
Query-focused Multi-document Summarization: Combining a Novel Topic Model with Graph-based Semi-supervised Learning
Graph-based semi-supervised learning has proven to be an effective approach for query-focused multi-document summarization.
A Novel Feature-based Bayesian Model for Query Focused Multi-document Summarization
Both supervised learning methods and LDA based topic model have been successfully applied in the field of query focused multi-document summarization.
