Key information extraction (KIE) from visually rich documents, such as receipts and forms, involves a deep understanding of textual, visual, and layout feature information. Transformers fine-tuned for KIE achieve state-of-the-art performance but lack generality and portability across different domains. In contrast, vision large language models (VLLMs) offer higher flexibility and zero-shot capability but fall short with domain-specific layout relations unless performing a resource-demanding supervised fine-tuning. To reach the best compromise solution between lightweight models and VLLMs, we propose KIEPrompter, a cost-effective LLM-based KIE approach that leverages the predictions of lightweight models as external knowledge injected into VLLM prompts. By incorporating these auxiliary predictions, VLLMs are guided to attend relevant multimodal content without ad hoc training. The accuracy results achieved by KIEPrompter in three benchmark document collections are superior to those of VLLMs in both zero-shot and layout-sensitive scenarios. We compare various strategies for incorporating lightweight model predictions, ranging from coarse-grained predictions without explicit confidence scores to fine-grained per-element network logits. We also demonstrate that our approach is robust to the absence of specific classes in trained lightweight models, as the VLLMs' pre-training compensates for the limited generality of lightweight models.

Identifying entities in medical text often involves dealing with discontinuous word sequences or entities sharing a common head, which pose significant challenges for traditional Named Entity Recognition (NER) systems. Current state-of-the-art discontinuous NER models typically process each sentence in isolation, overlooking valuable intra-sentence context. However, recent studies have shown that large language models (LLMs) perform exceptionally well when provided such context. In this work, we introduce DocDiscNER, a novel approach to discontinuous NER, which features (i) a context-aware document chunking method that provides contextually related segments as input for LLM-based NER models; (ii) a dataset and approach for coordination ellipses resolution, to address candidate spans sharing common heads and (iii) a self-consistency decoding strategy that uses self-ensembling and a majority voting mechanism to select the most consistent predictions as entity spans. We demonstrate the effectiveness and generalisability of our method on three discontinuous NER benchmarks, achieving new state-of-the-art (SOTA) performance on two of them–CADEC and ShARe-14 (2.48 and 2.2 absolute F1 points gain, respectively); while achieving competitive results on ShARe-13. In addition, our method surpasses previous SOTA performance specifically in recognising discontinuous mentions. A deeper analysis unveils that incorporating semantically relevant context significantly enhances overall NER performance compared to using individual sentences as input.

Visually Rich Document Understanding (VRDU) has emerged as a critical field in document intelligence, enabling automated extraction of key information from complex documents across domains such as medical, financial, and educational applications. However, form-like documents pose unique challenges due to their complex layouts, multi-stakeholder involvement, and high structural variability. Addressing these issues, the VRD-IU Competition was introduced, focusing on extracting and localizing key information from multi-format forms within the Form-NLU dataset, which includes digital, printed, and handwritten documents. This paper presents insights from the competition, which featured two tracks: Track A, emphasizing entity-based key information retrieval, and Track B, targeting end-to-end key information localization from raw document images. With over 20 participating teams, the competition showcased various state-of-the-art methodologies, including hierarchical decomposition, transformer-based retrieval, multimodal feature fusion, and advanced object detection techniques. The top-performing models set new benchmarks in VRDU, providing valuable insights into document intelligence.

Discontinuous Named Entity Recognition (DNER) presents a challenging problem where entities may be scattered across multiple non-adjacent tokens, making traditional sequence labelling approaches inadequate. Existing methods predominantly rely on custom tagging schemes to handle these discontinuous entities, resulting in models tightly coupled to specific tagging strategies and lacking generalisability across diverse datasets. To address these challenges, we propose TriG-NER, a novel Triplet-Grid Framework that introduces a generalisable approach to learning robust token-level representations for discontinuous entity extraction. Our framework applies triplet loss at the token level, where similarity is defined by word pairs existing within the same entity, effectively pulling together similar and pushing apart dissimilar ones. This approach enhances entity boundary detection and reduces the dependency on specific tagging schemes by focusing on word-pair relationships within a flexible grid structure. We evaluate TriG-NER on three benchmark DNER datasets and demonstrate significant improvements over existing grid-based architectures. These results underscore our framework's effectiveness in capturing complex entity structures and its adaptability to various tagging schemes, setting a new benchmark for discontinuous entity extraction.

Abstract Stock price prediction remains a critical challenge in financial research due to its potential to inform strategic decision-making. Existing approaches predominantly focus on two key tasks: (1) regression, which forecasts future stock prices, and (2) classification, which identifies trading signals such as buy, sell, or hold. However, the inherent limitations of financial data hinder effective model training, often leading to suboptimal performance. To mitigate this issue, prior studies have expanded datasets by aggregating historical data from multiple companies. This strategy, however, fails to account for the unique characteristics and interdependencies among individual stocks, thereby reducing predictive accuracy. To address these limitations, we propose a novel BiLSTM-GAT-AM model that integrates bidirectional long short-term memory (BiLSTM) networks with graph attention networks (GAT) and an attention mechanism (AM). Unlike conventional graph-based models that define edges based solely on technical or fundamental relationships, our approach employs a dual-graph structure: one graph captures technical similarities, while the other encodes fundamental industry relationships. These two representations are aligned through an attention mechanism, enabling the model to exploit both technical and fundamental insights for enhanced stock market predictions. We conduct extensive experiments, including ablation studies and comparative evaluations against baseline models. The results demonstrate that our model achieves superior predictive performance. Furthermore, leveraging the model’s forecasts, we construct an optimized portfolio and conduct backtesting on the test dataset. Empirical results indicate that our portfolio consistently outperforms both baseline models and the S&P 500 index, highlighting the effectiveness of our approach in stock market prediction and portfolio optimization.

The significance of mental health classification is paramount in contemporary society, where digital platforms serve as crucial sources for monitoring individuals' well-being. However, existing social media mental health datasets primarily consist of text-only samples, potentially limiting the efficacy of models trained on such data. Recognising that humans utilise cross-modal information to comprehend complex situations or issues, we present a novel approach to address the limitations of current methodologies. In this work, we introduce a Multimodal and Multi-Teacher Knowledge Distillation model for Mental Health Classification, leveraging insights from cross-modal human understanding. Unlike conventional approaches that often rely on simple concatenation to integrate diverse features, our model addresses the challenge of appropriately representing inputs of varying natures (e.g., texts and sounds). To mitigate the computational complexity associated with integrating all features into a single model, we employ a multimodal and multi-teacher architecture. By distributing the learning process across multiple teachers, each specialising in a particular feature extraction aspect, we enhance the overall mental health classification performance. Through experimental validation, we demonstrate the efficacy of our model in achieving improved performance.

Visual storytelling aims to automatically generate a coherent story based on a given image sequence.Unlike tasks like image captioning, visual stories should contain factual descriptions, worldviews, and human social commonsense to put disjointed elements together to form a coherent and engaging human-writeable story.However, most models mainly focus on applying factual information and using taxonomic/lexical external knowledge when attempting to create stories.This paper introduces SCO-VIST, a framework representing the image sequence as a graph with objects and relations that includes human action motivation and its social interaction commonsense knowledge.SCO-VIST then takes this graph representing plot points and creates bridges between plot points with semantic and occurrence-based edge weights.This weighted story graph produces the storyline in a sequence of events using Floyd-Warshall's algorithm.Our proposed framework produces stories superior across multiple metrics in terms of visual grounding, coherence, diversity, and humanness, per both automatic and human evaluations.

Large language models are able to generate code for visualisations in response to simple user requests.This is a useful application and an appealing one for NLP research because plots of data provide grounding for language.However, there are relatively few benchmarks, and those that exist may not be representative of what users do in practice.This paper investigates whether benchmarks reflect realworld use through an empirical study comparing benchmark datasets with code from public repositories.Our findings reveal a substantial gap, with evaluations not testing the same distribution of chart types, attributes, and actions as real-world examples.One dataset is representative, but requires extensive modification to become a practical end-to-end benchmark.This shows that new benchmarks are needed to support the development of systems that truly address users' visualisation needs.These observations will guide future data creation, highlighting which features hold genuine significance for users.

This tutorial explores recent advancements in multimodal pretrained and large models, capable of integrating and processing diverse data forms such as text, images, audio, and video. Participants will gain an understanding of the foundational concepts of multimodality, the evolution of multimodal research, and the key technical challenges addressed by these models. We will cover the latest multimodal datasets and pretrained models, including those beyond vision and language. Additionally, the tutorial will delve into the intricacies of multimodal large models and instruction tuning strategies to optimise performance for specific tasks. Hands-on laboratories will offer practical experience with state-of-the-art multimodal models, demonstrating real-world applications like visual storytelling and visual question answering. This tutorial aims to equip researchers, practitioners, and newcomers with the knowledge and skills to leverage multimodal AI. ACM Multimedia 2024 is the ideal venue for this tutorial, aligning perfectly with our goal of understanding multimodal pretrained and large language models, and their tuning mechanisms.

Document understanding and information extraction include different tasks to understand a document and extract valuable information automatically. Recently, there has been a rising demand for developing document understanding among different domains, including business, law, and medicine, to boost the efficiency of work that is associated with a large number of documents. This workshop aims to bring together researchers and industry developers in the field of document intelligence and understanding diverse document types to boost automatic document processing and understanding techniques. We also release a data challenge on the recently introduced document-level VQA dataset, PDFVQA. The PDFVQA challenge examines the model's structural and contextual understandings on the natural full document level of multiple consecutive document pages by including questions with a sequence of answers extracted from multi-pages of the full document. This task helps to boost the document understanding step from the single-page level to the full document level understanding.

Extracting meaningful drug-related information chunks, such as adverse drug events (ADE), is crucial for preventing morbidity and saving many lives. Most ADEs are reported via an unstructured conversation with the medical context, so applying a general entity recognition approach is not sufficient enough. In this paper, we propose a new multi-aspect cross-integration framework for drug entity/event detection by capturing and aligning different context/language/knowledge properties from drug-related documents. We first construct multi-aspect encoders to describe semantic, syntactic, and medical document contextual information by conducting those slot tagging tasks, main drug entity/event detection, part-of-speech tagging, and general medical named entity recognition. Then, each encoder conducts cross-integration with other contextual information in three ways: the key-value cross, attention cross, and feedforward cross, so the multi-encoders are integrated in depth. Our model outperforms all SOTA on two widely used tasks, flat entity detection and discontinuous event extraction.

Natural language understanding typically maps single utterances to a dual level semantic frame, sentence level intent and slot labels at the word level. The best performing models force explicit interaction between intent detection and slot filling. We present a novel tri-level joint natural language understanding approach, adding domain, and explicitly exchange semantic information between all levels. This approach enables the use of multi-turn datasets which are a more natural conversational environment than single utterance. We evaluate our model on two multi-turn datasets for which we are the first to conduct joint slot-filling and intent detection. Our model outperforms state-of-the-art joint models in slot filling and intent detection on multi-turn data sets. We provide an analysis of explicit interaction locations between the layers. We conclude that including domain information improves model performance.

Attention mechanism has been used as an important component across Vision-and-Language(VL) tasks in order to bridge the semantic gap between visual and textual features. While attention has been widely used in VL tasks, it has not been examined the capability of different attention alignment calculation in bridging the semantic gap between visual and textual clues. In this research, we conduct a comprehensive analysis on understanding the role of attention alignment by looking into the attention score calculation methods and check how it actually represents the visual region’s and textual token’s significance for the global assessment. We also analyse the conditions which attention score calculation mechanism would be more (or less) interpretable, and which may impact the model performance on three different VL tasks, including visual question answering, text-to-image generation, text-and-image matching (both sentence and image retrieval). Our analysis is the first of its kind and provides useful insights of the importance of each attention alignment score calculation when applied at the training phase of VL tasks, commonly ignored in attention-based cross modal models, and/or pretrained models. Our code is available at: https://github.com/adlnlp/Attention_VL

Recognizing the layout of unstructured digital documents is crucial when parsing the documents into the structured, machine-readable format for downstream applications. Recent studies in Document Layout Analysis usually rely on visual cues to understand documents while ignoring other information, such as contextual information or the relationships between document layout components, which are vital to boost better layout analysis performance. Our Doc-GCN presents an effective way to harmonize and integrate heterogeneous aspects for Document Layout Analysis. We construct different graphs to capture the four main features aspects of document layout components, including syntactic, semantic, density, and appearance features. Then, we apply graph convolutional networks to enhance each aspect of features and apply the node-level pooling for integration. Finally, we concatenate features of all aspects and feed them into the 2-layer MLPs for document layout component classification. Our Doc-GCN achieves state-of-the-art results on three widely used DLA datasets: PubLayNet, FUNSD, and DocBank. The code will be released at https://github.com/adlnlp/doc_gcn

Text classification aims to assign labels to textual units by making use of global information. Recent studies have applied graph neural network (GNN) to capture the global word co-occurrence in a corpus. Existing approaches require that all the nodes (training and test) in a graph are present during training, which are transductive and do not naturally generalise to unseen nodes. To make those models inductive, they use extra resources, like pretrained word embedding. However, high-quality resource is not always available and hard to train. Under the extreme settings with no extra resource and limited amount of training set, can we still learn an inductive graph-based text classification model? In this paper, we introduce a novel inductive graph-based text classification framework, InducT-GCN (InducTive Graph Convolutional Networks for Text classification). Compared to transductive models that require test documents in training, we construct a graph based on the statistics of training documents only and represent document vectors with a weighted sum of word vectors. We then conduct one-directional GCN propagation during testing. Across five text classification benchmarks, our InducT-GCN outperformed state-of-the-art methods that are either transductive in nature or pre-trained additional resources. We also conducted scalability testing by gradually increasing the data size and revealed that our InducT-GCN can reduce the time and space complexity. The code is available on: https://github.com/usydnlp/InductTGCN.

Image-text retrieval task is a challenging task. It aims to measure the visual-semantic correspondence between an image and a text caption. This is tough mainly because the image lacks semantic context information as in its corresponding text caption, and the text representation is very limited to fully describe the details of an image. In this paper, we introduce Graph-based Dual-modal Representations (GraDual), including Vision-Integrated Text Embedding (VITE) and Context-Integrated Visual Embedding (CIVE), for image-text retrieval. The GraDual improves the coverage of each modality by exploiting textual context semantics for the image representation, and using visual features as a guidance for the text representation. To be specific, we design: 1) a dual-modal graph representation mechanism to solve the lack of coverage issue for each modality. 2) an intermediate graph embedding integration strategy to enhance the important pattern across other modality global features. 3) a dual-modal driven crossmodal matching network to generate a filtered representation of another modality. Extensive experiments on two benchmark datasets, MS-COCO and Flickr30K, demonstrates the superiority of the proposed GraDual in comparison to state-of-the-art methods.

Abstract As an essential component of human cognition, cause–effect relations appear frequently in text, and curating cause–effect relations from text helps in building causal networks for predictive tasks. Existing causality extraction techniques include knowledge-based, statistical machine learning (ML)-based, and deep learning-based approaches. Each method has its advantages and weaknesses. For example, knowledge-based methods are understandable but require extensive manual domain knowledge and have poor cross-domain applicability. Statistical machine learning methods are more automated because of natural language processing (NLP) toolkits. However, feature engineering is labor-intensive, and toolkits may lead to error propagation. In the past few years, deep learning techniques attract substantial attention from NLP researchers because of its powerful representation learning ability and the rapid increase in computational resources. Their limitations include high computational costs and a lack of adequate annotated training data. In this paper, we conduct a comprehensive survey of causality extraction. We initially introduce primary forms existing in the causality extraction: explicit intra-sentential causality, implicit causality, and inter-sentential causality. Next, we list benchmark datasets and modeling assessment methods for causal relation extraction. Then, we present a structured overview of the three techniques with their representative systems. Lastly, we highlight existing open challenges with their potential directions.

Recommender systems typically operate on high-dimensional sparse user-item matrices. Matrix completion is a very challenging task to predict one's interest based on millions of other users having each seen a small subset of thousands of items. We propose a G lobal-Local Kernel-based matrix completion framework, named GLocal-K, that aims to generalise and represent a high-dimensional sparse user-item matrix entry into a low dimensional space with a small number of important features. Our GLocal-K can be divided into two major stages. First, we pre-train an auto encoder with the local kernelised weight matrix, which transforms the data from one space into the feature space by using a 2d-RBF kernel. Then, the pre-trained auto encoder is fine-tuned with the rating matrix, produced by a convolution-based global kernel, which captures the characteristics of each item. We apply our GLocal-K model under the extreme low-resource setting, which includes only a user-item rating matrix, with no side information. Our model outperforms the state-of-the-art baselines on three collaborative filtering benchmarks: ML-100K, ML-1M, and Douban.

Intent classification and slot filling are two critical tasks for natural language understanding.Traditionally the two tasks proceeded independently.However, more recently joint models for intent classification and slot filling have achieved state-of-theart performance, and have proved that there exists a strong relationship between the two tasks.In this paper, we propose a bi-directional joint model for intent classification and slot filling, which includes a multi-stage hierarchical process via BERT and bi-directional joint natural language understanding mechanisms, including intent2slot and slot2intent, to obtain mutual performance enhancement between intent classification and slot filling.The evaluations show that our model achieves state-ofthe-art results on intent classification accuracy, slot filling F1, and significantly improves sentence-level semantic frame accuracy when applied to publicly available benchmark datasets, ATIS (88.6%) and SNIPS (92.8%).

The Federal Reserve System (the Fed) plays a significant role in affecting monetary policy and financial conditions worldwide. Although it is important to analyse the Fed's communications to extract useful information, it is generally long-form and complex due to the ambiguous and esoteric nature of content. In this paper, we present FedNLP, an interpretable multi-component Natural Language Processing (NLP) system to decode Federal Reserve communications. This system is designed for end-users to explore how NLP techniques can assist their holistic understanding of the Fed's communications with NO coding. Behind the scenes, FedNLP uses multiple NLP models from traditional machine learning algorithms to deep neural network architectures in each downstream task. The demonstration shows multiple results at once including sentiment analysis, summary of the document, prediction of the Federal Funds Rate movement and visualization for interpreting the prediction model's result. Our application system and demonstration are available at https://fednlp.net.

Text-to-image multimodal tasks, generating/retrieving an image from a given text description, are extremely challenging tasks since raw text descriptions cover quite limited information in order to fully describe visually realistic images.We propose a new visual contextual text representation for text-to-image multimodal tasks, VICTR, which captures rich visual semantic information of objects from the text input.First, we use the text description as initial input and conduct dependency parsing to extract the syntactic structure and analyse the semantic aspect, including object quantities, to extract the scene graph.Then, we train the extracted objects, attributes, and relations in the scene graph and the corresponding geometric relation information using Graph Convolutional Networks, and it generates text representation which integrates textual and visual semantic information.The text representation is aggregated with word-level and sentencelevel embedding to generate both visual contextual word and sentence representation.For the evaluation, we attached VICTR to the state-of-the-art models in text-to-image generation.VICTR is easily added to existing models and improves across both quantitative and qualitative aspects 1 .

Online abusive language detection (ALD) has become a societal issue of increasing importance in recent years.Several previous works in online ALD focused on solving a single abusive language problem in a single domain, like Twitter, and have not been successfully transferable to the general ALD task or domain.In this paper, we introduce a new generic ALD framework, MACAS, which is capable of addressing several types of ALD tasks across different domains.Our generic framework covers multi-aspect abusive language embeddings that represent the target and content aspects of abusive language and applies a textual graph embedding that analyses the user's linguistic behaviour.Then, we propose and use the cross-attention gate flow mechanism to embrace multiple aspects of abusive language.Quantitative and qualitative evaluation results show that our ALD algorithm rivals or exceeds the six state-of-the-art ALD algorithms across seven ALD datasets covering multiple aspects of abusive language and different online community domains.The code can be downloaded from https://github.com/usydnlp/MACAS.

In traditional Chinese medicine (TCM), herbal prescriptions are accumulated from doctors' clinical experience and play an essential role in treatment process for more than thousands of years. Mining their containing many-to-many relationship between symptoms and herbs are important for both clinical practice and novel prescriptions development. Previously, the research attention in this field is attracted by topic models and multi-label classification approaches, but they don't fully capture the correlations of labels and achieve very promising performance. Alternatively, we problematic it as a machine translation problem, that the source sentence is formed by symptoms and the target sentence consists of herbs. Two sentences are both in weakly order. In terms of this assumption, we propose a novel sequence-to-sequence (seq2seq) architecture, namely TCM translator, to translate symptoms to herbs. Seq2seq, consisting of an encoder and a decoder, is a well known framework for resolving the machine translation problem in natural language processing (NLP) community. It maps a bag of symptoms to the latent space, which is decoded to the target sentence, i.e., herbs. In this paper, transformer is used as our encoder to explore the latent correlations of symptoms and LSTM is implemented as the decoder to generate the herbs sequentially. The experimental results demonstrate that our proposed approach is effective and outperforms other baseline models by a substantial margin.

One way to extract patterns from clinical records is to consider each patient record as a bag with various number of instances in the form of symptoms. Medical diagnosis is to discover informative ones first and then map them to one or more diseases. In many cases, patients are represented as vectors in some feature space and a classifier is applied after to generate diagnosis results. However, in many real-world cases, data is often of low-quality due to a variety of reasons, such as data consistency, integrity, completeness, accuracy, etc. In this paper, we propose a novel approach, attention based multi-instance neural network (AMINet), to make the single disease classification only based on the existing and valid information in the real-world outpatient records. In the context of a patient, it takes a bag of instances as input and output the bag label directly in end-to-end way. Embedding layer is adopted at the beginning, mapping instances into an embedding space which represents the individual patient condition. The correlations among instances and their importance for the final classification are captured by multi-head attention transformer, instance-level multi-instance pooling and bag-level multi-instance pooling. The proposed approach was test on two non-standardized and highly imbalanced datasets, one in the Traditional Chinese Medicine (TCM) domain and the other in the Western Medicine (WM) domain. Our preliminary results show that the proposed approach outperforms all baselines results by a significant margin.

This modern day, the amount of digital text documents is enormous and cover almost all fields and industry. Natural Language Processing (NLP) looks into systematically deriving information from text written in natural language. A task under NLP, Relation Extraction (ER) focus on identifying relations from natural text. It has found significant application on biomedical publications, where it has been used to identify protein-to-protein interaction and gene-to-disease relationships in biomedical publications. Such application is also effective on Traditional Chinese Medicine (TCM) publications. This research identifies two forms of relations in TCM publications: Effect Relation and Conditional Effect Relation. This research introduces and compares two extraction approaches, in which also address some of the more Chinese-specific NLP problems, such as word segmentation and flexible syntactic structure.

BACKGROUND: Recent quality control of complex mixtures, including herbal medicines, is not limited to chemical chromatographic definition of one or two selected compounds; multivariate linear regression methods with dimension reduction or regularisation have been used to predict the bioactivity capacity from the chromatographic fingerprints of the herbal extracts. The challenge of this type of analysis requires a multi-dimensional approach at two levels: firstly each herb comprises complex mixtures of active and non-active chemical components; and secondly there are many factors relating to the growth, production, and processing of the herbal products. All these factors result in the significantly diverse concentrations of bioactive compounds in the herbal products. Therefore, it is imminent to have a predictive model with better generalisation that can accurately predict the bioactivity capacity of samples when only the chemical fingerprints data are available. RESULTS: In this study, the algorithm of Stacking Multivariate Linear Regression (SMLR) and a few other commonly used chemometric approaches were evaluated. They were to predict the Cluster of Differentiation 80 (CD80) expression bioactivity of a commonly used herb, Astragali Radix (AR), from the corresponding chemical chromatographic fingerprints. SMLR provides a superior prediction accuracy in comparison with the other multivariate linear regression methods of PCR, PLSR, OPLS and EN in terms of MSEtest and the goodness of prediction of test samples. CONCLUSIONS: SMLR is a better platform than some multivariate linear regression methods. The first advantage of SMLR is that it has better generalisation to predict the bioactivity capacity of herbal medicines from their chromatographic fingerprints. Future studies should aim to further improve the SMLR algorithm. The second advantage of SMLR is that single chemical compounds can be effectively identified as highly bioactive components which demands further CD80 bioactivity confirmation..

Currently, there are many multivariate linear regression algorithms being used for predicting the bioactive capacity of herbal formulae or herbal extracts from their chromatographic fingerprints, such as Principal Component Regression (PCR), Partial Least Squares Regression (PLSR), Orthogonal Projections to Latent Structures (OPLS) and Elastic Net (EN). In this study, the predicting performance and the complexity of predictive models developed by PCR, PLSR, OPLS, and EN are evaluated and compared using a set of chromatographic fingerprints of Astragali Radix (AR) and their corresponding bioactivity: Cluster of Differentiation 80 (CD80).

In order to separate the chromatogram peaks and spectra from the High Performance Liquid Chromatography with Diode Array Detector (HPLC-DAD) data set, a separation model of Generalized Reference Curve Measurement and its solution by multitarget Bare Bones Particle Swarm Optimization (GRCMmBBPSO) is proposed in this paper. Firstly, parameters are constructed which will generate Reference Curves (RCs) for chromatogram peaks. Secondly, the GRCM model is proposed to transform all these parameters to scalar values, which indicate the possibility for the HPLC-DAD data set containing chromatogram peaks similar to the RCs constructed by corresponding parameters. Then, the algorithm of mBBPSO is introduced to calculate the optimal parameters by minimizing the scalar values. Finally, the spectra are estimated. Through simulations and experiments, following conclusions are drawn: (1) the GRCMmBBPSO method can extract chromatogram peaks from the simulation data set without knowing the number of the compounds in advance even when a severe overlap and white noise exist; (2) the GRCMmBBPSO method can be applied to real HPLC-DAD data set.

BACKGROUND: The 3D chromatogram generated by High Performance Liquid Chromatography-Diode Array Detector (HPLC-DAD) has been researched widely in the field of herbal medicine, grape wine, agriculture, petroleum and so on. Currently, most of the methods used for separating a 3D chromatogram need to know the compounds' number in advance, which could be impossible especially when the compounds are complex or white noise exist. New method which extracts compounds from 3D chromatogram directly is needed. METHODS: In this paper, a new separation model named parallel Independent Component Analysis constrained by Reference Curve (pICARC) was proposed to transform the separation problem to a multi-parameter optimization issue. It was not necessary to know the number of compounds in the optimization. In order to find all the solutions, an algorithm named multi-areas Genetic Algorithm (mGA) was proposed, where multiple areas of candidate solutions were constructed according to the fitness and distances among the chromosomes. RESULTS: Simulations and experiments on a real life HPLC-DAD data set were used to demonstrate our method and its effectiveness. Through simulations, it can be seen that our method can separate 3D chromatogram to chromatogram peaks and spectra successfully even when they severely overlapped. It is also shown by the experiments that our method is effective to solve real HPLC-DAD data set. CONCLUSIONS: Our method can separate 3D chromatogram successfully without knowing the compounds' number in advance, which is fast and effective.

In order to separate a 3D chromatography, which is generated from High Performance Liquid Chromatography-Diode Array Detector (HPLC-DAD), into chromatograms and spectra, we proposed a model called parallel Independent Component Analysis constrained by Reference Curve (pICARC), which transforms the separation problem to a multi-parameter optimization issue. Then, A new algorithm named multi-areas Genetic Algorithm (mGA) is developed to search multiple solutions in parallel. It was demonstrated that our approach could successfully separate a given HPLC-DAD dataset into chromatograms and spectra with little errors and in short computational time.

Chinese Medicine Formulae (CMF) is the most common treatment by prescribing sets of herbs to address the patient's syndromes and symptoms in Traditional Chinese Medicine (TCM). Genetic Algorithm (GA) was used to model the discovery process in this paper. Since a fitness function plays a key role in GA, the definition of such a function carries a specific semantic meaning and may yield different results. A change in the fitness function will create a different landscape for the GA to solve. In other words, each function will generate different answers for a given dataset. This paper aims to understand the impact and the appropriateness of a fitness function to the discovery of symptom-herb relationship. Four different fitness functions were applied to an insomnia dataset. These fitness functions use either the frequency or efficacy as metrics. Some fitness functions were successful to extract core and peripheral sets from the datasets. Some of the results were even shown to be statistical significant, however, not all functions are appropriate for the discovery process. The impact and implications of crafting a fitness function should be carefully considered.

Traditional Chinese Medicine (TCM) is a holistic approach to medicine which has been in use in China for thousands of years. The main treatment, Chinese Medicine Formula, is prescribed by combining sets of herbs to address the patient's syndromes and symptoms based on clinical diagnosis. Although herbs are often combined based on various classical formulas, the underlying principles for the choice of herbs are not well understood. In this paper, we apply a set-theoretic approach to explore the complex relationships amongst herbs in TCM clinical prescriptions using Boolean logic.

Traditional Chinese Medicine (TCM) is a complementary alternative medical approach. Its holistic approach is drastically different from the western medicine (WM). Upon the gathering of various symptoms in a diagnosis, a TCM practitioner prescribes treatment methods, of which herbal medicine is still one of the most popular. Each formula consists of multiple herbs. Since it is not a one-to-one mapping between symptom and herb, overlapping subsets of herbs are meant to address sets of overlapping symptoms. As a result, the discovery of the symptoms-herbs relationship is a crucial step to the research of the underlying TCM principle. The discovery of many existing formulas took a long time to stabilize to the current configurations. In this paper, the relationship discovery is argued to be more than just an evolutionary process, but a coevolutionary process, i.e. a set of symptoms searches for candidate sets of herbs, while a given set of herbs are appropriate for multiple sets of symptoms. In other words, a well recognized symptoms-herbs relationship is the result of a dynamic equilibrium of two inter-related evolutionary processes. This model of discovery was implemented using a Combined Gene Genetic Algorithm (CoGA1) where the symptoms and herbs are encoded in the same chromosome to evolve over time. The algorithm was tested with an insomnia dataset from a TCM hospital. The algorithm was able to find the symptoms-herbs relationships that are consistent with TCM principles and have better fitness from Simple GA.

In this paper, we investigate the use of data mining, in particular the text classification and co-training techniques, to identify more relevant passages based on a small set of labeled passages obtained from the blind feedback of a retrieval system. The data mining results are used to expand query terms and to re-estimate some of the parameters used in a probabilistic weighting function. We evaluate the data mining based feedback method on the TREC HARD data set. The results show that data mining can be successfully applied to improve the text retrieval performance. We report our experimental findings in detail.

The imbalance dataset problem arises in many domains, such as Web page search, scam sites detection. In this paper, we propose an alternative re-sampling approach to deal with imbalance datasets. We demonstrate this approach with a concrete implementation and it has shown promising results when compared to other standard approaches that deals with imbalance dataset. We have also performed an analysis of the data complexity to help understand imbalanced dataset, which has also shown to be a promising approach

When dealing with information overload from the Internet, such as the classification of Web pages and the filtering of email spam, a new technique called co-training has been shown to be a promising approach to help build more accurate classifiers. Co-training allows classifiers to learn with fewer labelled documents by taking advantage of the more abundant unclassified documents. However, conventional co-training requires the dataset to be described by two disjoint and natural feature sets that are sufficiently redundant. In many practical situations, it is not intuitively obvious how to obtain two natural feature sets. This paper shows that when only a single natural feature set is used, the performance of co-training is beneficial in the application of email classification.

We present a neural network based system for automated e-mail filing into folders and anti-spam filtering. The experiments show that it is more accurate than several other techniques. We also investigate the effects of various feature selection, weighting and normalization methods, and also the portability of the anti-spam filter across different users.

We present INTIMATE, a Web-based movie recommender that makes suggestions by using text categorization to learn from movie synopses. The performance of various feature representations, feature selectors, feature weighting mechanisms and classifiers is evaluated and discussed. INTIMATE was also compared with a feature-based movie recommender. The results show that the text-based approach outperforms the feature-based if the ratio of the number of user ratings to the vocabulary size is high.