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- Named entity recognition using an HMM-based chunk tagger809
A Hidden Markov Model and an HMM-based chunk tagger is proposed, from which a named entity (NE) recognition system is built to recognize and classify names, times and numerical quantities, and the NER problem can be resolved effectively.
- Using Cross-Entity Inference to Improve Event Extraction294
This paper proposes a new method of event extraction by well using cross-entity inference that regard entity-type consistency as key feature to predict event mentions and adopts this inference method to improve the traditional sentence-level event extraction system.
- Recognizing names in biomedical texts: a machine learning approach287
The PowerBioNE system is the first system which deals with the cascaded entity name phenomenon and the HMM and the k-NN algorithm outperform other models, such as back-off HMM, linear interpolated H MM, support vector machines, C4.5 rules and RIPPER, by effectively capturing the local context dependency and resolving the data sparseness problem.
- A composite kernel to extract relations between entities with both flat and structured features286
This study illustrates that the composite kernel can effectively capture both flat and structured features without the need for extensive feature engineering, and can also easily scale to include more features.
- Multi-criteria-based active learning for named entity recognition261
A multi-criteria-based active learning approach is proposed and effectively applied to named entity recognition and includes all the criteria using two selection strategies, both of which result in less labeling cost than single-criterion-based method.
- RETRACTED: Multi-modal Graph Fusion for Named Entity Recognition with Targeted Visual Guidance259
A unified multi-modal graph fusion (UMGF) approach for MNER that achieves an attention-based multi- modal representation for each word and performs entity labeling with a CRF decoder.
- Modeling both Context- and Speaker-Sensitive Dependence for Emotion Detection in Multi-speaker Conversations239
This paper proposes a conversational graph-based convolutional neural network that represents each utterance and each speaker as a node and can be symbolized as a large heterogeneous graph and the emotion detection task can be recast as a classification problem of the utterance nodes in the graph.
- Tree Kernel-Based Relation Extraction with Context-Sensitive Structured Parse Tree Information221
Evaluation on the ACE RDC corpora shows that the dynamic context-sensitive tree span is much more suitable for relation extraction than SPT and the tree kernel outperforms the state-of-the-art Collins and Duffy’s convolution tree kernel.
- Semi-Supervised Learning for Imbalanced Sentiment Classification191
This paper investigates a more common case of semi-supervised learning for imbalanced sentiment classification, in which various random subspaces are dynamically generated to deal with the imbalanced class distribution problem.
- Effective adaptation of a Hidden Markov Model-based named entity recognizer for biomedical domain169
A general Hidden Markov Model-based named entity recognizer effectively to biomedical domain is adapted and a simple algorithm to solve the abbreviation problem and a rule-based method to deal with the cascaded phenomena in biomedical domain are presented.
- Joint Multi-modal Aspect-Sentiment Analysis with Auxiliary Cross-modal Relation Detection168
This paper is the first to jointly perform multi-modal ATE (MATE) and multi- modal ASC (MASC), and it proposes a multi-Modal joint learning approach with auxiliary cross-modAL relation detection for multi- Modal aspect-level sentiment analysis (MALSA).
- Coreference resolution using competition learning approach164
The experimental results show that the proposed competition learning approach to coreference resolution can outperform those based on the single-candidate model and applies a candidate filter to reduce the computational cost and data noises during training and resolution.
- Modeling Source Syntax for Neural Machine Translation150
This paper shows that source syntax can be explicitly incorporated into NMT effectively to provide further improvements, and linearize parse trees of source sentences to obtain structural label sequences.
- Sentiment Classification and Polarity Shifting142
A feature selection method is adopted to automatically generate the training data for a binary classifier on polarity shifting detection of sentences, and each document in the original polarity classification training data is split into two partitions which are used to train two base classifiers respectively for further classifier combination.
- Exploiting constituent dependencies for tree kernel-based semantic relation extraction140
Evaluation on the ACE RDC 2004 corpus shows that the dynamic syntactic parse tree outperforms all previous tree spans, and the composite kernel combining this tree kernel with a linear state-of-the-art feature-based kernel, achieves the so far best performance.
- Enhancing HMM-based biomedical named entity recognition by studying special phenomena128
This work proposes a rich set of features, including orthographic, morphological, part-of-speech, and semantic trigger features, integrated via a Hidden Markov Model with back-off modeling for biomedical abbreviation recognition and cascaded named entity recognition.
- Stance Detection with Hierarchical Attention Network125
This work presents a neural model to fully employ various linguistic information to construct the document representation and proposes a hierarchical attention network to weigh the importance ofVarious linguistic information, and learn the mutual attention between the document and the linguistic information.
- Employing Personal/Impersonal Views in Supervised and Semi-Supervised Sentiment Classification124
An ensemble method and a co-training algorithm are explored to employ the two views, personal and impersonal views, in both supervised and semi-supervised sentiment classification respectively.
- Recognition of protein/gene names from text using an ensemble of classifiers109
An ensemble of classifiers for biomedical name recognition in which three classifiers, one Support Vector Machine and two discriminative Hidden Markov Models, are combined effectively using a simple majority voting strategy is proposed.
- Aspect Sentiment Classification with both Word-level and Clause-level Attention Networks107
The need for incorporating the importance degrees of both words and clauses inside a sentence is highlighted and a hierarchical network with both word-level and clause-level attentions to aspect sentiment classification is proposed.
- Modeling Graph Structure in Transformer for Better AMR-to-Text Generation105
This paper proposes a novel structure-aware self-attention approach to better model the relations between indirectly connected concepts in the state-of-the-art seq2seq model, i.e. the Transformer.
- Cache-based Document-level Statistical Machine Translation101
This paper presents three kinds of caches to store relevant document-level information: a dynamic cache, which stores bilingual phrase pairs from the best translation hypotheses of previous sentences in the test document; a static cache,which stores relevantilingual phrase pairs extracted from similar bilingual document pairs in the training parallel corpus; and a topic cache,Which stores the target-side topic words related with the test documents in the source-side.
- Chemical-induced disease relation extraction via convolutional neural network98
This article describes the work on the BioCreative-V chemical–disease relation (CDR) extraction task, which employed a maximum entropy (ME) model and a convolutional neural network model for relation extraction at inter- and intra-sentence level, respectively.
- Active Learning for Imbalanced Sentiment Classification95
This paper proposes a novel active learning approach, named co-selecting, that employs two feature subspace classifiers to collectively select most informative minority-class samples for manual annotation by leveraging a certainty measurement and an uncertainty measurement, to reduce human-annotation efforts.
- Building Chinese Discourse Corpus with Connective-driven Dependency Tree Structure94
Preliminary evaluation justifies the appropriateness of the CDT scheme to Chinese discourse analysis and the usefulness of the manually annotated CDTB corpus.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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