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Showing 1 to 20 of 76 for “"Entity Recognition"”.
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SKEWER: Sentiment Knowledge Extraction with Entity Recognition
<p>The California state legislature introduces approximately 5,000 new bills each legislative session. While the legislative hearings are recorded on video, the recordings are not easily accessible to the public. The lack of official transcripts or summaries also increases the effort required to …
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Microbial named entity recognition using BERT models
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-08-01
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Minimally-supervised Methods for Arabic Named Entity Recognition
Named Entity Recognition (NER) has attracted much attention over the past twenty years, as a main task of Information Extraction. The current dominant techniques for addressing NER are supervised methods that can achieve high performance, but require new manually annotated data for every new domain …
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Specialized Named Entity Recognition for Breast Cancer Subtyping
… information from text on various topics. Named Entity Recognition (NER), is one way to automate knowledge extraction of raw text. NER is defined as the task of identifying named entities from text using labels such as people, dates, locations, diseases, and proteins. There are several NLP tools …
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Entity recognition for multi-modal socio-technical systems
Entity Recognition (ER) can be used as a method for extracting information about socio-technical systems from unstructured, natural language text data. This process is limited by the set of entity classes considered in many current ER solutions. In this thesis, we report on the development of an ER …
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NumNER: Numerical named entity recognition in scientific literature
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms
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Domain-agnostic named entity recognition on unstructured text
Named Entity Recognition (NER) is the task of extracting informing entities belonging to predefined semantic classes from raw text. These semantic classes could be general-purpose like a person, location or domain-specific like genes, protein names in biomedical texts. NER has widespread …
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Consistency-aware and LLM-assisted methods for named entity recognition
Named Entity Recognition (NER) is a fundamental task in natural language processing and serves as a critical component for many downstream applications, including information extraction, biomedical text mining, and knowledge graph construction. Despite significant progress with neural models, most …
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Character language models for generalization of multilingual named entity recognition
"State-of-the-art Named Entity Recognition (NER) models usually achieve high performance on entities that they have seen in training data, but a significantly lower performance on unseen entities. This is one of the key reasons in performance degradation observed when NER models are evaluated on …
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Enhancing E-commerce Dataset recommendations using BERT and Named Entity Recognition
… Processing, and Query Processing. It uses Named Entity Recognition (NER) to enrich incomplete metadata by extracting contextual information and applies Term Frequency-Inverse Document Frequency (TF-IDF) alongside BERT embeddings to capture both keyword relevance and semantic context. This …
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Named entity recognition for Icelandic: comparing and combining different machine learning methods
Named Entity Recognition (NER) is the task of identifying person names, places, organizations, and other Named Entities in text. This can also include some numerical entities like dates, amounts of money and percentages. NER is often an important step in other Natural Language Processing tasks, …
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Improving Automated Literature-based Discovery with Neural Networks: Neural biomedical Named Entity Recognition, Link Prediction and Discovery
… LBD in three ways: 1) improving biomedical Named Entity Recognition (NER) to extract entities from unstructured text by using multi-task learning across multiple biomedical datasets; 2) improving knowledge discovery from realistic, random- and time-sliced biomedical graphs using link prediction …
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Joint multilingual learning for coreference resolution
… syntactic parsing and joint learning of named entity recognition and coreference resolution. The syntactic parsing model outperforms current state-of-the-art models by discovering linguistic information shared across languages at the granular level of a sentence. The coreference resolution …
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Domain adaptation with minimal training
… to Wikipedia concepts for adaptation of a named entity recognition system. Since Wikipedia has a broad domain coverage, the linking system is robust across domain variations. Therefore, jointly performing entity recognition and linking improves the accuracy of entity recognition on the target …
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Semi-supervised learning for natural language
… we focus on two segmentation tasks, named-entity recognition and Chinese word segmentation. The goal of named-entity recognition is to detect and classify names of people, organizations, and locations in a sentence. The goal of Chinese word segmentation is to find the word boundaries in a …
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A schema conversion approach for constructing heterogeneous information networks from documents
… information networks. First, we utilize named entity recognition (NER) tools to explore networks over entities, topics, and words to demonstrate how a probabilistic model can convert the data schema of the NER tools. Second, we address a pat- tern mining method to construct a network with …
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Natural Language Processing methods for short informal text
… for many NLP methods like topic modelling, named entity recognition, and sentiment analysis. We produced novel methods in NLP that target the short text informality. Our first novel model is in topic modelling for short messy text. The proposed model was inspired by the relation between the word's …
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Uncertainty Estimation on Natural Language Processing
… us to undertake tasks like text classification, entity recognition, and even crafting responses within a dialogue context. However, despite the expansive utility of NLP, it frequently necessitates a critical decision: whether to place trust in a model's predictions. To illustrate, consider a …
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Scalable information extraction with large language models
… annotated full-text papers with more than 24,000 entity mentions and 12,000 relations, providing a more realistic testbed than prior resources limited to abstracts or selected paragraphs. Second, it proposes DynClean, a training dynamics-based label cleaning framework for distantly supervised …
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