; On Glushkov K-graphs (P Caron & M Flouret); NLP Dictionaries Implemented as FSAs (J Daciuk et al.); Tree-Language Based Querying (A Berlea); Quotient Monoids and Concurrent Behaviours (R Janicki et al.); Correction Queries in Active Learning (C Tirnauca); ...
In addition to language comprehension, LLMs have recently shown their utility in generative language applications, such as generative AI interfaces (chatbots) [6]. The availability of LLMs, such as OpenAI’s generative pretrained transformer (GPT) models, Google’s Bard, and Meta’s Llama, has...
Causal Language Modeling (CausalLM):Suitable for tasks requiring sequential data generation. Masked Language Modeling (MLM):Ideal for tasks needing bidirectional context understanding. Sequence-to-Sequence (Seq2Seq):Used for tasks like translation or summarization where input sequences are mapped to outp...
BERT (Bidirectional Encoder Representations from Transformers) has revolutionized Natural Language Processing (NLP) by significantly enhancing the capabili
- Natural Language in Conceptual Modeling: Analysis of Natural Language Descriptions, Terminological Ontologies, Consistency Checking, Metadata Creation and Harvesting, Ontology-driven Systems Integration, Ontology Management - NLP Applications Business Intelligence, Subjectivity and Sentiment Analysis, QA systems...
Let’s dive in together to understand the 8 most relevant NLP applications and explore how can they help you become more profitable. 1. Text classification Text classificationis the core task in natural language processing.The goal of text classification is to read the text and assign one label...
=== The Third Workshop on NLP Applications to Field Linguistics === Field linguistics plays a crucial role in the development of linguistic theory and universal language modeling, as it provides uncontested, the only way to obtain structural data about the rapidly diminishing diversity of natural ...
- 掩码语言建模(Masked Language Modeling, MLM):预测序列中被掩码覆盖的词。 - 前缀语言建模(Prefix Language Modeling):允许序列的一部分非因果地关注未来的词。 3. **模型架构(Architectures)**: - 讨论了基于Transformer的不同架构,包括编码器(Encoder)、解码器(Decoder)和两者结合的架构。
(MatBERT) Quantifying the Advantage of Domain-Specific Pre-training on Named Entity Recognition Tasks in Materials Science Patterns 2022 [Paper] [GitHub] (BatteryBERT) BatteryBERT: A Pretrained Language Model for Battery Database Enhancement Journal of Chemical Information and Modeling 2022 [Paper] [...
Natural Language Processing (NLP), Computational Linguistics (CL), Natural Language Engineering (NLE), Text Mining (TM), Information Retrieval (IR), and related areas. A huge amount of information is openly published every day, on many different topics and written in natural language, thus ...