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A formal corpus of mathematical theorems: a bridge from natural language to first-order logic 24

A formal corpus of mathematical theorems: a bridge from natu...

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13th international conference on Computing and Pattern Recognition, ICCPR 2024

作者： Li, Lulu Li, Zhengtao Hu, Peng Zhang, Hao School of Electronic and Communication Engineering Tianjin Normal University Tianjin China

ISBN: (纸本)9798400717482

Currently, there is a significant gap in the conversion of mathematical theorems from natural language to logical expressions, specifically in the form of first-order predicate logic. To address this issue, this paper presents a corpus of mathematical theorems expressed in first-order predicate logic and explores its necessity and practical applications. The primary objective of this work is to introduce a new paradigm for the logical transformation of mathematical theorems by detailing specific the construction process. The paper aims to demonstrate the potential functions and roles of this corpus, providing solid data support for automated proofs, formal verification, and other related fields. This corpus facilitates the automated processing and understanding of mathematical theorems. The contribution of this paper lies in proposing an innovative solution to bridge the gap between natural language processing and formal logic research. This study aims to enhance the performance and accuracy of algorithms and tools for processing mathematical theorems by constructing a corpus of theorems in first-order predicate logic. © 2024 Copyright held by the owner/author(s).

关键词： Mathematical transformations

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EpiK-Eval: Evaluation for language Models as Epistemic Models

EpiK-Eval: Evaluation for Language Models as Epistemic Model...

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conference on Empirical Methods in natural language processing (EMNLP)

作者： Prato, Gabriele Huang, Jerry Parthasarathi, Prasannna Sodhani, Shagun Chandar, Sarath Mila Montreal PQ Canada Meta AI Toronto ON Canada Univ Montreal Montreal PQ Canada Polytech Montreal Montreal PQ Canada CIFAR AI Chair Montreal PQ Canada

ISBN: (纸本)9798891760608

In the age of artificial intelligence, the role of large language models (LLMs) is becoming increasingly central. Despite their growing prevalence, their capacity to consolidate knowledge from different training documents-a crucial ability in numerous applications-remains unexplored. This paper presents the first study examining the capability of LLMs to effectively combine such information within their parameter space. We introduce EpiK-Eval, a novel question-answering benchmark tailored to evaluate LLMs' proficiency in formulating a coherent and consistent knowledge representation from segmented narratives. Evaluations across various LLMs reveal significant weaknesses in this domain. We contend that these shortcomings stem from the intrinsic nature of prevailing training objectives. Consequently, we advocate for refining the approach towards knowledge consolidation, as it harbors the potential to dramatically improve their overall effectiveness and performance. The findings from this study offer insights for developing more robust and reliable LLMs. Our code and benchmark are available at https://***/chandar-lab/EpiK-Eval

关键词： knowledge representation

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Medical Entity Disambiguation with Medical Mention Relation and Fine-grained Entity knowledge 30

Medical Entity Disambiguation with Medical Mention Relation ...

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Joint 30th international conference on Computational Linguistics and 14th international conference on language Resources and Evaluation, LREC-COLING 2024

作者： Lu, Wenpeng Zhang, Guobiao Peng, Xueping Guan, Hongjiao Wang, Shoujin Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan China Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia Data Science Institute University of Technology Sydney Sydney Australia Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9782493814104

Medical entity disambiguation (MED) plays a crucial role in natural language processing and biomedical domains, which is the task of mapping ambiguous medical mentions to structured candidate medical entities from knowledge bases (KBs). However, existing methods for MED often fail to fully utilize the knowledge within medical KBs and overlook essential interactions between medical mentions and candidate entities, resulting in knowledge- and interaction-inefficient modeling and suboptimal disambiguation performance. To address these limitations, this paper proposes a novel approach, MED with Medical Mention Relation and Fine-grained Entity knowledge (MMR-FEK). Specifically, MMR-FEK incorporates a mention relation fusion module and an entity knowledge fusion module, followed by an interaction module. The former employs a relation graph convolutional network to fuse mention relation information between medical mentions to enhance mention representations, while the latter leverages an attention mechanism to fuse synonym and type information of candidate entities to enhance entity representations. Afterwards, an interaction module is designed to employ a bidirectional attention mechanism to capture interactions between medical mentions and entities to generate the matching representation. Extensive experiments on two publicly available real-world datasets demonstrate MMR-FEK's superiority over state-of-the-art(SOTA) MED baselines across all metrics. Our source code is publicly available. © 2024 ELRA language Resource Association: CC BY-NC 4.0.

关键词： natural language processing systems

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international conference Recent Advances in natural language processing, RANLP 2023: Large language Models for natural language processing - Proceedings

International Conference Recent Advances in Natural Language...

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2023 international conference Recent Advances in natural language processing: Large language Models for natural language processing, RANLP 2023

ISBN: (纸本)9789544520922

The proceedings contain 135 papers. The topics discussed include: Bipol: multi-axes evaluation of bias with explainability in benchmark datasets;automatically generating Hindi Wikipedia pages using Wikidata as a knowledge graph: a domain-specific template sentences approach;cross-lingual classification of crisis-related tweets using machine translation;lexicon-driven automatic sentence generation for the skills section in a job posting;multilingual racial hate speech detection using transfer learning;exploring Amharic hate speech data collection and classification approaches;are you not moved? incorporating sensorimotor knowledge to improve metaphor detection;HAQA and QUQA: constructing two Arabic question-answering Corpora for the Quran and Hadith;a review in knowledge extraction from knowledge bases;evaluating of large language models in relationship extraction from unstructured data: empirical study from holocaust testimonies;and impact of emojis on automatic analysis of individual emotion categories.

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Hybrid Model for Predicting Used Car Prices: Integrating natural language processing with Random Forest Regressor 2

Hybrid Model for Predicting Used Car Prices: Integrating Nat...

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2nd IEEE international conference on Advances in Information Technology, ICAIT 2024

作者： Tyagi, Shiva Sirohi, Shagun Singh, Yash Siddhant Vishwakarma, Akash Department of Computer Science and Engineering Ajay Kumar Garg Engineering College Ghaziabad India

ISBN: (纸本)9798350383867

The automotive sector has a notable upswing in the sales of pre-owned vehicles, hence necessitating the development of precise pricing models to enable knowledgeable decision-making for both purchasers and vendors. In order to predict used automobile prices, we provide a novel hybrid methodology in this work that combines natural language processing (NLP) methods with the Random Forest regressor. Our method leverages structured and unstructured data sources to extract valuable contextual information that is hidden in textual listings for automobiles. Preprocessing a heterogeneous dataset with unstructured textual descriptions and structured attributes (make, model, year, mileage) is part of the methodology. We leverage natural language processing (NLP) approaches to extract informative features from textual representations, improving the model's comprehension of subtleties in the conditions and attributes linked to individual car listings. ©2024 IEEE.

关键词： Random forests

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A Guide to Cost-Effective Collection of Colloquial Algerian Arabic Speech Data 8

A Guide to Cost-Effective Collection of Colloquial Algerian ...

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8th IEEE international conference on Image and Signal processing and their Applications, ISPA 2024

作者： Moussa, Touahmi Abdelmalik, Ouamane Ammar, Chouchane Mohamed Khider University LI3C Biskra Algeria YAHIA Fares University LI3C Medea Algeria

ISBN: (纸本)9798350309249

Modern Standard Arabic (MSA) serves as the official language across all Arab countries, employed in administrative, educational, official broadcast, and press settings. However, in everyday informal communication, each Arab country adopts its own variant dialect, commonly known as colloquial dialect. This colloquial form has emerged as the dominant language on social media platforms due to the widespread usage of such platforms over the past decade. Consequently, there is a growing demand for language resources and natural language processing (NLP) systems that cater to these dialects. This paper details the development of an Algerian Arabic dialect dataset, with a focus on average colloquial dialect to ensure effective performance on standard Algerian dialect. Carefully curated, the dataset encompasses a diverse collection of high-quality audio extracted from social media platforms. Employing a finetuning approach, we leverage existing Automatic Speech Recognition (ASR) systems, particularly the Whisper model by OpenAI for its noise robustness and adaptability to varying audio quality. The paper delves into the process of efficiently collecting speech data, while also outlining potential applications and future directions. Researchers in speech analysis and natural language processing will find this work invaluable for advancing their knowledge of Algerian Arabic and fostering accurate ASR system development tailored to the dialect. © 2024 IEEE.

关键词： natural language processing systems

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3rd internationalWorkshop on natural language processing for knowledge Graph Creation (NLP4KGC 2024) 3

3rd InternationalWorkshop on Natural Language Processing for...

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3rd international Workshop on natural language processing for knowledge Graph Creation, NLP4KGC 2024

作者： Vakaj, Edlira Stamatia, Rizou Mihindukulasooriya, Nandana Tiwari, Sanju Ortiz-Rodríguez, Fernando McGranaghan, Ryan Birmingham City University Birmingham United Kingdom Singular Logic Athens Greece IBM Research New York United States Universidad Autónoma de Tamaulipas Tamaulipas Mexico NASA Jet Propulsion Laboratory CA United States

knowledge Graphs (KG) are emerging and becoming increasingly popular. Building a domain knowledge graph from a large amount of text is a challenging task which requires a tremendous amount of work, including entity recognition, entity disambiguation, and relationship extraction. In this context, the 3rd NLP4KGC workshop brings together academics, industry experts, and enthusiasts from the natural language processing (NLP) and knowledge graph (KG) creation fields in a common forum. The objective is to foster collaboration and knowledge sharing among researchers, developers, and practitioners working at the cutting edge of NLP and KG creation. After two successful previous editions, the 3rd NLP4KGc is co-located with the 20th international conference on Semantic Systems (SEMANTiCS 2024). After a rigorous review process, 10 papers were accepted for oral presentations at the event. Workshop Website: https://***/view/3rdnlp4kgc/ Previous Proceedings: https://***/Vol-3510/, https://***/10.1145/3543873.3589746. © 2024 Copyright for this paper by its authors.

关键词： knowledge graph

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Integrating Model-Agnostic Meta-Learning with Advanced language Embeddings for Few-Shot Intent Classification 32

Integrating Model-Agnostic Meta-Learning with Advanced Langu...

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32nd international conference on Electrical engineering (ICEE)

作者： Rahimi, Ali Veisi, Hadi Univ Tehran Coll Interdisciplinary Sci & Technol Tehran Iran

ISBN: (纸本)9798350376357;9798350376340

Addressing the challenge of few-shot learning in intent classification tasks within natural language processing (NLP), this study introduces a novel approach that harnesses the robust adaptation capabilities of Model-Agnostic Meta-Learning (MAML) combined with sophisticated language embeddings, namely BERT, LaBSE, and text-embedding-ada-002. The need for models to understand and classify intents with minimal training data is imperative to progress in creating versatile, responsive AI systems. We propose a methodology that leverages the generalizability of MAML and the deeply contextualized representations offered by state-of-the-art embeddings, allowing for significant improvements in Accuracy and data efficiency. We evaluate our approach using the CLINC150 dataset across a series of N-way & K-shot configurations, demonstrating the efficacy of the proposed model with varying numbers of intent classes and examples. Our findings reveal that the text-embedding-ada-002 embeddings consistently provide superior performance in both 1-shot and 5-shot settings across all class configurations tested, indicating their potent synergy with metalearning strategies. Specifically, text-embedding-ada-002 achieved an accuracy of 97.07% in the 5-Way & 1-Shot setting and 99.1% in the 5-Way & 5-Shot setting. The outcomes of our experimental evaluation suggest that our approach also illuminates the potential of harmonious integration of cutting-edge language embeddings with meta-learning frameworks. This work provides a solid foundation for further exploration in optimizing fewshot intent classification, paving the way for creating AI systems proficient in understanding user intents with minimal exemplars. This research lays the groundwork for future advancements in few-shot intent classification, enabling the development of AI systems that require minimal training data to interpret user intent accurately.

关键词： Few-shot learning Model-Agnostic MetaLearning (MAML) Intent classification natural language processing (NLP) BERT LaBSE and Ada

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knowledge Graphs for Textbooks: Extraction and Completion Techniques 6

Knowledge Graphs for Textbooks: Extraction and Completion Te...

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6th IEEE international conference on Artificial Intelligence and knowledge engineering, AIKE 2023

作者： Yao, Yutong Potikas, Petros Potika, Katerina Department of Computer Science San José State University San JoseCA United States School of Electrical and Computer Engineering National Technical University of Athens Greece

ISBN: (纸本)9798350331288

This paper aims to apply knowledge graph construction techniques to textbooks, explicitly focusing on the challenge of the absence of domain-specific schema for each textbook. Various entity and relation extraction models are utilized to capture logical and semantic information related to the textbook s topic. These models include a Text-Encoding-Initiative (TEI) model to extract hierarchical concepts, spaCy natural language processing (NLP), and Google Cloud natural language to extract semantic information from the main textual content. The study includes a case study on a cloud computing textbook, where each approach is evaluated and analyzed. Ultimately, the goal is to create knowledge graphs of textbooks, enabling the completion task of predicting missing entities or relations in a low-dimensional space. © 2023 IEEE.

关键词： knowledge graph

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Named Entities Based on the BERT-BILSTM-ACRF Model Recognition Research 7

Named Entities Based on the BERT-BILSTM-ACRF Model Recogniti...

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7th international conference on natural language processing and Information Retrieval (NLPIR)

作者： Wang, Jingdong Guo, Yongjia Northeast Elect Power Univ Jilin Jilin Peoples R China

ISBN: (纸本)9798400709227

As a crucial first step in the process of constructing knowledge graph, the accuracy of named entity recognition determines the construction effect of the final graph. However, at present, Chinese named entity recognition methods still have many problems, such as long training time and lower accuracy. Hence, we come up with a BERT-BILSTM-ACRF entity recognition method that combines the "self-attention" mechanism. To begin with, Bert model is selected as the embedding layer, the text is vectorized, and the character position in-formation is obtained through the bidirectional Long Short-Term Memory network. Secondly, the internal relationship of the character sequence is further searched through the self-attention mechanism, and finally the final optimal sequence is decoded by the conditional random field model. To check the effectiveness of the BERT-BILSTM-ACRF model, the model is applied to the data set of the university course textbook" Da-ta Structure", and the result reaches 98.97%F1 value and 98.14%accuracy, which has good experimental results and certain practical value.

关键词： knowledge graph Self-attention mechanism natural language processing Named entity recognition

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