Semantic maps and metrics for science Semantic maps and metrics for science using deep transformer encoders

• URL: http://arxiv.org/abs/2104.05928v1
• Date: Tue, 13 Apr 2021 04:12:20 GMT
• Title: Semantic maps and metrics for science Semantic maps and metrics for science using deep transformer encoders
• Authors: Brendan Chambers and James Evans
• Abstract summary: Recent advances in natural language understanding driven by deep transformer networks offer new possibilities for mapping science. Transformer embedding models capture shades of association and connotation that vary across different linguistic contexts. We report a procedure for encoding scientific documents with these tools, measuring their improvement over static word embeddings.
• Score: 1.599072005190786
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The growing deluge of scientific publications demands text analysis tools that can help scientists and policy-makers navigate, forecast and beneficially guide scientific research. Recent advances in natural language understanding driven by deep transformer networks offer new possibilities for mapping science. Because the same surface text can take on multiple and sometimes contradictory specialized senses across distinct research communities, sensitivity to context is critical for infometric applications. Transformer embedding models such as BERT capture shades of association and connotation that vary across the different linguistic contexts of any particular word or span of text. Here we report a procedure for encoding scientific documents with these tools, measuring their improvement over static word embeddings in a nearest-neighbor retrieval task. We find discriminability of contextual representations is strongly influenced by choice of pooling strategy for summarizing the high-dimensional network activations. Importantly, we note that fundamentals such as domain-matched training data are more important than state-of-the-art NLP tools. Yet state-of-the-art models did offer significant gains. The best approach we investigated combined domain-matched pretraining, sound pooling, and state-of-the-art deep transformer network encoders. Finally, with the goal of leveraging contextual representations from deep encoders, we present a range of measurements for understanding and forecasting research communities in science.

Related papers

• SciMMIR: Benchmarking Scientific Multi-modal Information Retrieval [64.03631654052445]
  Current benchmarks for evaluating MMIR performance in image-text pairing within the scientific domain show a notable gap. We develop a specialised scientific MMIR benchmark by leveraging open-access paper collections. This benchmark comprises 530K meticulously curated image-text pairs, extracted from figures and tables with detailed captions in scientific documents.
  arXiv  Detail & Related papers  (2024-01-24T14:23:12Z)
• A Comprehensive Survey on Applications of Transformers for Deep Learning Tasks [60.38369406877899]
  Transformer is a deep neural network that employs a self-attention mechanism to comprehend the contextual relationships within sequential data. transformer models excel in handling long dependencies between input sequence elements and enable parallel processing. Our survey encompasses the identification of the top five application domains for transformer-based models.
  arXiv  Detail & Related papers  (2023-06-11T23:13:51Z)
• Semantic Similarity Measure of Natural Language Text through Machine Learning and a Keyword-Aware Cross-Encoder-Ranking Summarizer -- A Case Study Using UCGIS GIS&T Body of Knowledge [2.4909170697740968]
  GIS&T Body of Knowledge (BoK) is a community-driven endeavor to define, develop, and document geospatial topics. This research evaluates the effectiveness of multiple natural language processing (NLP) techniques in extracting semantics from text. It also offers a new perspective on the use of machine learning techniques for analyzing scientific publications.
  arXiv  Detail & Related papers  (2023-05-17T01:17:57Z)
• How Do Transformers Learn Topic Structure: Towards a Mechanistic Understanding [56.222097640468306]
  We provide mechanistic understanding of how transformers learn "semantic structure" We show, through a combination of mathematical analysis and experiments on Wikipedia data, that the embedding layer and the self-attention layer encode the topical structure.
  arXiv  Detail & Related papers  (2023-03-07T21:42:17Z)
• A Survey of Text Representation Methods and Their Genealogy [0.0]
  In recent years, with the advent of highly scalable artificial-neural-network-based text representation methods the field of natural language processing has seen unprecedented growth and sophistication. We provide a survey of current approaches, by arranging them in a genealogy, and by conceptualizing a taxonomy of text representation methods to examine and explain the state-of-the-art.
  arXiv  Detail & Related papers  (2022-11-26T15:22:01Z)
• TeKo: Text-Rich Graph Neural Networks with External Knowledge [75.91477450060808]
  We propose a novel text-rich graph neural network with external knowledge (TeKo) We first present a flexible heterogeneous semantic network that incorporates high-quality entities. We then introduce two types of external knowledge, that is, structured triplets and unstructured entity description.
  arXiv  Detail & Related papers  (2022-06-15T02:33:10Z)
• An Informational Space Based Semantic Analysis for Scientific Texts [62.997667081978825]
  This paper introduces computational methods for semantic analysis and the quantifying the meaning of short scientific texts. The representation of scientific-specific meaning is standardised by replacing the situation representations, rather than psychological properties. The research in this paper conducts the base for the geometric representation of the meaning of texts.
  arXiv  Detail & Related papers  (2022-05-31T11:19:32Z)
• Contextualized Semantic Distance between Highly Overlapped Texts [85.1541170468617]
  Overlapping frequently occurs in paired texts in natural language processing tasks like text editing and semantic similarity evaluation. This paper aims to address the issue with a mask-and-predict strategy. We take the words in the longest common sequence as neighboring words and use masked language modeling (MLM) to predict the distributions on their positions. Experiments on Semantic Textual Similarity show NDD to be more sensitive to various semantic differences, especially on highly overlapped paired texts.
  arXiv  Detail & Related papers  (2021-10-04T03:59:15Z)
• EDS-MEMBED: Multi-sense embeddings based on enhanced distributional semantic structures via a graph walk over word senses [0.0]
  We leverage the rich semantic structures in WordNet to enhance the quality of multi-sense embeddings. We derive new distributional semantic similarity measures for M-SE from prior ones. We report evaluation results on 11 benchmark datasets involving WSD and Word Similarity tasks.
  arXiv  Detail & Related papers  (2021-02-27T14:36:55Z)
• TopicBERT: A Transformer transfer learning based memory-graph approach for multimodal streaming social media topic detection [8.338441212378587]
  Social networks with bursty short messages and their respective large data scale spread among vast variety of topics are research interest of many researchers. These properties of social networks which are known as 5'Vs of big data has led to many unique and enlightenment algorithms and techniques applied to large social networking datasets and data streams.
  arXiv  Detail & Related papers  (2020-08-16T10:39:50Z)
• An Evaluation of Recent Neural Sequence Tagging Models in Turkish Named Entity Recognition [5.161531917413708]
  We propose a transformer-based network with a conditional random field layer that leads to the state-of-the-art result. Our study contributes to the literature that quantifies the impact of transfer learning on processing morphologically rich languages.
  arXiv  Detail & Related papers  (2020-05-14T06:54:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.