Introduction of a novel word embedding approach based on technology labels extracted from patent data

• URL: http://arxiv.org/abs/2102.00425v1
• Date: Sun, 31 Jan 2021 10:37:38 GMT
• Title: Introduction of a novel word embedding approach based on technology labels extracted from patent data
• Authors: Mark Standke, Abdullah Kiwan, Annalena Lange, Dr. Silvan Berg
• Abstract summary: This paper introduces a word embedding approach using statistical analysis of human labeled data to produce accurate and language independent word vectors for technical terms. The resulting algorithm is a development of the former EQMania UG (eqmania.com) and can be tested under eqalice.com until April 2021.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Diversity in patent language is growing and makes finding synonyms for conducting patent searches more and more challenging. In addition to that, most approaches for dealing with diverse patent language are based on manual search and human intuition. In this paper, a word embedding approach using statistical analysis of human labeled data to produce accurate and language independent word vectors for technical terms is introduced. This paper focuses on the explanation of the idea behind the statistical analysis and shows first qualitative results. The resulting algorithm is a development of the former EQMania UG (eqmania.com) and can be tested under eqalice.com until April 2021.

Related papers

• Connecting the Dots: Inferring Patent Phrase Similarity with Retrieved Phrase Graphs [18.86788223751979]
  We study the patent phrase similarity inference task, which measures the semantic similarity between two patent phrases. We introduce a graph-augmented approach to amplify the global contextual information of the patent phrases.
  arXiv  Detail & Related papers  (2024-03-24T18:59:38Z)
• Unveiling Black-boxes: Explainable Deep Learning Models for Patent Classification [48.5140223214582]
  State-of-the-art methods for multi-label patent classification rely on deep opaque neural networks (DNNs) We propose a novel deep explainable patent classification framework by introducing layer-wise relevance propagation (LRP) Considering the relevance score, we then generate explanations by visualizing relevant words for the predicted patent class.
  arXiv  Detail & Related papers  (2023-10-31T14:11:37Z)
• Biomedical Named Entity Recognition via Dictionary-based Synonym Generalization [51.89486520806639]
  We propose a novel Synonym Generalization (SynGen) framework that recognizes the biomedical concepts contained in the input text using span-based predictions. We extensively evaluate our approach on a wide range of benchmarks and the results verify that SynGen outperforms previous dictionary-based models by notable margins.
  arXiv  Detail & Related papers  (2023-05-22T14:36:32Z)
• Large-Scale Text Analysis Using Generative Language Models: A Case Study in Discovering Public Value Expressions in AI Patents [2.246222223318928]
  This paper employs a novel approach using a generative language model (GPT-4) to produce labels and rationales for large-scale text analysis. We collect a database comprising 154,934 patent documents using an advanced Boolean query submitted to InnovationQ+. We design a framework for identifying and labeling public value expressions in these AI patent sentences.
  arXiv  Detail & Related papers  (2023-05-17T17:18:26Z)
• New Product Development (NPD) through Social Media-based Analysis by Comparing Word2Vec and BERT Word Embeddings [0.0]
  Two popular word embedding techniques, Word2Vec and BERT, were evaluated to identify the best-performing approach in sentiment analysis and opinion detection. BERT word embeddings combined with Balanced Random Forest yielded the most accurate single model for both sentiment analysis and opinion detection.
  arXiv  Detail & Related papers  (2023-04-17T15:32:11Z)
• Textual Entailment Recognition with Semantic Features from Empirical Text Representation [60.31047947815282]
  A text entails a hypothesis if and only if the true value of the hypothesis follows the text. In this paper, we propose a novel approach to identifying the textual entailment relationship between text and hypothesis. We employ an element-wise Manhattan distance vector-based feature that can identify the semantic entailment relationship between the text-hypothesis pair.
  arXiv  Detail & Related papers  (2022-10-18T10:03:51Z)
• Patent Sentiment Analysis to Highlight Patent Paragraphs [0.0]
  Given a patent document, identifying distinct semantic annotations is an interesting research aspect. In the process of manual patent analysis, to attain better readability, recognising the semantic information by marking paragraphs is in practice. This work assist patent practitioners in highlighting semantic information automatically and aid to create a sustainable and efficient patent analysis using the aptitude of Machine Learning.
  arXiv  Detail & Related papers  (2021-11-06T13:28:29Z)
• Named Entity Recognition for Social Media Texts with Semantic Augmentation [70.44281443975554]
  Existing approaches for named entity recognition suffer from data sparsity problems when conducted on short and informal texts. We propose a neural-based approach to NER for social media texts where both local (from running text) and augmented semantics are taken into account.
  arXiv  Detail & Related papers  (2020-10-29T10:06:46Z)
• Commonsense Evidence Generation and Injection in Reading Comprehension [57.31927095547153]
  We propose a Commonsense Evidence Generation and Injection framework in reading comprehension, named CEGI. The framework injects two kinds of auxiliary commonsense evidence into comprehensive reading to equip the machine with the ability of rational thinking. Experiments on the CosmosQA dataset demonstrate that the proposed CEGI model outperforms the current state-of-the-art approaches.
  arXiv  Detail & Related papers  (2020-05-11T16:31:08Z)
• Word Sense Disambiguation for 158 Languages using Word Embeddings Only [80.79437083582643]
  Disambiguation of word senses in context is easy for humans, but a major challenge for automatic approaches. We present a method that takes as input a standard pre-trained word embedding model and induces a fully-fledged word sense inventory. We use this method to induce a collection of sense inventories for 158 languages on the basis of the original pre-trained fastText word embeddings.
  arXiv  Detail & Related papers  (2020-03-14T14:50:04Z)

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.