Word Shot Extraction (2025)

1. Few-shot information extraction from PDF documents - Modulai

  • 1 apr 2023 · 1. Extract text from the PDFs · 2. Define the schema that we want to hold the structured information · 3. Label some examples · 4 . Create a prompt ...

  • LLM's for structured text extraction using LLM's.

2. Keyword Extraction for Social Media Short Text - IEEE Xplore

  • Our approach uses the Word2vec to capture the semantic features between words in selected text, and meanwhile naturally fuses the word frequency, semantic ...

  • With the booming development of social media in recent years, researchers have begun to pay more attention to extracting personal profiles from information. Keyword extraction plays an important role in extracting personal profiles. However, most of the previous studies are only valid for ordinary text, but not ideal for social media short text. In this paper, we propose an improved method for keyword extraction based on Word2vec and Textrank to solve the unique problem of social media short text. Our approach uses the Word2vec to capture the semantic features between words in selected text, and meanwhile naturally fuses the word frequency, semantic relation and directional relation into Textrank to extract keywords. We conduct the experiments on the three datasets. The experimental results show the superior performance of our method in keyword extraction.

3. [2209.14008] Keyword Extraction from Short Texts with~a~Text-To ... - arXiv

  • 28 sep 2022 · The paper explores the relevance of the Text-To-Text Transfer Transformer language model (T5) for Polish (plT5) to the task of intrinsic and extrinsic keyword ...

  • The paper explores the relevance of the Text-To-Text Transfer Transformer language model (T5) for Polish (plT5) to the task of intrinsic and extrinsic keyword extraction from short text passages. The evaluation is carried out on the new Polish Open Science Metadata Corpus (POSMAC), which is released with this paper: a collection of 216,214 abstracts of scientific publications compiled in the CURLICAT project. We compare the results obtained by four different methods, i.e. plT5kw, extremeText, TermoPL, KeyBERT and conclude that the plT5kw model yields particularly promising results for both frequent and sparsely represented keywords. Furthermore, a plT5kw keyword generation model trained on the POSMAC also seems to produce highly useful results in cross-domain text labelling scenarios. We discuss the performance of the model on news stories and phone-based dialog transcripts which represent text genres and domains extrinsic to the dataset of scientific abstracts. Finally, we also attempt to characterize the challenges of evaluating a text-to-text model on both intrinsic and extrinsic keyword extraction.

4. Zero-Shot Relation Extraction from Word Embeddings - eScholarship

  • We propose an automated approach to mine word embeddings for sets of entities of the same type, as well as relationships that hold between them. Our approach ...

  • Author(s): Goldstein, Orpaz | Advisor(s): Van den Broeck, Guy | Abstract: Word embeddings learned from text are well-known to capture relational information. However, extracting such relations and their associated vectors is typically performed manually, to illustrate what knowledge is embedded in the space. We propose an automated approach to mine word embeddings for sets of entities of the same type, as well as relationships that hold between them. Our approach starts from a single seed entity and extracts a relational representation from the surrounding vector space. It does so without any relational supervision. Experiments show that our extraction algorithm outperforms spectral clustering and indeed is able to extract high-quality relations from noisy embeddings.

5. [PDF] Key word extraction for short text via word2vec, doc2vec, and textrank

  • 19 jan 2019 · In this paper, we attempt to use Word2Vec and Doc2Vec to improve short-text key word extraction. We first added the method of the collaborative.

6. [PDF] Key-frame Extraction and Shot Retrieval Using Nearest Feature ...

7. [PDF] Few-Shot Relation Extraction with Hybrid Visual Evidence

  • 20 mei 2024 · The goal of few-shot relation extraction is to predict relations between name entities in a sentence when only a few labeled instances are ...

8. Zero-Shot Information Extraction as a Unified Text-to-Triple Translation

  • We cast a suite of information extraction tasks into a text-to-triple translation framework. Instead of solving each task relying on task-specific datasets and ...

  • Chenguang Wang, Xiao Liu, Zui Chen, Haoyun Hong, Jie Tang, Dawn Song. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 2021.

9. Few-Shot Relation Extraction with Hybrid Visual Evidence - arXiv

  • 1 mrt 2024 · Existing few-shot relation extraction methods focus on uni-modal information such as text only. This reduces performance when there are no ...

  • The goal of few-shot relation extraction is to predict relations between name entities in a sentence when only a few labeled instances are available for training. Existing few-shot relation extraction methods focus on uni-modal information such as text only. This reduces performance when there are no clear contexts between the name entities described in text. We propose a multi-modal few-shot relation extraction model (MFS-HVE) that leverages both textual and visual semantic information to learn a multi-modal representation jointly. The MFS-HVE includes semantic feature extractors and multi-modal fusion components. The MFS-HVE semantic feature extractors are developed to extract both textual and visual features. The visual features include global image features and local object features within the image. The MFS-HVE multi-modal fusion unit integrates information from various modalities using image-guided attention, object-guided attention, and hybrid feature attention to fully capture the semantic interaction between visual regions of images and relevant texts. Extensive experiments conducted on two public datasets demonstrate that semantic visual information significantly improves the performance of few-shot relation prediction.

10. Automatic Video Scene Extraction by Shot Grouping - Microsoft Research

  • 1 sep 2000 · For more efficient organizing, browsing, and retrieving digital video content, it is important to extract video structure information at ...

  • For more efficient organizing, browsing, and retrieving digital video content, it is important to extract video structure information at both scene and shot levels. This paper presents an effective approach to video scene segmentation based on a pseudo-object-based shot correlation analysis. A new measure of the semantic correlation of consecutive shots based on dominant color […]

11. Need help with definitions: over-extraction and under-extraction

  • 18 mei 2020 · keno wrote: To see what it tastes like pull a super short shot for under-extraction. For over-extraction pull a really long shot but only ...

  • Can someone help me define over-extraction and under-extraction? What does the espresso taste like in each case. I see these terms a lot in forums and youtube videos but I feel like people use them to describe different things and it gets confusing. thanks

12. EATEN: Entity-Aware Attention for Single Shot Visual Text Extraction

  • This paper proposes an Entity-aware Attention Text Extraction Network called EATEN, which is an end-to-end trainable system to extract the ToIs without any post ...

  • Extracting Text of Interest (ToI) from images is a crucial part of many OCR applications, such as entity recognition of cards, invoices, and receipts. Most of the existing works employ complicated engineering pipeline, which contains OCR and structure information extraction, to fulfill this task. This paper proposes an Entity-aware Attention Text Extraction Network called EATEN, which is an end-to-end trainable system to extract the ToIs without any post-processing. In the proposed framework, each entity is parsed by its corresponding entity-aware decoder, respectively. Moreover, we innovatively introduce a state transition mechanism which further improves the robustness of visual ToI extraction. In consideration of the absence of public benchmarks, we construct a dataset of almost 0.6 million images in three real-world scenarios (train ticket, passport and business card), which is publicly available at https://github.com/beacandler/EATEN. To the best of our knowledge, EATEN is the first single shot method to extract entities from images. Extensive experiments on these benchmarks demonstrate the state-of-the-art performance of EATEN.

13. Automatic Keyword Extraction from Persian short Text Using word2vec

  • With the growing number of Persian electronic documents and texts, the use of quick and inexpensive methods to access desired texts from the extensive ...

  • With the growing number of Persian electronic documents and texts, the use of quick and inexpensive   methods to access desired texts from the extensive collection of these documents becomes more important. One of the effective techniques to achieve this goal is the extraction of the keywords which represent the main concept of the text. For this purpose, the frequency of a word in the text can not be a proper indication of its significance and its crucial role. Also, most of the keyword extraction methods ignore the concept and semantic of the text. On the other hand, the unstructured nature of new texts in news and electronic        documents makes it difficult to extract these words. In this paper, an automated, unsupervised method for keywords extraction in the Persian language that does not have a proper structure is proposed. This method not only takes into account the probability of occurrence of a word and its frequency in the text, but it also understands the concept and semantic of the text by learning word2vec model on the text. In the proposed method, which is a combination of statistical and machine learning methods, after learning word2vec on the text, the words that have the smallest distance with other words are extracted. Then, a statistical equation is proposed to calculate the score of each extracted word using co-occurence and frequency. Finally, words which have the highest scores are selected as the keywords. The evaluations indicate that the efficiency...

14. What is Extraction? - Clive Coffee

  • What is Extraction? If you listen to coffee professionals talk about the brewing process you'll often hear the word extraction crop up. Sometimes it sounds ...

  • If you listen to coffee professionals talk about the brewing process you’ll often hear the word extraction crop up. Sometimes it sounds technical, sometimes subjective. Sometimes it refers to a number but sometimes it refers to the way a shot looks while it’s pulling. These seemingly mixed messages can make it hard to suss out what it’s really supposed to mean. Extraction as a word simply refers to the act of taking something out. In the case of coffee, the “extracting” that we’re referring to is the act of pulling soluble compounds out of coffee beans by using water. Pretty simple. Where it gets tricky is that people use the word extraction to refer to both the way in which a thing was extracted as well as to describe the thing that was extracted. Today, we’ll just be talking about the latter, using the word to describe the stuff that we extract from coffee. For instance, if you’re pulling a shot the extraction is all of the stuff that was in the grounds in our portafilter basket that’s now in the cup. The amount of dissolved solids in that cup in proportion to the amount of water represents the strength of our coffee. If there were more dissolved solids in that same volume of water it would be a stronger cup of coffee. We can measure this by using a device called a refractometer which shines a laser through a sample of our coffee to determine what percentage of the sample is dissolved solids. Now, knowing the strength of our coffee is somewhat handy. Far more useful is cal...

15. Self-Verification Improves Few-Shot Clinical Information Extraction

  • 30 mei 2023 · Extracting patient information from unstructured text is a critical task in health decision-support and clinical research.

  • Extracting patient information from unstructured text is a critical task in health decision-support and clinical research. Large language models (LLMs) have shown the potential to accelerate clinical curation via few-shot in-context learning, in contrast to supervised learning which requires much more costly human annotations. However, despite drastic advances in modern LLMs such as GPT-4, they […]

16. Coffee Extraction Photography: A Novel Way to Photograph Espresso

  • 2 mei 2020 · I have long been interested in the bottom of the espresso shot. Ever since I got a bottomless portafilter, I had a thirst for more data.

  • Seeing the bottom of an espresso shot has been obscured by the shot itself, until now.

17. One-shot Text Field labeling using Attention and Belief Propagation for ...

  • 12 okt 2020 · Structured information extraction from document images usually consists of three steps: text detection, text recognition, and text field ...

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18. Really long extraction time? - Tips and Techniques - Home-Barista.com

  • 29 mrt 2011 · Obviously taste and preference is subjective, but I am curious to know how many people have pulled very long shots (whether by accident or ...

  • Obviously taste and preference is subjective, but I am curious to know how many people have pulled very long shots (whether by accident or on purpose) and found the results to be enjoyable. Every once in a while I do by mistake and I am always surprised how much I enjoy it. To me, very long is...

19. Coffee Extraction and How to Taste It - Over/Under ... - Barista Hustle

  • 30 jan 2017 · Cast your mind to a shot of espresso that was far too short; a ... word as 'acidity'. As you can imagine, this makes multilingual ...

  • Extraction is everything that the water takes from the coffee. This post will cover some basic extraction theory and the tastes associated with over, under and ideal coffee extractions.

20. Image to Text (Extract Text From Image)

  • Image to text converter is a free online image OCR tool that allows you to extract text from image at one click. It converts picture to text accurately.

21. [PDF] Incorporating Type Information into Zero-Shot Relation Extraction

  • ℎ and tail entity e, identify the correct relation r as expressed in the text. Zero-shot relation extraction separates the set of relations encountered during ...

Word Shot Extraction (2025)

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