Information Extraction

By M. Pazienza. 1997

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Citations in the corpus

Zero-Shot Information Extraction via Chatting with ChatGPT (2023, background): “It is a fundamental and crucial task in natural language processing (Sarawagi et al., 2008).”
Knowledge Graphs 2021: A Data Odyssey (2021, background): “Relation extraction (RE) from text sources has been advanced over thirty years (see surveys [30, 34]), achieving good results in benchmarks such as TACRED ( https://paperswithcode.com/dataset/tacred ), but with precision below 80 percent and far from being robust “in the wild”.”
Fake News: A Survey of Research, Detection Methods, and Opportunities (2018, background): “Broadly speaking, there are four (but not limited to) types of Web content: text, tabular data, structured pages and human annotations that contain relational information and can be utilized for knowledge extraction by different extractors [Dong et al. 2014; Grishman 2015].”
News events prediction using Markov logic networks (2018, background): “Our investigation did not attempt to extract named entity and relation [1] including causal relation (textual entailment) [2], specifically temporal information (TDT) [3] and future-related information [4] from text.”
Profiling relational data: a survey (2015, methodology): “Statistical methods are used for tasks, such as information extraction [125], part-of-speech tagging [20], and text categorization [83].”
A Survey of the Usages of Deep Learning for Natural Language Processing (2020, background): “The outputs of systems vary, but often, the extracted data and the relationships within it are saved in relational databases [172].”
An Automated Information Extraction Tool for International Conflict Data with Performance as Good as Human Coders: A Rare Events Evaluation Design (2003, background): “Information extraction (Cowie and Lehnert, 1996; Grishman, 1997) is a subfield of computational linguistics, the branch of computer science that studies machine processing”
Solving Geometry Problems: Combining Text and Diagram Interpretation (2015, background): “Relation extraction is another area of NLP that is related to our task (Cowie and Lehnert, 1996; Culotta and Sorensen, 2004).”
Highly Parallel Autoregressive Entity Linking with Discriminative Correction (2021, background): “, 2021), and information extraction (Sarawagi, 2008; MartinezRodriguez et al., 2020), to name a few.”
Comparative experiments on learning information extractors for proteins and their interactions (2005, methodology): “We measured precision (percentage of extracted names that are correct), recall (percentage of correct names that are found), and Fmeasure (harmonic mean of precision and recall) [11].”