Table extraction using conditional random fields

By David Pinto, Andrew McCallum, Xing Wei, W. Bruce Croft. Digital Government Research, 2003

458 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/6991606a1a9d5c285af385ee9159fd46cc14048e

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Citations in the corpus

Document Retrieval Model Through Semantic Linking (2017, methodology): “In [27, 28], CRFs are used as a method for combining a diverse set of features.”
An Introduction to Conditional Random Fields (2010, background): “Since then, linear-chain CRFs have been applied to many problems in natural language processing, including named-entity recognition [72], feature induction for NER [71], shallow parsing [108, 120], identifying protein names in biology abstracts [107], segmenting addresses in Web pages [26], informat”
Sentiment Analysis Based on Deep Learning: A Comparative Study (2020, methodology): “Nevertheless, corpus-based sentiment analysis does not rely on a predefined dictionary but on statistical analysis of the contents of a collection of documents, using techniques based on k-nearest neighbors (k-NN) [32], conditional random field (CRF) [33], and hidden Markov models (HMM) [34], among ”
Protein Fold Recognition Using Segmentation Conditional Random Fields (SCRFs) (2006, unknown): “, are very successful in many applications, such as information extraction, image processing, and so on (Pinto et al., 2003; Kumar and Hebert, 2003).”
A Survey of Web Information Extraction Systems (2006, background): “(such as government statistical reports) and segmenting them into records [44].”
Predicting Program Properties from "Big Code" (2015, methodology): “This query makes a joint prediction for all elements together by optimizing a scoring function based on the learned CRF model.”
Applying Conditional Random Fields to Japanese Morphological Analysis (2004, methodology): “, 2004), and Information Extraction (Pinto et al., 2003; Peng and McCallum, 2004).”
Interactive Information Extraction with Constrained Conditional Random Fields (2004, unknown): “CRFs have been shown to perform well on information extraction tasks (McCallum & Li 2003; Pinto et al. 2003; McCallum 2003; Sha & Pereira 2003), and are well-suited for interactive information extraction since the confi dence of the labels can be estimated and there is a natural scheme for…”
Semantic Role Labelling with Tree Conditional Random Fields (2005, methodology): “…applied with impressive empirical results to the tasks of named entity recognition (McCallum and Li, 2003; Cohn et al., 2005), part-of-speech (PoS) tagging (Lafferty et al., 2001), noun phrase chunking (Sha and Pereira, 2003) and extraction of table data (Pinto et al., 2003), among other tasks.”
Conditional Random Fields: An Introduction (2004, methodology): “CRFs outperform both MEMMs and HMMs on a number of real-world sequence labeling tasks [8, 11, 15].”
Discriminative Language Modeling with Conditional Random Fields and the Perceptron Algorithm (2004, methodology): “, 1999), and tagging or segmentation tasks (Lafferty et al., 2001; Sha and Pereira, 2003; McCallum and Li, 2003; Pinto et al., 2003).”
Semi-Supervised Learning for Natural Language (2005, background): “, 2004), shallow parsing (Sha and Pereira, 2003), table extraction (Pinto et al., 2003), language modeling (Roark et al.”
Web data extraction based on partial tree alignment (2005, background): “Partial alignment means that we align only those data fields in a pair of data records that can be aligned (or matched) with certainty, and make no commitment on the rest of the data fields.”
Accurate Information Extraction from Research Papers using Conditional Random Fields (2004, unknown): “From Table (1, 2), one can see that CRF performs signi(cid:2)cantly better than HMMs, which again supports the previous (cid:2)ndings (Lafferty et al., 2001; Pinto et al., 2003).”