Most current statistical natural language processing models use only local features so as to permit dynamic programming in inference, but this makes them unable to fully account for the long distance structure that is prevalent in language use. We show how to solve this dilemma with Gibbs sampling, a simple Monte Carlo method used to perform approximate inference in factored probabilistic models. By using simulated annealing in place of Viterbi decoding in sequence models such as HMMs, CMMs, and CRFs, it is possible to incorporate non-local structure while preserving tractable inference. We use this technique to augment an existing CRF-based information extraction system with long-distance dependency models, enforcing label consistency and extraction template consistency constraints. This technique results in an error reduction of up to 9% over state-of-the-art systems on two established information extraction tasks.

/pdf/incorporating-non-local-information-into-information-2upchk9bsy.pdf

Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling

1. Introduction to text mining 2. Core text mining operations 3. Text mining preprocessing techniques 4. Categorization 5. Clustering 6. Information extraction 7. Probabilistic models for Information extraction 8. Preprocessing applications using probabilistic and hybrid approaches 9. Presentation-layer considerations for browsing and query refinement 10. Visualization approaches 11. Link analysis 12. Text mining applications Appendix Bibliography.

/pdf/the-text-mining-handbook-advanced-approaches-in-analyzing-ge55wa29fm.pdf

The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data

https://cs.nyu.edu/parida/DIMACSworkshopJune20-2205/DIMACSworkshop/slides/AndreyRzhetsky1.pdf

GeneWays: a system for extracting, analyzing, visualizing, and integrating molecular pathway data

Our ultimate objective in data mining is to identify any hidden patterns or relationships between our data elements, and in one sense, machine learning provides us with a set of techniques to do just that: techniques that allow us to learn the patterns without any outside influence (unsupervised learning). However, just as is the case with anything, that power comes at a price, but the results can be very interesting and very significant. In other cases, we have some sense on what the results should be and so can guide the learning techniques through an initial “training” phase, directing our system and honing the results (supervised learning). These two broad classifications of machine-learning methods will ground us as we discuss a broad range of techniques and where they are currently being applied in life sciences research, expanding our toolkit and enabling us to take a very different path in our analysis efforts: using an artificial intelligence discipline and letting the data tell us what it contains. As datasets grow, these techniques become more important.

Machine-Learning Techniques

An intrinsic part of information extraction is the creation and manipulation of relations extracted from text. In this article, we develop a foundational framework where the central construct is what we call a document spanner (or just spanner for short). A spanner maps an input string into a relation over the spans (intervals specified by bounding indices) of the string. The focus of this article is on the representation of spanners. Conceptually, there are two kinds of such representations. Spanners defined in a primitive representation extract relations directly from the input string; those defined in an algebra apply algebraic operations to the primitively represented spanners. This framework is driven by SystemT, an IBM commercial product for text analysis, where the primitive representation is that of regular expressions with capture variables. We define additional types of primitive spanner representations by means of two kinds of automata that assign spans to variables. We prove that the first kind has the same expressive power as regular expressions with capture variables; the second kind expresses precisely the algebra of the regular spanners—the closure of the first kind under standard relational operators. The core spanners extend the regular ones by string-equality selection (an extension used in SystemT). We give some fundamental results on the expressiveness of regular and core spanners. As an example, we prove that regular spanners are closed under difference (and complement), but core spanners are not. Finally, we establish connections with related notions in the literature.

https://researcher.watson.ibm.com/researcher/files/us-fagin/jacm15.pdf

Document Spanners: A Formal Approach to Information Extraction

This paper presents the implementation and evaluation of a Hidden Markov Model to extract addresses from OCR text. Although Hidden Markov Models discover addresses with high precision and recall, this type of Information Extraction task seems to be affected negatively by the presence of OCR text.

Address extraction using hidden Markov models

In this paper, we report on two experiments on identification and extraction of Date of Birth instances. The objective of these experiments is to increase the recall level by increasing the edit distance while obtaining a reasonable precision.

Fuzzy Information Extraction on OCR Text

This paper presents the implementation and evaluation of a pattern-based program to extract date of birth information
from OCR text. Although the program finds data of birth information with high precision and recall, this type of
information extraction task seems to be negatively impacted by OCR errors.

Date of birth extraction using precise shallow parsing

We report on an attempt to build an automatic redaction system by applying information extraction techniques to the identification of private dates of birth. We conclude that automatic redaction is a promising concept although information extraction is significantly affected by the presence of OCR error.

Automatic Redaction of Private Information Using Relational Information Extraction

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Address extraction using hidden Markov models

Fuzzy Information Extraction on OCR Text

Date of birth extraction using precise shallow parsing

Automatic Redaction of Private Information Using Relational Information Extraction