Textual Information Access: Statistical Models

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But this is true only when we correctly reformulate the task as an annotation or labeling problem.

Statistical Regression and Classification: From Linear Models to Machine Learning

The best statistical models capable of learning data annotation are conditional random fields CRFs. This chapter is thus an opportunity to present the task of information extraction and the statistical labeling models able to handle it. The first two sections concentrate on the task, by discussing its issues and the specific problems posed. The following four sections focus on statistical Stay ahead with the world's most comprehensive technology and business learning platform. With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Inherent here is that the data be complete, clean, diverse, readily available in the future, and causal — not merely corollary. Moreover, there has to be enough causally associated, labeled, and clean data to run experiments on different models to determine their comparative effectiveness in predicting via train-test cycles.

dblp: Textual Information Access: Statistical Models

For example, if one was trying to predict economic growth or contraction, they would need a large enough quantity and diversity of clean and labeled data to be able to determine which of those data elements were causal — or telltales — as to when the economy was growing or contracting, by how much, and at what acceleration or deceleration arguably, this would probably be best done quickly and reasonably using random forest decision trees from Salford Systems, which could easily determine which elements were causal and in what proportions.

Once key causal terms are identified, sentiment analysis can infer meaning, and clustering can monitor shifts in sentiment. In many cases, this prerequisite is a focus of substantial time and resources and rightfully so. It may take many data fishing expeditions to find the data elements that are most causal or predictive of the wanted outcome, after which, training a machine learning system to monitor and predict shifts is only the second half of the job.

Once data sources have been identified and causality confirmed in its features and labeled, data cleansing and organization can be deftly handled by a dozen or so R functions in easily accessible and well-versed libraries. Alternatively, text analytics can be used to identify and monitor trends. Typically, this means the training is different in a way that is faster and cheaper to do. The analysis is focused more on concept extraction than causality and prediction. Even these sentiment trends though can be monitored overtime to detect changing sentiments as clusters shift.

The decision to use supervised or unsupervised more automatic machine learning for text analytics largely depends on which step in the process is being performed, how much text needs to be analyzed how often, and how perfect it needs to be e. Smaller datasets that can be turned around more slowly, or with strategic timelines, may lend themselves more to supervised machine learning approaches.

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Larger data sets that need to provide streams of recommendations or predictions, regardless of timelines, lend themselves to unsupervised machine learning approaches, provided they can deliver the desired degree of accuracy, sensitivity, and specificity. Lexical Coverage. One of the key steps in applying or adapting text analytics to different domains is preparation to infer sentiment, or feature extraction. Historically, something called n-grams were created by converting words into tokens that could be represented in binary zeros and ones.

Newer methods using transfer learning — reused elements from prior models — are now both faster and far more accurate. In the case of having a lexicon to predict and classify sentiment, FastAI, which is a model pre-trained on Wikipedia, is an advanced starting point for building a customized language model for specific domain applications. There are often other domain adaptations that must be taken into consideration; however, lexicon and vocabulary are universally major considerations. There is an aphorism that requests can be fulfilled cheaply, perfectly, or quickly, and you only ever get to pick two of those requirements because all three are impossible.


Non parametric statistical models for on-line text classification

My 20 years experience applying predictive analytics and data science has proven that correct. Money, talent, and time restrict most projects, including those in data science, and it is a positive thing that they do, else spending would be ad nauseam and a positive return on the investment would be highly improbable. Decisions taken regarding which tools to use often depend on the tools available.

Similarly, it may depend on the talent resources a project has access to and for how long. A classic example in data science is whether to use the R statistical language or Python general language.

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While there are advantages and disadvantages to both I prefer R , it may depend on what language you have available to you on your team, which languages your teammates know. Regarding timelines, supervised text analytics is far more labor intensive and thus, could take longer ; however, custom coding, feature extraction, and tuning of unsupervised machine learning algorithms, especially for predictions, can also be time intensive.

A larger team probably lends itself to supervised approaches and a smaller team and more data probably lends itself to unsupervised approaches. Most of the consumers and decision makers employing machine learning are executives without a specialized background in data science, or even statistics. Or, even if they do have such backgrounds, machine learning is far enough away for their domain expertise and moves so quickly as to make its value recognizable but not known in detail or thoroughly. Therefore, for these decision makers to use the predictive models they must trust them, and to trust them, they must understand them.

Hence, explainability becomes a key consideration. One way to maximize explainability is to use visualizations for feature analysis. One favored visualization, which is an unsupervised machine learning technique in and of itself, is self-organizing maps SOMs. SOMs classify data into clustered segments based on similar traits. In ecommerce, for example, they cluster groups of customers into those that have high spending but low frequency, or high frequency but low spending.

In ecommerce, this informs how a group can be targeted with marketing or behavioral interventions.

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In sentiment analysis, the positivity or negativity and strength or weakness of sentiments can also be clustered using self-organizing maps SOM. If this analysis is repeated in different time frames, it is also possible to see how sentiment is shifting, in what direction, and at what speed or rate. From these shifting clusters one can calculate probabilities of future directions with confidence intervals. This necessitates a cyclical approach of ingest, analyze, repeat, and adds a clustering function to words, for example, after the concept extraction.

For example, imagine seeing a shift towards economic contraction registered by word usage in corporate 10K filings that is increasing at an increasing rate. One could theoretically calculate the probability of recession, or unemployment claims, or borrowing or savings rates, with reasonable accuracy. The ability to predict with probability where an economy is heading allows for preventive interventions to maximize its outcomes. These economic predictions can also be made based on consumer or corporate sentiment in text-based Big Data for different industries, geographies, or both and assembled into multi-dimensional databases for visualizations.

This would allow organizations to more accurately predict which sectors or regions are cooling and which are warming at different times to different degrees, or what issues are trending where, and sometimes, why. Key here is the notion of predictiveness. Historically, significant latency was introduced between when observations were made, recorded, analyzed, and new decisions were taken. These tools allow latency to be largely ameliorated and shifted into preventative policy or management, which also applies to discovering trends, not just causal predictions.

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At the end of the day, explainability is such a critical issue that experts will often recommend that it is better to have an understandable and explainable approach or model over one that might perform slightly better but is inexplicable. Value Proposition. Once these prototypes are socialized and gain trust, the budget, timeline, and sophistication of text analytics can evolve, during which time substantially more text data is also almost always available to better train and inform outcomes and predictions and trend analysis.

The possibilities of what can be done with text analytics grows every day because of the rapid growth of the corpus of text-based communications over time. At the end of the day, value or return on investment is what makes most business cases regarding how much budget — effort, time, talent — to invest in text analytics. Governments, multinational corporations, and healthcare have business cases to deploy text analytics, and other forms of machine learning, in evolutionary waves. Text analytics can offer extraordinary insights into public sentiment for economics, finance, ecommerce, and social and geopolitical issues.

In large part, this is because human behaviors on social media and the digitization of communications are creating such a massive corpus of textual data to mine. If you lead an organization or business in which the capability to predict consumer or large population sentiment is valuable, or analyze sentiment trends and how they shift over time in different cohorts, you probably have a business case to explore text analytics with real world experiments.

Lecture 06 - Statistical Models in Simulation

Brinegar, C. Journal of the American Statistical Association , 58 : 85— Griffith, E. Hobbs, J. Natural language access to structured text. Prague, Czechoslovakia: Academia Praha.

Top 63 software for text analysis, text mining, text analytics. Madigan, D.