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Words ending in -ed tend to be past tense verbs 5. Frequent use of will is indicative of news text 3. These observable patterns — word structure and word frequency — happen to correlate with particular aspects of meaning, such as tense and topic.
But how did we know where to start looking, which aspects Review questions for chap 2 1 form to associate with which aspects of meaning? The goal of this chapter is to answer the following questions: How can we identify particular features of language data that are salient for classifying it?
How can we construct models of language that can be used to perform language processing tasks automatically?
What can we learn about language from these models? Along the way we will study some important machine learning techniques, including decision trees, naive Bayes' classifiers, and maximum entropy classifiers. We will gloss over the mathematical and statistical underpinnings of these techniques, focusing instead on how and when to use them see the Further Readings section for more technical background.
Before looking at these methods, we first need to appreciate the broad scope of this topic. In basic classification tasks, each input is considered in isolation from all other inputs, and the set of labels is defined in advance.
Some examples of classification tasks are: Deciding whether an email is spam or not. Deciding what the topic of a news article is, from a fixed list of topic areas such as "sports," "technology," and "politics. The basic classification task has a number of interesting variants.
For example, in multi-class classification, each instance may be assigned multiple labels; in open-class classification, the set of labels is not defined in advance; and in sequence classification, a list of inputs are jointly classified.
A classifier is called supervised if it is built based on training corpora containing the correct label for each input. The framework used by supervised classification is shown in 1. These feature sets, which capture the basic information about each input that should be used to classify it, are discussed in the next section.
Pairs of feature sets and labels are fed into the machine learning algorithm to generate a model. These feature sets are then fed into the model, which generates predicted labels.
In the rest of this section, we will look at how classifiers can be employed to solve a wide variety of tasks. Our discussion is not intended to be comprehensive, but to give a representative sample of tasks that can be performed with the help of text classifiers. Names ending in a, e and i are likely to be female, while names ending in k, o, r, s and t are likely to be male.
Let's build a classifier to model these differences more precisely. The first step in creating a classifier is deciding what features of the input are relevant, and how to encode those features.
For this example, we'll start by just looking at the final letter of a given name. The following feature extractor function builds a dictionary containing relevant information about a given name: Feature values are values with simple types, such as booleans, numbers, and strings.
Note Most classification methods require that features be encoded using simple value types, such as booleans, numbers, and strings. But note that just because a feature has a simple type, this does not necessarily mean that the feature's value is simple to express or compute.
Indeed, it is even possible to use very complex and informative values, such as the output of a second supervised classifier, as features.
Now that we've defined a feature extractor, we need to prepare a list of examples and corresponding class labels. The training set is used to train a new "naive Bayes" classifier. For now, let's just test it out on some names that did not appear in its training data: Although this science fiction movie is set init still conforms with our expectations about names and genders.
We can systematically evaluate the classifier on a much larger quantity of unseen data: These ratios are known as likelihood ratios, and can be useful for comparing different feature-outcome relationships.
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Retrain the classifier with these new features, and test its accuracy. When working with large corpora, constructing a single list that contains the features of every instance can use up a large amount of memory. In these cases, use the function nltk.
Much of the interesting work in building a classifier is deciding what features might be relevant, and how we can represent them. Although it's often possible to get decent performance by using a fairly simple and obvious set of features, there are usually significant gains to be had by using carefully constructed features based on a thorough understanding of the task at hand.
Typically, feature extractors are built through a process of trial-and-error, guided by intuitions about what information is relevant to the problem.View Test Prep - Review_Chap_1 (2) from CST AA at Vanier Collegiate.
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Sep 08, · Chap. 1 Review Questions September 8, September 8, wuchristine Leave a comment srmvision.combe why the “discovery” of America was one of the “most important events recorded in the history of mankind,” according to Adam Smith.
Guidance for Industry: HACCP Regulation for Fish and Fishery Products; Questions and Answers for Guidance to Facilitate the Implementation of a HACCP System in Seafood Processing. View Notes - Questions for Review Chap 2(1) (1) from PSYC at Fort Hays State University.
What is meant by the statement “No two children grow up in the same family”? 2. Describe where your.