Understanding NLP models
Regardless of the NLP task being performed or the NLP tool set being used, there are several steps that they all have in common. In this section, we will present these steps. As you go through the chapters and techniques presented in this book, you will see these steps repeated with slight variations. Getting a good understanding of them now will ease the task of learning the techniques.
The basic steps include:
- Identifying the task
- Selecting a model
- Building and training the model
- Verifying the model
- Using the model
We will discuss each of these tasks in the following sections.
Identifying the task
It is important to understand the problem that needs to be solved. Based on this understanding, a solution can be devised that consists of a series of steps. Each of these steps will use an NLP task.
For example, suppose we want to answer a query such as "Who is the mayor of Paris?" We will need to parse the query into the POS, determine the nature of the question, the qualifying elements of the question, and eventually use a repository of knowledge, created using other NLP tasks, to answer the question.
Other problems may not be quite as involved. We might only need to break apart text into components so that the text can be associated with a category. For example, a vendor's product description may be analyzed to determine the potential product categories. The analysis of the description of a car would allow it to be placed into categories such as sedan, sports car, SUV, or compact.
Once you have an idea of what NLP tasks are available, you will be better able to match it with the problem you are trying to solve.
Selecting a model
Many of the tasks that we will examine are based on models. For example, if we need to split a document into sentences, we need an algorithm to do this. However, even the best sentence boundary detection techniques have problems doing this correctly every time. This has resulted in the development of models that examine the elements of text and then use this information to determine where sentence breaks occur.
The right model can be dependent on the nature of the text being processed. A model that does well for determining the end of sentences for historical documents might not work well when applied to medical text.
Many models have been created that we can use for the NLP task at hand. Based on the problem that needs to be solved, we can make informed decisions as to which model is the best. In some situations, we might need to train a new model. These decisions frequently involve trade-offs between accuracy and speed. Understanding the problem domain and the required quality of results permits us to select the appropriate model.
Building and training the model
Training a model is the process of executing an algorithm against a set of data, formulating the model, and then verifying the model. We may encounter situations where the text that needs to be processed is significantly different from what we have seen and used before. For example, using models trained using journalistic text might not work well when processing tweets. This may mean that the existing models will not work well with this new data. When this situation arises, we will need to train a new model.
To train a model, we will often use data that has been "marked up" in such a way that we know the correct answer. For example, if we are dealing with POS tagging, then the data will have POS elements (such as nouns and verbs) marked in the data. When the model is being trained, it will use this information to create the model. This dataset is called a corpus.
Verifying the model
Once the model has been created, we need to verify it against a sample set. The typical verification approach is to use a sample set where the correct responses are known. When the model is used with this data, we are able to compare its result to the known good results and assess the quality of the model. Often, only part of a corpus is used for training while the other part is used for verification.
Using the model
Using the model is simply applying the model to the problem at hand. The details are dependent on the model being used. This was illustrated in several of the earlier demonstrations, such as in the Detecting Parts of Speech section where we used the POS model as contained in the en-pos-maxent.bin file.
