Understanding Part-of-Speech Tagging in NLP: Techniques and Applications

Part-of-speech (POS) tagging is the process of labeling words in a text with their corresponding parts of speech in natural language processing (NLP). It helps algorithms understand the grammatical structure and meaning of a text.

Table of Content

• Introduction to POS Tagging
• Use of Parts of Speech Tagging in NLP
• Steps Involved in the POS tagging
• Application of POS Tagging
• Implement Parts-Of-Speech tags using Spacy in Python
• Types of POS Tagging in NLP
  • Rule Based POS Tagging
  • Statistical POS Tagging
  • Transformation-based tagging (TBT)
  • Hidden Markov Model POS tagging
• Challenges in POS Tagging
• Conclusion

Introduction to POS Tagging

Part-of-speech (POS) tagging is a process in natural language processing (NLP) where each word in a text is labeled with its corresponding part of speech. This can include nouns, verbs, adjectives, and other grammatical categories.

POS tagging is useful for a variety of NLP tasks, such as information extraction, named entity recognition, and machine translation. It can also be used to identify the grammatical structure of a sentence and to disambiguate words that have multiple meanings.

POS tagging is typically performed using machine learning algorithms, which are trained on a large annotated corpus of text. The algorithm learns to predict the correct POS tag for a given word based on the context in which it appears.

There are various POS tagging schemes that have been developed, each with its own set of tags and rules. Some common POS tagging schemes include the Penn Treebank tagset and the Universal Dependencies tagset.

Let’s take an example,

Text: “The cat sat on the mat.”

POS tags:

• The: determiner
• cat: noun
• sat: verb
• on: preposition
• the: determiner
• mat: noun

In this example, each word in the sentence has been labeled with its corresponding part of speech. The determiner “the” is used to identify specific nouns, while the noun “cat” refers to a specific animal. The verb “sat” describes an action, and the preposition “on” describes the relationship between the cat and the mat.

POS tagging is a useful tool in natural language processing (NLP) as it allows algorithms to understand the grammatical structure of a sentence and to disambiguate words that have multiple meanings. It is typically performed using machine learning algorithms that are trained on a large annotated corpus of text.

Identifying part of speech of word is not just mapping words to their respective POS tags. Same word might have different part of speech tag based on different context. Thus it is not possible to have common mapping for parts of speech tags.

When you have a huge corpus manually finding different part-of-speech for each word is a scalable solution. As tagging itself might take days. This is why we rely on tool-based POS tagging.

But why are we tagging these words with their parts of speech?

Use of Parts of Speech Tagging in NLP

There are several reasons why we might tag words with their parts of speech (POS) in natural language processing (NLP):

1. To understand the grammatical structure of a sentence: By labeling each word with its POS, we can better understand the syntax and structure of a sentence. This is useful for tasks such as machine translation and information extraction, where it is important to know how words relate to each other in the sentence.
2. To disambiguate words with multiple meanings: Some words, such as “bank,” can have multiple meanings depending on the context in which they are used. By labeling each word with its POS, we can disambiguate these words and better understand their intended meaning.
3. To improve the accuracy of NLP tasks: POS tagging can help improve the performance of various NLP tasks, such as named entity recognition and text classification. By providing additional context and information about the words in a text, we can build more accurate and sophisticated algorithms.
4. To facilitate research in linguistics: POS tagging can also be used to study the patterns and characteristics of language use and to gain insights into the structure and function of different parts of speech.

Steps Involved in the POS tagging

Here are the steps involved in a typical example of part-of-speech (POS) tagging in natural language processing (NLP):

1. Collect a dataset of annotated text: This dataset will be used to train and test the POS tagger. The text should be annotated with the correct POS tags for each word.
2. Preprocess the text: This may include tasks such as tokenization (splitting the text into individual words), lowercasing, and removing punctuation.
3. Divide the dataset into training and testing sets: The training set will be used to train the POS tagger, and the testing set will be used to evaluate its performance.
4. Train the POS tagger: This may involve building a statistical model, such as a hidden Markov model (HMM), or defining a set of rules for a rule-based or transformation-based tagger. The model or rules will be trained on the annotated text in the training set.
5. Test the POS tagger: Use the trained model or rules to predict the POS tags of the words in the testing set. Compare the predicted tags to the true tags and calculate metrics such as precision and recall to evaluate the performance of the tagger.
6. Fine-tune the POS tagger: If the performance of the tagger is not satisfactory, adjust the model or rules and repeat the training and testing process until the desired level of accuracy is achieved.
7. Use the POS tagger: Once the tagger is trained and tested, it can be used to perform POS tagging on new, unseen text. This may involve preprocessing the text and inputting it into the trained model or applying the rules to the text. The output will be the predicted POS tags for each word in the text.

Application of POS Tagging

There are several real-life applications of part-of-speech (POS) tagging in natural language processing (NLP):

1. Information extraction: POS tagging can be used to identify specific types of information in a text, such as names, locations, and organizations. This is useful for tasks such as extracting data from news articles or building knowledge bases for artificial intelligence systems.
2. Named entity recognition: POS tagging can be used to identify and classify named entities in a text, such as people, places, and organizations. This is useful for tasks such as building customer profiles or identifying key figures in a news story.
3. Text classification: POS tagging can be used to help classify texts into different categories, such as spam emails or sentiment analysis. By analyzing the POS tags of the words in a text, algorithms can better understand the content and tone of the text.
4. Machine translation: POS tagging can be used to help translate texts from one language to another by identifying the grammatical structure and relationships between words in the source language and mapping them to the target language.
5. Natural language generation: POS tagging can be used to generate natural-sounding text by selecting appropriate words and constructing grammatically correct sentences. This is useful for tasks such as chatbots and virtual assistants.

Implement Parts-Of-Speech tags using Spacy in Python

To use the Python spacy library to implement part-of-speech (POS) tagging, you will need to install the library and download a language model. Here is an example of how to use spacy to perform POS tagging:

1. First, install the spacy library and download a language model:

2. Next, import the spacy library and load the language model:

In case you are getting an error in importing the library, you need to install the spacy library first. You can do ‘pip install spacy’ to install the spacy on your local machine. You can download the specific version of of spacy if you want or else you can avoid mentioning the version and the pip install will get you the latest version of the library.

As a next step we will load the en_core_web_sm medium-sized English model trained on written web text (blogs, news, comments), that includes a tagger, a dependency parser, a lemmatizer, a named entity recognizer and a word vector table with 20k unique vectors.

3. Once you are done loading the trained English model, as a next step you can directly add a sentence to check the the POS tags assigned to it. Spacy will automatically add the parts of speech tag to it.

To perform POS tagging on a text, you can use the

object to process the text and access the POS tags of the words:

token.pos_ will give the POS tag of the specific token. token.tag_ will give you a detailed aspect of the POS tag assigned to the token. It output of them are abbreviated as AUX, PROPN, PART, etc. If you want a detail of it you can use spacy.explain( ) to understand the POS tag better.

Output

Apple | PROPN | proper noun | NNP noun, proper singular
is | AUX | auxiliary | VBZ verb, 3rd person singular present
planning | VERB | verb | VBG verb, gerund or present participle
to | PART | particle | TO infinitival "to"
buy | VERB | verb | VB verb, base form
Indian | ADJ | adjective | JJ adjective (English), other noun-modifier (Chinese)
startup | NOUN | noun | NN noun, singular or mass
for | ADP | adposition | IN conjunction, subordinating or preposition
$ | SYM | symbol | $ symbol, currency
1 | NUM | numeral | CD cardinal number
billion | NUM | numeral | CD cardinal number

Here you can see that spacy has tokenized the sentence and added specific POS tags to each of the words. Along with parts of speech tags spacy has also identified the symbols and numerals.

Note: Spacy uses its own set of POS tags, which may be different from other POS tagging schemes. You can find a list of Spacy’s POS tags here.

There are many other libraries and tools available in Python for performing POS tagging, such as nltk and stanfordnlp. You can choose the one that best fits your needs and use it to implement POS tagging in your Python applications.

Types of POS Tagging in NLP

Rule Based POS Tagging

Rule-based part-of-speech (POS) tagging is a method of labeling words with their corresponding parts of speech using a set of pre-defined rules. This is in contrast to machine learning-based POS tagging, which relies on training a model on a large annotated corpus of text.

In a rule-based POS tagging system, words are assigned POS tags based on their characteristics and the context in which they appear. For example, a rule-based POS tagger might assign the tag “noun” to any word that ends in “-tion” or “-ment,” as these suffixes are often used to form nouns.

Rule-based POS taggers can be relatively simple to implement and are often used as a starting point for more complex machine learning-based taggers. However, they can be less accurate and less efficient than machine learning-based taggers, especially for tasks with large or complex datasets.

Here is an example of how a rule-based POS tagger might work:

1. Define a set of rules for assigning POS tags to words. For example:

• If the word ends in “-tion,” assign the tag “noun.”
• If the word ends in “-ment,” assign the tag “noun.”
• If the word is all uppercase, assign the tag “proper noun.”
• If the word is a verb ending in “-ing,” assign the tag “verb.”

2. Iterate through the words in the text and apply the rules to each word in turn. For example:

• “Nation” would be tagged as “noun” based on the first rule.
• “Investment” would be tagged as “noun” based on the second rule.
• “UNITED” would be tagged as “proper noun” based on the third rule.
• “Running” would be tagged as “verb” based on the fourth rule.

3. Output the POS tags for each word in the text.

This is a very basic example of a rule-based POS tagger, and more complex systems can include additional rules and logic to handle more varied and nuanced text.

Statistical POS Tagging

Statistical part-of-speech (POS) tagging is a method of labeling words with their corresponding parts of speech using statistical techniques. This is in contrast to rule-based POS tagging, which relies on pre-defined rules, and to unsupervised learning-based POS tagging, which does not use any annotated training data.

In statistical POS tagging, a model is trained on a large annotated corpus of text to learn the patterns and characteristics of different parts of speech. The model uses this training data to predict the POS tag of a given word based on the context in which it appears and the probability of different POS tags occurring in that context.

Statistical POS taggers can be more accurate and efficient than rule-based taggers, especially for tasks with large or complex datasets. However, they require a large amount of annotated training data and can be computationally intensive to train.

Here is an example of how a statistical POS tagger might work:

1. Collect a large annotated corpus of text and divide it into training and testing sets.
2. Train a statistical model on the training data, using techniques such as maximum likelihood estimation or hidden Markov models.
3. Use the trained model to predict the POS tags of the words in the testing data.
4. Evaluate the performance of the model by comparing the predicted tags to the true tags in the testing data and calculating metrics such as precision and recall.
5. Fine-tune the model and repeat the process until the desired level of accuracy is achieved.
6. Use the trained model to perform POS tagging on new, unseen text.

There are various statistical techniques that can be used for POS tagging, and the choice of technique will depend on the specific characteristics of the dataset and the desired level of accuracy.

Transformation-based tagging (TBT)

Transformation-based tagging (TBT) is a method of part-of-speech (POS) tagging that uses a series of rules to transform the tags of words in a text. This is in contrast to rule-based POS tagging, which assigns tags to words based on pre-defined rules, and to statistical POS tagging, which relies on a trained model to predict tags based on probability.

In TBT, a set of rules is defined to transform the tags of words in a text based on the context in which they appear. For example, a rule might change the tag of a verb to a noun if it appears after a determiner such as “the.” The rules are applied to the text in a specific order, and the tags are updated after each transformation.

TBT can be more accurate than rule-based tagging, especially for tasks with complex grammatical structures. However, it can be more computationally intensive and requires a larger set of rules to achieve good performance.

Here is an example of how a TBT system might work:

1. Define a set of rules for transforming the tags of words in the text. For example:

• If the word is a verb and appears after a determiner, change the tag to “noun.”
• If the word is a noun and appears after an adjective, change the tag to “adjective.”

2. Iterate through the words in the text and apply the rules in a specific order. For example:

• In the sentence “The cat sat on the mat,” the word “sat” would be changed from a verb to a noun based on the first rule.
• In the sentence “The red cat sat on the mat,” the word “red” would be changed from an adjective to a noun based on the second rule.

3. Output the transformed tags for each word in the text.

This is a very basic example of a TBT system, and more complex systems can include additional rules and logic to handle more varied and nuanced text.

Hidden Markov Model POS tagging

Hidden Markov models (HMMs) are a type of statistical model that can be used for part-of-speech (POS) tagging in natural language processing (NLP). In an HMM-based POS tagger, a model is trained on a large annotated corpus of text to learn the patterns and characteristics of different parts of speech. The model uses this training data to predict the POS tag of a given word based on the probability of different tags occurring in the context of the word.

An HMM-based POS tagger consists of a set of states, each corresponding to a possible POS tag, and a set of transitions between the states. The model is trained on the training data to learn the probabilities of transitioning from one state to another and the probabilities of observing different words given a particular state.

To perform POS tagging on a new text using an HMM-based tagger, the model uses the probabilities learned during training to compute the most likely sequence of POS tags for the words in the text. This is typically done using the Viterbi algorithm, which calculates the probability of each possible sequence of tags and selects the most likely one.

HMMs are widely used for POS tagging and other tasks in NLP due to their ability to model complex sequential data and their efficiency in computation. However, they can be sensitive to the quality of the training data and may require a large amount of annotated data to achieve good performance.

Challenges in POS Tagging

Some common challenges in part-of-speech (POS) tagging include:

• Ambiguity: Some words can have multiple POS tags depending on the context in which they appear, making it difficult to determine their correct tag. For example, the word “bass” can be a noun (a type of fish) or an adjective (having a low frequency or pitch).
• Out-of-vocabulary (OOV) words: Words that are not present in the training data of a POS tagger can be difficult to tag accurately, especially if they are rare or specific to a particular domain.
• Complex grammatical structures: Languages with complex grammatical structures, such as languages with many inflections or free word order, can be more challenging to tag accurately.
• Lack of annotated training data: Some languages or domains may have limited annotated training data, making it difficult to train a high-performing POS tagger.
• Inconsistencies in annotated data: Annotated data can sometimes contain errors or inconsistencies, which can negatively impact the performance of a POS tagger.

Conclusion

Part-of-speech (POS) tagging is a crucial step in natural language processing (NLP), as it allows algorithms to understand the grammatical structure and meaning of a text. There are several methods for performing POS tagging, including rule-based, statistical, transformation-based, and hidden Markov model (HMM) tagging.

Rule-based POS tagging relies on a set of pre-defined rules to assign tags to words, while statistical POS tagging uses a trained model to predict tags based on probability. Transformation-based tagging (TBT) uses a series of rules to transform the tags of words based on context, and HMM tagging uses an HMM to learn the patterns and characteristics of different parts of speech.

Each method has its own strengths and limitations, and the choice of method will depend on the specific characteristics of the dataset and the desired level of accuracy. Overall, POS tagging is an important tool in NLP that helps algorithms understand and analyze the structure and meaning of text.