Classification of Morphemes
We can classify Morphemes into two categories: Content and Functional morphemes.
1. Content Morpheme- Content morphemes are those morphemes that contain some semantic meaning—Example, car, -able, un-, etc.
2.Functional Morphemes- Functional morphemes provide grammatical information about a word.
Example: -s(plural), -s(third singular).
We will generate a new word based on the type of affixes that we applied with the word.
Case 1- If I have a word walk and apply a suffixing, I will get a new word walking.
Case 2- If I have a word drive with the suffix 'er,' it is converted into the driver.
In the first case, we are not changing the word's grammatical category. 'Walk' is a verb, and it remains a verb in 'Walking.' On the other hand, we started with a verb called drive, which is converted into a noun. So there are two different kinds of morphological processes applied here. The first one is called Inflectional Morphology, and the second one is called Derivational Morphology.
1.Inflectional Morphology- It creates a new form of the same word that is bring => brought, brings => bringing, etc.
2.Derivational Morphology- it creates new words by changing part of speech that is logic => logical, illogical => illogical, etc.
Morphological Processes
So far, we have talked about morphemes and different types of exercise that we can apply. But in general, what are the other morphological processes involved to convert one word into another?
Concatenation- We take various Morphemes and concatenate them to form a single morpheme. For Example, adding continuous affixes, Hope+less, un+happy, anti+capital+ist+s.
When we combine different morphemes, there may be some changes at the boundary. The changes may happen in the way of the final word pronounced or the final word written. For example, shoe+s => shoes read as shoes.
Suppletion- In Suppletion, we replace the word entirely with a term that has no connection with the original word. For example, if we convert go into the past tense, it becomes went.
We cannot find any connection between 'go,' and 'went' at the surface level.
From the NLP perspective, how do we process these morphologies? What are the different operations that are done?
Lemmatization- Given a word, can we identify what is the root word of the Lemma? For example, in the word Saw, the root word is See.
Morphological analysis- Given a word, can we find the Lemma along with the morphological category of the given word.?
Tagging- Given a word, can we find the actual category of the word? For example, the fundamental type of the word went is 'Past.'
Morphological segmentation- Given a word, we can segment it into different morphemes involved in it. Example: 'de-national-iz-ation.'
Generation- We can take a root word and a particular grammatical category To generate a new buzz.
Morphological Analysis
The goal of the morphological analysis is to take a word as an input and produce the morphological parsed output. For example, if the input word is 'cats,' the morphological parsed result will be 'cat +N +PL .'The result contains stem and additional information; +N for noun, +SG for singular, +PL for plural, +V for verb, etc.
Applications
1. Text to speech synthesis
2. search and Information retrieval
3. machine translation and grammar correction
FAQs
1. What is Morphology?
In Morphology, we study the internal structure of words and how words are built up from smaller meaningful units called morphemes.
2. What is a Content morpheme?
Content morphemes contain some semantic meaning—Example, car, -able, un-, etc.
3. What is a Functional morpheme?
Functional morphemes provide grammatical information of a word.Example: -s(plural), -s(third singular).
4. What is Lammenization?
In Lemmatization, we can identify the root word of the Lemma. For example, in the word Saw, the root word is See.
5. What is a Suppletion morphology?
In Suppletion, we replace the word with a word that has no connection with the original word. For example, if we convert go into the past tense, it becomes gone.
Key Takeaways
In this blog, we learned the morphological information that helps us capture the data and different linguistic terms along with the applications. Computational Morphology is an essential part of Natural Language Processing, a domain of Machine Learning. Do Check Restaurant Review Analysis using NLP.