Text Analysis with Visual Dependency Parsing
Description
Text analysis is the process of obtaining information from a lot of text data. Natural language processing (NLP) is the process through which a program comprehends and makes sense of this data. If you want to get deeper information about your given text, this demo will help you and you can easily analyze your text.
You should know the following points to understand the phenomenon:
You should know the following points to understand the phenomenon:
- TEXT: the original text of the token, as it appears in the input text.
- LEMMA: the base form of the token, also known as its "dictionary form".
- POS: the part-of-speech tag of the token, indicating its grammatical role in the sentence.
- TAG: a more detailed version of the part-of-speech tag, providing additional information about the token's tense, aspect, person, number, etc.
- DEP: the dependency relationship between the token and its head word in the sentence.
- SHAPE: the shape of the token, indicating its capitalization and punctuation.
- ALPHA: a boolean value indicating whether the token consists entirely of alphabetic characters.
- STOP: a boolean value indicating whether the token is a stop word, i.e. a common word that is often removed from text before analysis.
Dependency parsing is used to generate a chart that shows the relationships between the words in the input text. The chart is a graphical representation of the syntactic structure of the text, with each word represented as a node in a graph. The arcs connecting the nodes indicate the relationship between the words and their dependencies, such as subject-verb and object-verb relationships. This chart can be useful for understanding the grammatical structure of the text and identifying potential errors or ambiguities. Take further help from here.
Technical Background
This demo is built on Python and it uses a famous package of Python i.e Spacy. SpaCy is a library for advanced Natural Language Processing in Python, spaCy comes with pretrained pipelines and currently supports tokenization and training for 70+ languages.
Use Cases
For educational purposes we can use this demo. For example, we have the text and want to know the details of this text and analyze the relation between each words this demo can be helpful.
How can we use this demo?
In this app we will give text as input and it will provide us details about every word and their dependency using dependency parsing. You can use the given examples as well by just clicking on it.