Named Entity Recognition (NER) for Telugu.pptx

NER

1. Named Entity Recognition (NER) for Telugu

2. What is NER ? Named Entity Recognition (NER) is a crucial task in Natural Language Processing (NLP). It involves identifying and classifying named entities in text into predefined categories such as: Persons: Names of people. Locations: Names of places. Organizations: Names of companies, institutions, etc. Dates: Temporal expressions. Miscellaneous: Other named entities.

3. W hy NER is Important: It enables computers to extract structured information from unstructured text, making it valuable for: Information Extraction: Automating the process of gathering key details from documents.   Search Engines: Improving search accuracy by understanding the context of queries.   News Analysis: Categorizing and summarizing news articles.   Chatbots: Enhancing the ability of chatbots to understand user input.

4. How it Works: NER Approaches: 1. Rule-Based Systems : Uses predefined patterns (e.g., regex) to identify entities. Example: Recognizing capitalized words as proper nouns. 2.Machine Learning-Based Systems : Uses algorithms like Conditional Random Fields (CRF) or Hidden Markov Models . Requires labeled training data for entity recognition. 3.Deep Learning-Based Systems : Uses neural networks (e.g., LSTMs, Transformers like BERT) to recognize entities. Learns context better and handles complex sentence structures.

5. Example: Input: "Elon Musk is the CEO of Tesla, which is headquartered in California." NER Output: Person: Elon Musk Organization: Tesla Location: California

6. What is the use of NER for Telugu Named Entity Recognition (NER) is valuable for any language, and Telugu is no exception. Here's a breakdown of the specific uses of NER for the Telugu language: 1. Information Extraction:  News Analysis: NER can automatically extract key information from Telugu news articles, such as the names of politicians, locations of events, and organizations involved. This helps in summarizing and analyzing news content.  Data Mining: It can be used to extract structured information from large volumes of Telugu text, which can then be used for data analysis and research. 2. Search Engine Improvement:  Enhanced Search Accuracy: NER can help search engines better understand Telugu search queries by identifying named entities, leading to more relevant search results.

7. 3. Question Answering Systems:  Accurate Responses: In question-answering systems, NER can identify the entities mentioned in a question and then search for relevant information in Telugu text. 4. Machine Translation:  Improved Translation Quality: NER can help machine translation systems better understand the context of Telugu text, leading to more accurate translations. By identifying named entities, the system can preserve those entities in the translated text. 5. Social Media Analysis:  Sentiment Analysis: NER can be used to identify entities mentioned in Telugu social media posts, which can then be used to analyze public sentiment towards those entities.  Trend Identification: It can help identify trending topics and entities in Telugu social media.

8. Applications for NER for Telugu 1.Information retrieval and search engines. 2.News analysis and media monitoring. 3.Question answering systems. 4.Machine translation. 5.Social media analysis.

9. Challenges and Considerations: Resource Limitations: As with many less-resourced languages, the availability of annotated Telugu data for training NER models is a challenge. Linguistic Complexity: The morphological complexity of Telugu can make NER tasks more difficult.

10. T HANK Y OU