Natural Language Processing: Adding the Human Voice to Computers

By Anolytics | 09 April, 2024 in Content Moderation | 4 mins read

Natural Language Processing

Natural language processing (NLP) can cross-reference the data with natural language text or voice. It is the backbone of virtual assistants like Oracle Digital Assistant (ODA), Siri, Cortana, Alexa, etc. It not only enables these virtual assistants to comprehend the user’s request, but also respond in natural language. It also assists businesses in enhancing their efficiency, productivity and performance by simplifying complicated tasks which involve language. NLP utilizes computational techniques for analyzing and comprehending human language. It comprises of tasks like language comprehension, language generation, and language interaction.

Working of Natural Language Processing

1. Speech Recognition: It involves translation of spoken language to text and is a critical component of any application requiring voice commands or spoken response. Speech recognition poses certain key challenges including speaking at a fast pace, slurring words, using various accents as well as wrong grammar.

2. Part of Speech Tagging: This is also called grammatical tagging and is a critical process for determining the part of speech of a given text as per context and use. It is used to differentiate between usage of a given word as a verb or noun based on context. For instance, “I can make a card.” and “What make of car do you own?”

3. Named Entity Recognition (NER): This involves identifying useful entities or phrases. Co-reference resolution involves identification of two words referring to the same entity. Sentiment analysis involves extracting subjective qualities like attitudes, emotions, sarcasm, confusion and suspicion from a given text.

4. Natural Language Generation  (NLG):  This involves generation of natural language by a computer. In contrast to speech recognition, NLG involves putting structured information into human language.

Applications of Natural Language Processing

1. Routine Task Automation: NLP-powered chatbots process vast quantities of routine tasks handled by humans. This frees up employees to work on challenging and interesting tasks.

2. Enhancing Search: NLP enhances keyword matching search to retrieve document and FAQ through disambiguation of word senses depending on contexts. matching synonyms as well as by taking into account morphological variation. Efficient NLP-powered academic search systems drastically enhance access for cutting-edge research for doctors, lawyers, and other specialists.

3. Search Engine Optimization: NLP helps in content optimization by ranking your business high in online searches. It is used by search engines to rank results resulting in better business visibility.

4. Analysis and organization of large documents: Techniques like document clustering and topic modeling lead to simplification of task of comprehending the content variety in wider document collections including corporate reports, news articles, or scientific documents.

5. Social media analytics: NLP analyses customer reviews and comments on social media for interpreting vast amounts of information. Sentiment analysis is used for identifying positive and negative comments for offering a direct measure of customer sentiment in real time. This in turn results in massive payoffs like highest customer satisfaction and revenue.

6. Market insights: NLP works in analyzing the language of your business customers and enables one to have a better idea of their needs and ways to communicate with them. Sentiment analysis based on aspect is used for detecting the sentiment linked with specific aspects or products in social media and offers them direct actionable information for designing and marketing products.

7. Content moderation: As businesses garner vast amounts of customer comments, NLP enables one to moderate spoken words with the aim of maintaining quality and civility through analyses of spoken words as well as the tone and intent of comments.

Key Challenges in Natural Language Processing

Owing to the complex and diverse nature of human language, NLP suffers from certain key challenges as discussed below.

1. Difference in Language: The understanding of human language is rich and intricate as several languages are spoken by them. As humans cannot comprehend all languages, its productivity is high. There is also a huge challenge with natural language comprehension as same words and phrases can have varied meaning in varied contexts.

2. Training Data: This is a collection of input-output pairs. The input represents the features of the data and the output its corresponding label or target. In case of NLP, the features comprise of text data and labels comprise of categories, sentiments, etc. It assists the model in generalizing patterns from the training data set with the aim of making predictions on data that was not seen before.

3. Bias Mitigation in NLP Algorithms: This is done with the aim of confirming fairness, equity and inclusivity in NLP applications. Let’s look at the points listed below for bias mitigation in NLP algorithms.

  1. Data Collection and Annotation: The training data used for developing NLP algorithms must be diverse, symbolic and bias-free.
  2. Bias Detection and Analysis: This helps in finding biases based on demographic factors like race, gender and age.
  3. Data Pre-processing: This helps in mitigating biases which include debiasing word embeddings, balance class distributions and augmenting underrepresented samples.
  4. Fair representation learning: NLP models acquire training for learning fair representations which are invariant for protecting attributes including race and gender.
  5. Model Evaluation and Auditing: Metrics and audits are used for evaluating the fairness and bias of natural language models. These models are assessed on varied datasets for performing analyses for searching and mitigating inherent biases in NLP algorithms.

4. Typos and Grammatical Errors: These are key challenges in NLP as they tend to affect the precision of comprehension and analysis. Given below are key points for resolving spelling and grammatical errors in NLP.

  1. Spell check: Implementing spell check algorithms for finding and rectifying typos.
  2. Text Normalization: This involves converting text to lowercase, removing punctuation and special characters as well as opening contractions.
  3. Tokenization: This permits identification and isolation of typos and grammatical errors enabling easy correction of the phrase.
  4. Language models: Using large language models for predicting the probability of word or phrase as correct or independent of context.

Hence, NLP is a great discipline with potential to impact the world in manifold ways. However, NLP suffers from certain key challenges which have been discussed above. Also, sophisticated language models might generate disinformation with the larger concern that training large models generates a good amount of greenhouse gas emissions.

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