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Natural Language Processing (NLP) is:
i) Teaching machines to understand and produce language (e.g. text, speech).
ii) A combination of computer science and computational linguistics.
NLP encompasses a broader spectrum of tasks and challenges. It involves a range of tasks to enable computers to understand, interpret, and generate human-like language, including the main steps below:
Language Modeling: Developing models that can predict the probability of a sequence of words, which is fundamental for many NLP tasks.
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Text Classification: Assigning predefined categories or labels to a given piece of text based on its content.
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Named Entity Recognition (NER): Developing accurate NER systems involves creating annotated datasets with labeled entities, which can be a time-consuming process. Additionally, maintaining and updating NER models for new entities or domains requires ongoing effort. While NER is more focused on identifying entities, understanding the syntactic context in which entities appear can enhance the accuracy of NER models. Training NER models often involves incorporating syntactic features. This is normally the secondary labor-intensive and time-consuming step in NLP.
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Information Extraction: Extracting structured information from unstructured text involves defining and annotating relationships between entities. Designing and maintaining knowledge bases, as well as dealing with the intricacies of different types of information, contribute to the labor intensity of this task.
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Text Summarization (Extractive): Generating concise and coherent summaries of larger pieces of text. -
Text Summarization (Abstractive): Abstractive summarization, which involves generating novel sentences to summarize content, is a challenging task. It often requires a deep understanding of the input text and the ability to generate coherent and contextually appropriate summaries. This can be a labor-intensive and time-consuming step in NLP.
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Machine Translation: Translating text from one language to another is a complex task, especially for languages with significant linguistic differences. Creating high-quality translation models often requires large parallel corpora for training, manual evaluation, and continuous refinement. This is normally the most labor-intensive and time-consuming step in NLP.
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Question Answering: Developing robust question-answering systems involves creating datasets with accurately labeled questions and answers. Training models for comprehension, reasoning, and context-aware answering can be labor resource-intensive.
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Sentiment Analysis: Determining the sentiment or emotion expressed in a piece of text, such as positive, negative, or neutral.
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Coreference Resolution: Resolving references in a text to determine which words or phrases refer to the same entity.
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Semantic Role Labeling (SRL): SRL is the task of identifying the roles of words in a sentence, such as the agent, patient, or theme. Syntax plays a crucial role in understanding these roles, and training SRL models often includes capturing syntactic information to improve role labeling accuracy.
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Text Generation: Creating human-like text, which can be used for tasks like chatbots, content creation, or story generation. When generating human-like text, understanding syntax is crucial for producing grammatically correct and coherent sentences. Training models for text generation involves learning syntax rules and structures to generate contextually appropriate language.
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Speech Recognition: Converting spoken language into written text, enabling machines to understand and process spoken input.
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Parsing: Analyzing the grammatical structure of sentences to understand their syntactic components. Parsing involves analyzing the grammatical structure of sentences, determining how words relate to each other, and identifying syntactic components such as subjects, objects, and predicates. Training models for parsing requires understanding the syntax of the language and learning to represent the hierarchical relationships between words.
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Text Clustering: Grouping similar documents or sentences together based on their content.
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Conversational AI: Building conversational agents capable of understanding and generating natural language in dynamic and context-rich interactions is a complex labor task. This involves not only language understanding but also context management and generating coherent responses.
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Language Translation beyond Text: Beyond written text, NLP can also involve tasks like translating spoken language, sign language, or even visual information into textual representation. -
Information Retrieval: Finding relevant information from a large dataset or document collection in response to a user's query. -
Topic Modeling: Identifying the main topics or themes present in a collection of documents. -
Dependency Parsing: Analyzing the grammatical relationships between words in a sentence. Similar to parsing, dependency parsing specifically focuses on identifying grammatical dependencies between words in a sentence. Training models for dependency parsing involves learning the syntactic relationships and dependencies within a given language.
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Training of syntax: The training of syntax is relevant to several steps mentioned above, particularly those involving syntactic analysis and understanding of grammatical structures: parsing, dependency parsing, SRL, text generation, and NER.
Each task above addresses a specific aspect of language understanding, and collectively, they contribute to the broader goal of enabling machines to comprehend and interact with human language. Some steps above are really labor-intensive. However, advancements in pre-trained language models, such as BERT, GPT, and their variants, have significantly improved the efficiency of training and performance across various NLP tasks. Fine-tuning these models for specific applications and ensuring their adaptation to domain-specific data can still require substantial effort.
In natural language processing, we have to make the words understandable for computers. There are several ways to do this, for instance, one hot encoding. In this one hot encoding technique, we create a one hot vector (a vector which has only one 1 value, also others have to be 0) which has a length number of the words we have in our vocab. As an example, for a vocab like {"This","is","Yougui","Liao"}, then each vector for a word will 4D. The vector of "This" is [1,0,0,0], and so on. However, if we compute the distance between "This" and "is", and "is" and "Yougui", we can see the distances are same and thus we could not protect the real relationships between the words. Fortunately, in Word Embeddings, each element of vector is a different number. For instance, for the same vocab like {"This","is","Yougui","Liao"}, their vectors might like this "This" = [1.12,1.42.1,45.1,52].
Table 4315. Text classification algorithms for Natural Language Processing (NLP).
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Commonalities |
Differences |
| Naive Bayes |
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Text Classification: All of these algorithms can be used for text classification tasks, such as sentiment analysis, spam detection, and topic categorization.
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Feature Engineering: They can work with different types of feature representations, including bag-of-words (BoW), TF-IDF, and word embeddings like Word2Vec or GloVe.
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Supervised Learning: These algorithms are typically applied in supervised learning scenarios, where labeled training data is used to build models for making predictions on new, unseen data.
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- Probabilistic Model: Naive Bayes is a probabilistic algorithm based on Bayes' theorem. It calculates probabilities of different classes for a given set of features.
- Assumption of Independence: It assumes that the features (words in NLP) are conditionally independent, which is a simplifying but often unrealistic assumption.
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| Support Vector Machines (SVM) |
- Margin Maximization: SVM aims to find the hyperplane that maximizes the margin between classes, making it effective for binary classification tasks.
- Kernel Trick: SVM can use kernel functions to transform data into higher-dimensional spaces, which can be useful when dealing with non-linearly separable data.
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| Decision Trees |
- Tree-Based Structure: Decision trees create a tree-like structure to make decisions based on the input features. They are interpretable and can be visualized.
- Prone to Overfitting: Decision trees can be prone to overfitting, which can be mitigated with techniques like pruning.
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| Random Forests |
- Ensemble Method: Random Forests are an ensemble of decision trees. They combine the predictions of multiple trees to improve accuracy and reduce overfitting.
- Robustness: Random Forests are more robust and less prone to overfitting compared to individual decision trees.
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| Convolutional Neural Networks (CNNs) |
- Image and Sequence Processing: CNNs are primarily designed for image data but can also be used for NLP tasks, particularly when dealing with sequences of data.
- Hierarchical Feature Learning: CNNs are capable of automatically learning hierarchical features, which can be useful for tasks like text classification or sentiment analysis.
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| Recurrent Neural Networks (RNNs) |
- Sequence Modeling: RNNs are designed for sequential data, making them particularly suitable for tasks like language modeling, machine translation, and sentiment analysis where the order of words is important.
- Long-Term Dependencies: RNNs can capture long-term dependencies in sequences, but they are susceptible to vanishing and exploding gradient problems.
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