![]() We will look at this in more detail in an upcoming blog post. Part-of-speech tagging forms the backbone of NLP engines (like automated support), translation apps, sentiment analysis, and a lot more. This application is most prevalent in IoT devices such as Amazon Alexa. ![]() Part-of-speech tagging is essential whenever you want to automatically analyze human speech data. This is a major problem in sentiment analysis of online chat rooms, since text is non-standard (with elements of sarcasm, hyperbole, informal speech and syntax errors), which can easily mislead even the best NLP algorithms. Compare language used on Twitter or in chat rooms against that in legal documents, for example. The ability to adapt to new domains is made essential by the fact that language is heavily context dependent. It may also require significantly larger datasets, as statistical models need to be able to generalize. ![]() But the quality of the models relies heavily on the quality of the labels in the training data. ![]() They are also more adaptable and transferable between domains. This makes them easier to maintain and faster to train. Statistical models learn the tagging “rules” automatically. The program isn’t really able to account for outliers since it is strictly rule-based, so if an unfamiliar word sequence is thrown into the mix, it can be tagged incorrectly. Additionally, the continual evolution of languages means that rules are likely to become outdated. There can be hundreds if not thousands of rules when it comes to classifying sentences in the English language, and the creator of the program will invariably miss a few. The problem here is in creating the rules themselves. Based on a list of tagging rules, the algorithm will tag each word within its classification. Rule-based tagging is interesting since it doesn’t leave that much room for error. This approach includes hidden Markov model, conditional random field, (deep) neural network models, and/or a combination of these. Statistical model: a statistical approach of learning to tag based on a labeled dataset.Rule-based: feeding in a preset list of rules for the algorithm to follow.Here are a few of the tagging techniques you can use: This entirely depends on your use case and the type of algorithm you’re trying to design. How does part-of-speech tagging work?Īs you can imagine, there is no hard and fast rule determining how speech is to be tagged. This is why it’s extremely important for each word to be tagged correctly and looked at contextually. If this sentence is sent to a customer support bot, it may be construed as positive feedback (looking at the words “great” and “help”), and a sentiment analysis bot may tag this sentence as positive feedback. However, a computer algorithm may very well simply classify the word “great” as positive, thus leading to an incorrect response. If the sentence is a part of some sort of opinion or feedback, it would convey a negative sentiment. In a support scenario, this would mean that a customer needs urgent help. “I require a great deal of help.” This is a plea for assistance to any service representative who reads it. It’s very easy for a customer service representative to look at a sentence and understand what it means, but the same cannot be said of a computer. If an algorithm is to sort words into part-of-speech categories, things are a little more complex than they seem at first, because the algorithm would need to perform some sort of contextual analysis as well. There are many such words that can only be classified in their respective categories if we are given additional context. For example, you can “play” (verb) a part in a “play” (noun). The tricky part here is that all words don’t necessarily fall neatly into a single category. Adjectives describe nouns, such as happy, beautiful, blue, or big.Adverbs: describe verbs, such as heavily, gingerly, swiftly, and gracefully.Common conjunctions include because, and, however and moreover. Prepositions: a position in space or time or used to introduce an object, for example, like under, near, during, of, with.Nouns: names for places, people, ideas, or things.Verbs: actions, for example, going, jumping, running, or being.Common examples include I, you, she, they, and it. Pronouns: words that stand in place of noun phrases.Parts of speech commonly include the following: Native speakers possess an intuitive knowledge of parts of speech (often without an awareness of the underlying technicalities), but a computer needs training to carry out part-of-speech tagging. In order to effectively analyze language, computers have to first understand these constituent parts of speech. Part-of-speech tagging is the process of breaking a language down into key categories on a word-by-word basis.
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