SEC B Nlp Quiz 1

The given statement is true because natural language understanding involves the process of mapping input in natural language into meaningful representations. This process includes tasks such as parsing, semantic analysis, and discourse processing, which all contribute to understanding and extracting meaning from human language.

Stemming is the process for reducing inflected words to their root form in linguistic morphology. Stemming helps in simplifying words by removing prefixes, suffixes, and other inflections to identify the base or root form of a word. This is commonly used in natural language processing tasks such as information retrieval, text mining, and search engines to improve the accuracy and efficiency of text analysis. Stemming algorithms vary in complexity and can be language-specific to handle different linguistic rules and patterns.

The word "unhappily" can be broken down into three sub-word tokens: "un", "happy", and "ly". Each of these tokens carries meaning and cannot be further broken down into smaller meaningful units. Therefore, these tokens are morphemes, which are the smallest units of meaning in a language. Phonemes, on the other hand, are the smallest units of sound in a language, and "unhappily" consists of more than three phonemes. Therefore, the correct answer is morpheme.

Lemmatization is a technique used for keyword normalization where a keyword is converted into its base form by reducing it to its root word. This helps in grouping together words with the same meaning. Stemming is another technique used for keyword normalization where the suffix of a word is removed to obtain its base form. Both lemmatization and stemming are effective methods for converting keywords into their base form, making options A and C the correct answer.

Hidden Markov Model (HMM) is a probabilistic based method because it uses probability distributions to model the transitions between hidden states and the observations emitted from these states. HMMs assume that the system being modeled is a Markov process, where the probability of transitioning from one state to another depends only on the current state. By using probability calculations, HMMs can predict the most likely sequence of hidden states given a sequence of observations. This probabilistic nature of HMMs makes them suitable for various applications such as speech recognition, natural language processing, and bioinformatics.

Word Sense Disambiguation is the process of determining the correct meaning of a word based on the context in which it is used. It involves analyzing the surrounding words and phrases to identify the most appropriate meaning. This technique is used when words have multiple meanings and helps in ensuring accurate understanding and interpretation of text. Fuzzy Logic and Shallow Semantic Analysis are related concepts but not specifically used for resolving multiple word meanings. Therefore, the correct answer is Word Sense Disambiguation.

The given correct answer is "Rule based tagging but the rules are learned from a corpus". This means that the tagging is done based on predefined rules, but these rules are derived from a corpus of labeled data. This approach allows the system to learn patterns and relationships between words and their tags from the training data, enabling it to make accurate predictions on unseen data.

A bound morpheme is a morpheme that cannot stand alone as a separate word and must be attached to another word to convey meaning. Therefore, it cannot occur as a separate meaningful word.

Pragmatic Analysis is the study of language by considering the semantic context in which it is used. This means that it focuses on understanding the meaning and interpretation of language based on the context in which it is used. By analyzing the semantic context, researchers can gain insights into how language is used to convey meaning, intentions, and social interactions. This approach helps to uncover the pragmatic aspects of language, such as implicatures, presuppositions, and speech acts, which go beyond the literal meaning of words and sentences.

The given statement is false. A noun is not a closed class of PoS tagger. In linguistics, a closed class refers to a category of words that has a fixed number of members and does not easily accept new additions. Examples of closed classes include pronouns, prepositions, and conjunctions. However, nouns are not limited in number and new nouns can be added to a language over time. Therefore, nouns are considered an open class of words.