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natural language understanding (nlu) is an ai-powered technology that allows machines to understand the structure and meaning of human languages. nlu, like natural language generation (nlg), is a subset of natural language processing (nlp) that focuses on assigning structure, rules, and logic to human language so machines can understand the intended meaning of words, phrases, and sentences in text. nlg, on the other hand, deals with generating realistic written/spoken human-understandable information from structured and unstructured data. since the development of nlu is based on theoretical linguistics, the process can be explained in terms of the following linguistic levels of language comprehension. linguistic levels in nlu phonology is the study of sound patterns in different languages/dialects, and in nlu it refers to the analysis of how sounds are organized, and their purpose and behavior. lexical or morphological analysis is the study of morphemes, indivisible basic units of language with their own meaning, one at a time. indivisible words with their own meaning, or lexical morphemes (e.g.: work) can be combined with plural morphemes (e.g.: works) or grammatical morphemes (e.g.: worked/working) to create word forms. lexical analysis identifies relationships between morphemes and converts words into their root form. syntactic analysis, or syntax analysis, is the process of applying grammatical rules to word clusters and organizing them on the basis of their syntactic relationships in order to determine meaning. this also involves detecting grammatical errors in sentences. while syntactic analysis involves extracting meaning from the grammatical syntax of a sentence, semantic analysis looks at the context and purpose of the text. it helps capture the true meaning of a piece of text by identifying text elements as well as their grammatical role. discourse analysis expands the focus from sentence-length units to look at the relationships between sentences and their impact on overall meaning. discourse refers to coherent groups of sentences that contribute to the topic under discussion. pragmatic analysis deals with aspects of meaning not reflected in syntactic or semantic relationships. here the focus is on identifying intended meaning readers by analyzing literal and non-literal components against the context of background knowledge. common tasks/techniques in nlu there are several techniques that are used in the processing and understanding of human language. here’s a quick run-through of some of the key techniques used in nlu and nlp. tokenization is the process of breaking down a string of text into smaller units called tokens. for instance, a text document could be tokenized into sentences, phrases, words, subwords, and characters. this is a critical preprocessing task that converts unstructured text into numerical data for further analysis. stemming and lemmatization are two different approaches with the same objective: to reduce a particular word to its root word. in stemming, characters are removed from the end of a word to arrive at the “stem” of that word. algorithms determine the number of characters to be eliminated for different words even though they do not explicitly know the meaning of those words. lemmatization is a more sophisticated approach that uses complex morphological analysis to arrive at the root word, or lemma. parsing is the process of extracting the syntactic information of a sentence based on the rules of formal grammar. based on the type of grammar applied, the process can be classified broadly into constituency and dependency parsing. constituency parsing, based on context-free grammar, involves dividing a sentence into sub-phrases, or constituents, that belong to a specific grammar category, such as noun phrases or verb phrases. dependency parsing defines the syntax of a sentence not in terms of constituents but in terms of the dependencies between the words in a sentence. the relationship between words is depicted as a dependency tree where words are represented as nodes and the dependencies between them as edges. part-of-speech (pos) tagging, or grammatical tagging, is the process of assigning a grammatical classification, like noun, verb, adjective, etc., to words in a sentence. automatic tagging can be broadly classified as rule-based, transformation-based, and stochastic pos tagging. rule-based tagging uses a dictionary, as well as a small set of rules derived from the formal syntax of the language, to assign pos. transformation-based tagging, or brill tagging, leverages transformation-based learning for automatic tagging. stochastic refers to any model that uses frequency or probability, e.g. word frequency or tag sequence probability, for automatic pos tagging. name entity recognition (ner) is an nlp subtask that is used to detect, extract and categorize named entities, including names, organizations, locations, themes, topics, monetary, etc., from large volumes of unstructured data. there are several approaches to ner, including rule-based systems, statistical models, dictionary-based systems, ml-based systems, and hybrid models. these are just a few examples of some of the most common techniques used in nlu. there are several other techniques like, for instance, word sense disambiguation, semantic role labeling, and semantic parsing that focus on different levels of semantic abstraction, nlp/nlu in biomedical research nlp/nlu technologies represent a strategic fit for biomedical research with its vast volumes of unstructured data — 3,000-5,000 papers published each day, clinical text data from ehrs, diagnostic reports, medical notes, lab data, etc., and non-standardized digital real-world data. nlp-enabled text mining has emerged as an effective and scalable solution for extracting biomedical entity relations from vast volumes of scientific literature. techniques, like named entity recognition (ner), are widely used in relation extraction tasks in biomedical research with conventionally named entities, such as names, organizations, locations, etc., substituted with gene sequences, proteins, biological processes, and pathways, drug targets, etc. the unique vocabulary of biomedical research has necessitated the development of specialized, domain-specific bionlp frameworks. at the same time, the capabilities of nlu algorithms have been extended to the language of proteins and that of chemistry and biology itself. a 2021 article detailed the conceptual similarities between proteins and language that make them ideal for nlp analysis. more recently, an nlp model was trained to correlate amino acid sequences from the uniprot database with english language words, phrases, and sentences used to describe protein function to annotate over 40 million proteins. researchers have also developed an interpretable and generalizable drug-target interaction model inspired by sentence classification techniques to extract relational information from drug-target biochemical sentences. large neural language models and transformer-based language models are opening up transformative opportunities for biomedical nlp applications across a range of bioinformatics fields including sequence analysis, genome analysis, multi-omics, spatial transcriptomics, and drug discovery. most importantly, nlp technologies have helped unlock the latent value in huge volumes of unstructured data to enable more integrative, systems-level biomedical research. read more about nlp’s critical role in facilitating systems biology and ai-powered data-driven drug discovery. if you want more information on seamlessly integrating advanced bionlp frameworks into your research pipeline, please drop us a line here.

today artificial intelligence (ai), machine learning (ml), deep learning (dl) and natural language processing (nlp) are all technologies that have become a part of the fabric of enterprise it. however, solutions providers and end-users often use these terms interchangeably. even though there can be significant conceptual overlaps, there are also important distinctions between these key technologies. increasingly, the value of ai in drug discovery is determined not by model complexity alone, but by how well biological context is preserved across data, computation, and experimentation. platforms such as mindwalk reflect this shift—prioritizing biological fidelity, traceability, and integration with experimental workflows so that computational insight remains actionable as discovery programs scale. here’s a quick overview of the definition and scope of each of these terms. artificial intelligence (ai) the term ai has been around since the 1950s and broadly refers to the simulation of human intelligence by machines. it encompasses several areas beyond computer science including psychology, philosophy, linguistics and others. ai can be classified into four types, from simplest to most advanced, as reactive machines, limited memory, theory of mind and self-awareness. reactive machines: purely reactive machines are trained to perform a basic set of tasks based on certain inputs. this ai cannot function beyond a specific context and is not capable of learning or evolving over time. examples: ibm’s deep blue chess ai, and google’s alphago ai. limited memory systems: as the nomenclature suggests, these ai systems have limited memory to store and analyze data. this memory is what enables “learning” and gives them the capability to improve over time. in practical terms, these are the most advanced ai systems we currently have. examples: self-driving vehicles, virtual voice assistants, chatbots. theory of mind: at this level, we are already into theoretical concepts that have not yet been achieved yet. with their ability to understand human thoughts and emotions, these advanced ai systems can facilitate more complex two-way interactions with users. self-awareness: self-aware ais with human-level desires, emotions and consciousness is the aspirational end state for ai and, as yet, are pure science fiction. another broad approach to distinguishing between ai systems is in terms of narrow or weak ai, specialized intelligence trained to perform specific tasks better than humans, general artificial intelligence (agi) or strong ai, a theoretical system that could be applied to any task or problem, and artificial super intelligence (asi), ai that comprehensively surpasses human cognition. the concept of ai is continuously evolving based on the emergence of technologies that enable the most accurate simulation of human intelligence. some of those technologies include ml, dl, and artificial neural networks (ann) or simply neural networks (nn). ml, dl, rl, and drl here’s the tl;dr before we get into each of these concepts in a bit more detail: if ai’s objective is to endow machines with human intelligence, ml refers to methods for implementing ai by using algorithms for data-driven learning and decision-making. dl is a technology for realizing ml and expanding the scope of ai. reinforcement learning (rl), or evaluation learning, is an ml technique. and deep reinforcement learning (drl) combines dl and rl to realize optimization objectives and advance toward general ai. source: researchgate machine learning (ml) ml is a subset of ai that involves the implementation of algorithms and neural networks to give machines the ability to learn from experience and act automatically. ml algorithms can be broadly classified into three categories. supervised learning ml algorithms using a labelled input dataset and known responses to develop a regression/classification model that can then be used on new datasets to generate predictions or draw conclusions. the limitation of this approach is that it is not viable for datasets that are beyond a certain context. unsupervised learning algorithms are subjected to “unknown” data that has yet to be categorized or labelled. in this case, the ml system itself learns to classify and process unlabeled data to learn from its inherent structure. there is also an intermediate approach between supervised and unsupervised learning, called semi-supervised learning, where the system is trained based on a small amount of labelled data to determine correlations between data points. reinforcement learning (rl) is an ml paradigm where algorithms learn through ongoing interactions between an ai system and its environment. algorithms receive numerical scores as rewards for generating decisions and outcomes so that positive interactions and behaviours are reinforced over time. deep learning (dl) dl is a subset of ml where models built on deep neural networks work with unlabeled data to detect patterns with minimal human involvement. dl technologies are based on the theory of mind type of ai where the idea is to simulate the human brain by using neural networks to teach models to perceive, classify, and analyze information and continuously learn from these interactions. dl techniques can be classified into three major categories: deep networks for supervised or discriminative learning, deep networks for unsupervised or generative learning, and deep networks for hybrid learning that is an integration of both supervised and unsupervised models and relevant others. deep reinforcement learning (drl) combines rl with dl techniques to solve challenging sequential decision-making problems. because of its ability to learn different levels of abstractions from data, drl is capable of addressing more complicated tasks. natural language processing (nlp) what is natural language processing? nlp is the branch of ai that deals with the training of machines to understand, process, and generate language. by enabling machines to process human languages, nlp helps streamline information exchange between human beings and machines and opens up new avenues by which ai algorithms can receive data. nlp functionality is derived from cross-disciplinary theories from linguistics, ai and computer science. there are two main types of nlp algorithms, rules-based and ml-based. rules-based systems use carefully designed linguistic rules whereas ml-based systems use statistical methods. nlp also consists of two core subsets, natural language understanding (nlu) and natural language generation (nlg). nlu enables computers to comprehend human languages and communicate back to humans in their own languages. nlg is the use of ai programming to mine large quantities of numerical data, identify patterns and share that information as written or spoken narratives that are easier for humans to understand. comparing rules-based and deep learning nlp approaches natural language processing (nlp) systems generally fall into two broad categories: rules-based and deep learning-based. rules-based systems rely on expert-defined heuristics and pattern matching, offering transparency and interpretability. however, they tend to be brittle and limited in scalability across biomedical domains. in contrast, deep learning models—including transformers like biobert and scispacy—automatically learn contextual relationships from large biomedical corpora. these models serve as powerful biomedical text mining tools, offering greater flexibility and accuracy in processing complex, ambiguous language found in clinical narratives, scientific publications, and electronic health records (ehrs). many life sciences applications now favor hybrid pipelines that combine the precision of rule-based systems with the adaptability of deep learning—balancing interpretability and performance in production settings. conclusion this overview outlines the key technological acronyms shaping today’s discussions around ai-driven drug discovery. you can also explore how ai/ml technologies are are advancing intelligent bioinformatics and autonomous drug discovery and the importance and challenges of nlp in biomedical research. curious about nlp? dive deeper into our article for further exploration.