Wolfram Natural Language Understanding or salvation for students
Wolfram is cool. It’s impossible to count how many schoolchildren received an “A” because of him, and how many students failed…
Everything is arranged simply: a bad student uploads a problem and gets a pleasant result with a good grade. All tasks are calculated algorithmically.
At least copy the physics lab…
Therefore, the main mystery of this service is the translation of unprepared student information into a data version that is digestible for algorithms.
The language model (NLU) is the answer.
What is NLU in its most general form?
Natural Language Understanding (NLU) in the Wolfram system is an architecture that combines symbolic methods with NLP. And here we need to emphasize. NLU is not about statistical methods that can constantly make mistakes. Accuracy of interpretation and translation into a digestible form is the most important thing in architecture.
“Complex linguistics, not statistics.”
Symbolic methods rely on the use of formal grammars, syntactic rules, and logical computations to extract semantic information.
Symbolic processing also includes the construction of dependency trees and the use of “ontologies” to deal with formal definitions of objects and their relationships.
This is not a classic transformer NLP with distributed weights, where in general there is no “curation” of meaning or context, whatever these words mean.
Semantics, meaning and sense, a constant understanding of what is happening “in notebooks” – this is the basis of Wolfram.
We can insert intermediate records of our decisions into the stream and get NLU’s “understanding” about the situation and the meaning of the input data.
Question-answering systems use NLU to process natural language queries, extract information from a knowledge base, and generate relevant answers. It's simple. The ChatGPT level is in action, but this is not usually what students or schoolchildren come to Wolfram for.
To process multi-layered or contextually rich texts, Wolfram uses inference and deep semantics techniques.
These mechanisms allow the system to go beyond simple data retrieval and move to reasoning about logical connections between entities, event chains and relationships.
Semantic interpretation here is not only about the analysis of explicit data, but also of latent dependencies that may be important for a complete understanding of the request. For example, Wolfram can work with scientific texts, analyze equations and formulas, structure information and produce meaningful answers taking into account all layers of context.
Tungsten works on all floors of the entered data.
Synthesis of symbolic and attention mechanisms:
The integration of machine learning and symbolic processing models into Wolfram NLU is built on the fundamental idea that these approaches are complementary, with each solving problems for which the other is ineffective or inapplicable.
Symbolic processing operates with deterministic rules, formal grammars and ontologies, which allows one to build rigorous logical inferences and provide text interpretation based on predefined data structures.
This methodologically involves the use of dependency graphs, syntax trees, and context-free grammars that accurately model syntactic and semantic relationships in sentences.
In turn, machine learning models based on probabilistic approaches provide adaptability and the ability to learn from data, identifying patterns that defy deterministic analysis.
Integrating these two approaches requires a complex architecture where both methods work closely together at different stages of processing. Symbolic processing is used to build an initial structural interpretation of the text: parsing sentences, identifying key entities, determining their relationships and dependencies.
These structures are formed on the basis of strictly defined rules, which work effectively in cases where syntactic and semantic relationships can be formally defined. At the same time, machine learning models such as transformers or neural networks come into play at stages where a more flexible approach is required.
They allow you to take into account contextual dependencies at the level of the entire text, work with ambiguous terms and process variable formulations that cannot be described through fixed symbolic rules.
The mechanism of integration is that the first stage creates a formal structure that ensures the basic coherence of the text in terms of grammar and syntax. Next, machine learning models use this structure as the main framework on which probabilistic estimates are superimposed.
These models are able to identify latent dependencies in data that symbolic rules cannot capture, such as contextual polysemy, distortions caused by incomplete or incorrect formulations, and other complexities that arise in natural language.
The result of integration is the construction of a model that combines strict rules of syntactic and semantic logic with probabilistic estimates, which significantly improves the quality of predictions in conditions of uncertainty and data complexity.
Thus, functionality overlaps: symbolic rules create a rigid structure, and statistical models correct it based on probabilistic dependencies.
As for multi-modal approaches in Wolfram NLU, their value extends beyond working with purely textual queries and includes the analysis of data from various sources: text, graphics, numbers, time and space.
This data is entered into the system in the form of various modalities, each of which represents a separate feature space with its own structure and specificity.
The essence of the multimodal approach is to combine these different sources of information into a single semantic space where complex connections and dependencies can be modeled. For example, textual data can be enhanced with numerical or graphical data such as time series or images, greatly improving the contextualization and understanding of the query.
Wolfram NLU uses multi-level representations to handle different modalities. At the stage of preprocessing and data presentation, each modality is transformed into a vector space of features, where their subsequent combination is performed.
Next, a semantic map is built, where each element of the request, be it text, a graphic image or a numerical sequence, receives its vector representation and is connected with other elements through multi-layer neural networks.
This process requires the use of latent encoding techniques, such as autoencoders or variational autoencoders, which allow information to be “compressed” and hidden dependencies between different modalities to be revealed.
Multimodal approaches handle queries that involve heterogeneous data and integrate them into the context of analysis.
Knowledge representation models: from symbols to vector representations
The disambiguation methods that Wolfram uses to deal with ambiguous words and contexts rely on several key techniques, including context-sensitive models, Bayesian methods, and inference rules.
When processing polysemantic words, the system is faced with the need to determine in which of the possible meanings the word is used.
To do this, Wolfram uses a hybrid approach, combining probabilistic models that predict the meaning of a word based on data, and symbolic methods that provide deterministic interpretation based on predefined grammatical and semantic rules. This is where transformers come into play, of course.
These models are capable of constructing word embeddings (vector representations), which depend on the context of their use. For example, the word “bank” in the sentence “I sat on the river bank” and in the sentence “I went to the bank” will have different contextual representations in vector space due to attention mechanisms.
This allows the system to classify the meaning of tokens depending on the immediate context and even take into account more distant dependencies.
For more complex cases of disambiguation, Wolfram also uses Bayesian methods, which build probability models based on posterior distributions.
Each word is considered as a random variable, and its possible values are considered as events with certain probabilities.
The model takes into account the probabilities of each meaning depending on the context, creating a dynamic system for predicting the meaning of a word. For example, if the text mentions “bank” along with the terms “loan”, “money” and “account”, the system will make an outline based on contextual probabilities.
Semantic memory, in the context of Wolfram NLU, is a framework in which knowledge is organized into hierarchical models that enable contextual and multi-layered understanding of natural language.
These knowledge models are based on ontologies and semantic networks, where entities and their connections are formalized in the form of nodes and edges. Allows Wolfram not only to analyze text at the level of superficial syntactic structures, but also to dive deeply into meaning, extracting hidden dependencies between concepts.
Hierarchical knowledge models built in the context of semantic memory organize data in the form of multi-level structures.
The top level represents the most general concepts and relationships, while the lower levels contain more detailed and specific knowledge.
For example, when working with the concept “animal,” the top level might represent the general category “animals,” followed by narrower classes such as “mammals” or “birds,” and at an even lower level there are specific species: “cat.” or “falcon”. The main task is to build
One of the key mechanisms that Wolfram uses to represent semantic information is embedding technology, in particular Word2Vec and its extended versions.
Word2Vec is a method for distributed representation of words as vectors in a high-dimensional space, where words that have similar context are located closer to each other.
To build such representations, an architecture based on two methods is used: Continuous Bag of Words (CBOW) and Skip-Gram. In CBOW, the task is to predict the central word based on the context (i.e., surrounding words), while in Skip-Gram it is the other way around: predicting surrounding words based on the central word.
For example, for the sentence “the cat is sitting on the carpet”, in Word2Vec the system will analyze which words are most often found near the word “cat”, such as “animal”, “pet” or “mammal”. This allows Word2Vec to build distributed representations of words that generalize their meanings depending on the context of use.
However, for deep representation of complex semantic dependencies and multi-layered contexts, Word2Vec alone may not be enough.
Wolfram uses advanced embeddings, such as contextualized transformer-based models (such as BERT), which allow you to take into account not only the immediate context, but also more distant relationships in a sentence or even a document.
Advanced embeddings are also used to build more complex semantic relationships between entities.
For example, the system can use contextualized embeddings to analyze sentences containing complex metaphors or implicit semantic dependencies.
For example, in the sentence “The wind shouted through the forest,” the system could use contextualized embeddings to recognize that the word “shouted” is a metaphor rather than a literal action.
Dependency trees and context-free grammars
Using dependency trees and context-free grammars (CFGs) for parsing in Wolfram plays a key role in natural language processing and understanding.
These approaches provide the system with formal structures for parsing complex sentences and constructing syntactic relationships, which is necessary for accurately interpreting the meaning of user queries.
Dependency trees are graph structures where nodes represent words in a sentence and edges show syntactic dependencies between those words. An important advantage of dependency trees is their ability to model grammatical relationships between words without depending on the order of words in a sentence.
This is especially important for languages with flexible word order, such as Russian, where syntax is highly dependent on context. In a dependency tree, each word is related to another through grammatical connections such as subject, object, or modifier.
For example, in the sentence “The cat catches the mouse,” the dependence between “catches” and “cat” (subject) and between “catches” and “mouse” (object) is clearly expressed through the edges of the tree. This helps Wolfram analyze sentence structure and extract entities such as subject, predicate and object.
Wolfram uses dependency trees to identify key elements in a query based on grammatical dependencies and build a structural representation of the sentence.
In more complex queries, such as “What is the level of CO2 in the Earth's atmosphere in 2020?”, the system builds a dependency tree where “level” is the main entity, and “CO2”, “Earth's atmosphere” and “2020” are attributes. modifying the essence. This allows the system to structure the request in such a way as to associate key entities and their characteristics for further processing.
Context-free grammars are formal grammar systems that describe the structure of sentences using a set of rules, where each rule recursively decomposes a phrase into its constituent parts.
In CFG, each sentence can be described as a sequence of syntactic categories (e.g., clauses, phrases, and phrases) that unfold according to predefined rules.
These grammars are effective for formalizing the syntax of a language because they allow many sentences to be described through a small number of grammatical rules.
Wolfram uses CFG to parse and break complex queries into components.
For example, for the question “Find the average temperature in Moscow in July 2020,” CFG will decompose the sentence into a main phrase (verb and object), subphrases (place, time) and individual modifiers (month, year). This allows the system to structurally identify that “average temperature” is the base entity, “Moscow” is the location, and “July 2020” is the time modifier.
Wolfram can then use this information to retrieve data from the knowledge base or perform calculations based on known data.
Structural analysis of complex queries requires the interaction of these two methods. Dependency trees provide an intuitive, graph-based representation of grammatical dependencies between words, which is important for accurately identifying key entities and their attributes.
At the same time, CFG sets strict rules for sentence expansion, which allows it to support a formal approach to parsing. Wolfram uses these techniques in tandem to create multi-layered parsing and semantic analysis that allows the system to identify entities, relationships, and events in queries.
When the system encounters a multi-layer query, such as “What is the probability of rain in Paris next week?”, it uses dependency trees to identify key entities such as “probability”, “rain”, “Paris”, and associates them with a time modifier “next week”.
CFG, in turn, helps break down that sentence into grammatical components, identifying major and minor phrases and helping to build the right structure for further semantic analysis and query execution.
Inference: How Wolfram solves problems using NLU
Wolfram's inference principles are based on the integration of reasoning engines with NLP modules, which allows the system not only to understand natural language queries, but also to carry out calculations based on strict logical and mathematical principles.
Inference in this context is the process of deriving new facts and conclusions based on known data and rules. It allows the system to not just answer questions directly, but to analyze, interpret and generate answers based on deep reasoning and modeling.
When a user enters a complex query that requires reasoning, the Wolfram system first processes the text query through an NLP engine, converting natural language into formal data structures.
This is done using syntactic and semantic analysis. Syntactic analysis is responsible for parsing the grammatical structure of a sentence, identifying key entities and relationships, and semantic analysis is responsible for understanding the meaning of these entities in context. After this, logical inference comes into play.
Integration of reasoning engines with NLP in the Wolfram system is carried out through the construction of connections between natural language constructs and mathematically expressed rules.
Inference mechanisms include not only deductive and inductive methods, but also heuristic and probabilistic approaches that allow the system to analyze data even when the information is incomplete or the query is ambiguous.
For example, when processing the query “What is the probability that it will rain in Paris tomorrow?”, the system uses statistical weather data and synoptic models, integrating them with probabilistic analysis rules, to generate an answer based on inference from the available data.
For more complex queries, Wolfram uses deep semantics techniques, which allow the system to understand not only the surface meanings of words, but also the hidden semantic relationships between them.
Deep semantics is based on the analysis of multi-valued, multi-layered concepts and the construction of multi-layered networks of meaning. This is especially important for complex scientific or technical queries where a precise understanding of terms and their relationships is required.
For example, in the query “What is the solution to the Schrödinger equation for a particle in a potential well?” the system must recognize the “Schrödinger equation” as a complex scientific entity, and then apply logical inference and symbolic calculations to find a solution to the equation in the specific context of the “potential well”.
To do this, Wolfram uses semantic networks that link mathematical and physical terms with algorithms and computational procedures.
An example of how the inference mechanism works can be seen in a query that involves complex mathematical and scientific problems, such as “How to calculate the area of a figure given by parametric equations?” or “What is the energy of the electron in the third energy level of the hydrogen atom?”
First, the NLP module identifies key mathematical entities: “area”, “parametric equations”, “energy”, “electron”, “hydrogen atom”.
These entities are then passed to a reasoning engine, which, based on the rules of calculus or quantum mechanics, uses symbolic computation to solve the problem.
For the example query “What is the energy of the electron in the third energy level of the hydrogen atom?” the system begins by applying the energy formula for the electron in a hydrogen-like atom. After the system recognizes the entity “third energy level”, the reasoning engine automatically substitutes n=3 into the equation and performs calculations, returning the result.
In this case, Wolfram uses a combination of symbolic computation, inference, and numerical methods to generate the answer.
This is a simplified model of the functioning of the Wolfram Language and, in general, their unique Alpha ecosystem.
It is important to emphasize that the complexity of designing their system lies in the need to relate the knowledge base, symbolic patterns, quantum mechanics and mathematical analysis to unstructured and simple human requests.
And according to some students’ reviews, it does a good job)
