Understanding Semantic Analysis Using Python - NLP

The first is lexical semantics, the study of the meaning of individual words and their relationships. This stage entails obtaining the dictionary definition of the words in the text, parsing each word/element to determine individual functions and properties, and designating a grammatical role for each. Key aspects of lexical semantics include identifying word senses, synonyms, antonyms, hyponyms, hypernyms, and morphology.

Healthcare professionals can develop more efficient workflows with the help of natural language processing. During procedures, doctors can dictate their actions and notes to an app, which produces an accurate transcription. NLP can also scan patient documents to identify patients who would be best suited for certain clinical trials. Insurance companies can assess claims with natural language processing since this technology can handle both structured and unstructured data. NLP can also be trained to pick out unusual information, allowing teams to spot fraudulent claims. While NLP-powered chatbots and callbots are most common in customer service contexts, companies have also relied on natural language processing to power virtual assistants.

This ends our Part-9 of the Blog Series on Natural Language Processing!

” is interpreted to “Asking for the current time” in semantic analysis whereas in pragmatic analysis, the same sentence may refer to “expressing resentment to someone who missed the due time” in pragmatic analysis. Thus, semantic analysis is the study of the relationship between various linguistic utterances and their meanings, but pragmatic analysis is the study of context which influences our understanding of linguistic expressions. Pragmatic analysis helps users to uncover the intended meaning of the text by applying contextual background knowledge. Compositionality in a frame language can be achieved by mapping the constituent types of syntax to the concepts, roles, and instances of a frame language.

AI has become an increasingly important tool in NLP as it allows us to create systems that can understand and interpret human language. By leveraging AI algorithms, computers are now able to analyze text and other data sources with far greater accuracy than ever before. Semantic analysis is an important subfield of linguistics, the systematic scientific investigation of the properties and characteristics of natural human language. What sets semantic analysis apart from other technologies is that it focuses more on how pieces of data work together instead of just focusing solely on the data as singular words strung together. Understanding the human context of words, phrases, and sentences gives your company the ability to build its database, allowing you to access more information and make informed decisions. In order to test whether network and identity play the hypothesized roles, we evaluate each model’s ability to reproduce just urban-urban pathways, just rural-rural pathways, and just urban-rural pathways.

Figure 5.1 shows a fragment of an ontology for defining a tendon, which is a type of tissue that connects a muscle to a bone. When the sentences describing a domain focus on the objects, the natural approach is to use a language that is specialized for this task, such as Description Logic[8] which is the formal basis for popular ontology tools, such as Protégé[9]. This information is determined by the noun phrases, the verb phrases, the overall sentence, and the general context.

In recent years there has been a lot of progress in the field of NLP due to advancements in computer hardware capabilities as well as research into new algorithms for better understanding human language. The increasing popularity of deep learning models has made NLP even more powerful than before by allowing computers to learn patterns from large datasets without relying on predetermined rules or labels. Natural language processing (NLP) is a form of artificial intelligence that deals with understanding and manipulating human language. It is used in many different ways, such as voice recognition software, automated customer service agents, and machine translation systems. NLP algorithms are designed to analyze text or speech and produce meaningful output from it. Semantic analysis is the process of interpreting words within a given context so that their underlying meanings become clear.

In recent years, various methods have been proposed to automatically evaluate machine translation quality by comparing hypothesis translations with reference translations. The development of natural language processing technology has enabled developers to build applications that can interact with humans much more naturally than ever before. These applications are taking advantage of advances in artificial intelligence (AI) technologies such as neural networks and deep learning models which allow them to understand complex sentences written by humans with ease. Natural language processing (NLP) is an area of computer science and artificial intelligence concerned with the interaction between computers and humans in natural language. It is the driving force behind things like virtual assistants, speech recognition, sentiment analysis, automatic text summarization, machine translation and much more. In this post, we’ll cover the basics of natural language processing, dive into some of its techniques and also learn how NLP has benefited from recent advances in deep learning.

Further, Natural Language Generation (NLG) is the process of producing phrases, sentences and paragraphs that are meaningful from an internal representation. The first objective of this paper is to give insights of the various important terminologies of NLP and NLG. Lexical semantics is not a solved problem for NLP and AI, as it poses many challenges and opportunities for research and development. Some of the challenges are ambiguity, variability, creativity, and evolution of language. Some of the opportunities are semantic representation, semantic similarity, semantic inference, and semantic evaluation. Lexical analysis is the process of identifying and categorizing lexical items in a text or speech.

These algorithms can be used to better identify relevant data points from text or audio sources, as well as more effectively parse natural language into its components (such as meaning, syntax and context). Additionally, such algorithms may also help reduce errors by detecting abnormal patterns in speech or text that could lead to incorrect interpretations. Hidden Markov Models are extensively used for speech recognition, where the output sequence is matched to the sequence of individual phonemes.

The Role of Knowledge Representation and Reasoning in Semantic Analysis

For the purposes of illustration, we will consider the mappings from phrase types to frame expressions provided by Graeme Hirst[30] who was the first to specify a correspondence between natural language constituents and the syntax of a frame language, FRAIL[31]. These mappings, like the ones described for mapping phrase constituents to a logic using lambda expressions, were inspired by Montague Semantics. Well-formed frame expressions include frame instances and frame statements (FS), where a FS consists of a frame determiner, a variable, and a frame descriptor that uses that variable. A frame descriptor is a frame symbol and variable along with zero or more slot-filler pairs. A slot-filler pair includes a slot symbol (like a role in Description Logic) and a slot filler which can either be the name of an attribute or a frame statement. The language supported only the storing and retrieving of simple frame descriptions without either a universal quantifier or generalized quantifiers.

It is a powerful application of semantic analysis that allows us to gauge the overall sentiment of a given piece of text. In this section, we will explore how sentiment analysis can be effectively performed using the TextBlob library in Python. By leveraging TextBlob’s intuitive interface and powerful sentiment analysis capabilities, we can gain valuable insights into the sentiment of textual content. Natural language processing (NLP) is the process of analyzing natural language in order to understand the meaning and intent behind it. Semantic analysis is one of the core components of NLP, as it helps computers understand human language.

• Fan et al. [41] introduced a gradient-based neural architecture search algorithm that automatically finds architecture with better performance than a transformer, conventional NMT models.
• An alternative is to express the rules as human-readable guidelines for annotation by people, have people create a corpus of annotated structures using an authoring tool, and then train classifiers to automatically select annotations for similar unlabeled data.
• Below is a parse tree for the sentence “The thief robbed the apartment.” Included is a description of the three different information types conveyed by the sentence.
• Some of the opportunities are semantic representation, semantic similarity, semantic inference, and semantic evaluation.

It is also essential for automated processing and question-answer systems like chatbots. Semantic analysis offers your business many benefits when it comes to utilizing artificial intelligence (AI). Semantic analysis aims to offer the best digital experience possible when interacting with technology as if it were human. This includes organizing information and eliminating repetitive information, which provides you and your business with more time to form new ideas. Thus, the ability of a machine to overcome the ambiguity involved in identifying the meaning of a word based on its usage and context is called Word Sense Disambiguation.

Syntax is the grammatical structure of the text, whereas semantics is the meaning being conveyed. A sentence that is syntactically correct, however, is not always semantically correct. For example, “cows flow supremely” is grammatically valid (subject — verb — adverb) but it doesn’t make any sense. As illustrated earlier, the word “ring” is ambiguous, as it can refer to both a piece of jewelry worn on the finger and the sound of a bell.

One example of how AI is being leveraged for NLP purposes is Google’s BERT algorithm which was released in 2018. BERT stands for “Bidirectional Encoder Representations from Transformers” and is a deep learning model designed specifically for understanding natural language queries. It uses neural networks to learn contextual relationships between words in a sentence or phrase so that it can better interpret user queries when they search using Google Search or ask questions using Google Assistant.

This type of model works by analyzing large amounts of text data and extracting important features from it. Unsupervised approaches are often used for tasks such as topic modeling, which involves grouping related documents together based on their content and theme. By leveraging this type of model, AI systems can better understand the relationship between different pieces of text even if they are written in different languages or contexts. Supervised machine learning techniques can be used to train NLP systems to recognize specific patterns in language and classify them accordingly.

If you’re interested in using some of these techniques with Python, take a look at the Jupyter Notebook about Python’s natural language toolkit (NLTK) that I created. You can also check out my blog post about building neural networks with Keras where I train a neural network to perform sentiment analysis. Relationship extraction takes the named entities of NER and tries to identify the semantic relationships between them. This could mean, for example, finding out who is married to whom, that a person works for a specific company and so on. This problem can also be transformed into a classification problem and a machine learning model can be trained for every relationship type.

How is Semantic Analysis different from Lexical Analysis?

The notion of a procedural semantics was first conceived to describe the compilation and execution of computer programs when programming was still new. Of course, there is a total lack of uniformity across implementations, as it depends on how the software application has been defined. Figure 5.6 shows two possible procedural semantics for the query, “Find all customers with last name of Smith.”, one as a database query in the Structured Query Language (SQL), and one implemented as a user-defined function in Python. Natural language processing brings together linguistics and algorithmic models to analyze written and spoken human language.

The easiest one I can think of is Random Indexing, which has been used extensively in NLP. I am interested to find the semantic relatedness of two words from a specific domain, i.e. “image quality” and “noise”. I am doing some research to determine if reviews of cameras are positive or negative for a particular attribute of the camera. The Conceptual Graph shown in Figure 5.18 shows how to capture a resolved ambiguity about the existence of “a sailor”, which might be in the real world, or possibly just one agent’s belief context. The graph and its CGIF equivalent express that it is in both Tom and Mary’s belief context, but not necessarily the real world.

By leveraging machine learning models – such as recurrent neural networks – along with KRR techniques, AI systems can better identify relationships between words, sentences and entire documents. Additionally, this approach helps reduce errors caused by ambiguities in natural language inputs since it takes context into account when interpreting user queries. In conclusion, semantic analysis is an essential component of natural language processing that has enabled significant advancement in AI-based applications over the past few decades. As its use continues to grow in complexity so too does its potential for solving real-world problems as well as providing insight into how machines can better understand human communication. Lexical semantics plays a vital role in NLP and AI, as it enables machines to understand and generate natural language. By applying the principles of lexical semantics, machines can perform tasks such as machine translation, information extraction, question answering, text summarization, natural language generation, and dialogue systems.

Thus, the cross-lingual framework allows for the interpretation of events, participants, locations, and time, as well as the relations between them. Output of these individual pipelines is intended to be used as input for a system that obtains event centric knowledge graphs. All modules take standard input, to do some annotation, and produce standard output which in turn becomes the input for the next module pipelines.

Stack Exchange network consists of 183 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. In this type, most of the previous techniques can be combined with word embeddings for better results because word embeddings capture the semantic relation between words. You can find out what a group of clustered words mean by doing principal component analysis (PCA) or dimensionality reduction with T-SNE, but this can sometimes be misleading because they oversimplify and leave a lot of information on the side. It’s a good way to get started (like logistic or linear regression in data science), but it isn’t cutting edge and it is possible to do it way better. Noun phrases are one or more words that contain a noun and maybe some descriptors, verbs or adverbs.

The type of behavior can be determined by whether there are “wh” words in the sentence or some other special syntax (such as a sentence that begins with either an auxiliary or untensed main verb). These three types of information are represented together, as expressions in a logic or some variant. With sentiment analysis we want to determine the attitude (i.e. the sentiment) of a speaker or writer with respect to a document, interaction or event.

Datasets in NLP and state-of-the-art models

KRR bridges the gap between the world of symbols, where humans communicate information, and the world of mathematical equations and algorithms used by machines to understand that information. If you’re interested in a career that involves semantic analysis, working as a natural language processing engineer is a good choice. Essentially, in this position, you would translate human language into a format a machine can understand. Empirical rural-rural pathways tend to be heavier when both network and identity pathways are heavy (high levels of strong-tie diffusion), and lightest when both network and identity pathways are light (low levels of weak-tie diffusion) (Fig. 4, dark blue bars).

The ultimate goal of natural language processing is to help computers understand language as well as we do. Natural language processing (NLP) is an increasingly important field of research and development, and a key component of many artificial intelligence projects. When it comes to NLP-based systems, there are several strategies that can be employed to improve accuracy. Event discovery in social media feeds (Benson et al.,2011) [13], using a graphical model to analyze any social media feeds to determine whether it contains the name of a person or name of a venue, place, time etc. Phonology is the part of Linguistics which refers to the systematic arrangement of sound. The term phonology comes from Ancient Greek in which the term phono means voice or sound and the suffix –logy refers to word or speech.

Pragmatic ambiguity occurs when different persons derive different interpretations of the text, depending on the context of the text. The context of a text may include the references of other sentences of the same document, which influence the understanding of the text and the background knowledge of the reader or speaker, which gives a meaning to the concepts expressed in that text. Semantic analysis focuses on literal meaning of the words, but pragmatic analysis focuses on the inferred meaning that the readers perceive based on their background knowledge.

The LSP-MLP helps enabling physicians to extract and summarize information of any signs or symptoms, drug dosage and response data with the aim of identifying possible side effects of any medicine while highlighting or flagging data items [114]. The National Library of Medicine is developing The Specialist System [78,79,80, 82, 84]. It is expected to function as an Information Extraction tool for Biomedical Knowledge Bases, particularly Medline abstracts. The lexicon was created using MeSH (Medical Subject Headings), Dorland’s Illustrated Medical Dictionary and general English Dictionaries.

The earpieces can also be used for streaming music, answering voice calls, and getting audio notifications. Overload of information is the real thing in this digital age, and already our reach and access to knowledge and information exceeds our capacity to understand it. This trend is not slowing down, so an ability to summarize the data while keeping the meaning intact is highly required. Since simple tokens may not represent the actual meaning of the text, it is advisable to use phrases such as “North Africa” as a single word instead of ‘North’ and ‘Africa’ separate words. Chunking known as “Shadow Parsing” labels parts of sentences with syntactic correlated keywords like Noun Phrase (NP) and Verb Phrase (VP). Various researchers (Sha and Pereira, 2003; McDonald et al., 2005; Sun et al., 2008) [83, 122, 130] used CoNLL test data for chunking and used features composed of words, POS tags, and tags.

IE systems should work at many levels, from word recognition to discourse analysis at the level of the complete document. An application of the Blank Slate Language Processor (BSLP) (Bondale et al., 1999) [16] approach for the analysis of a real-life natural language corpus that consists of responses to open-ended questionnaires in the field of advertising. This degree of language understanding can help companies automate even the most complex language-intensive processes and, in doing so, transform the way they do business.

• Semantics gives a deeper understanding of the text in sources such as a blog post, comments in a forum, documents, group chat applications, chatbots, etc.
• The National Library of Medicine is developing The Specialist System [78,79,80, 82, 84].
• If you really want to increase your employability, earning a master’s degree can help you acquire a job in this industry.
• It’s task was to implement a robust and multilingual system able to analyze/comprehend medical sentences, and to preserve a knowledge of free text into a language independent knowledge representation [107, 108].
• However, following the development

  of advanced neural network techniques, especially the Seq2Seq model,[17]

  and the availability of powerful computational resources, neural semantic parsing started emerging.

You can foun additiona information about ai customer service and artificial intelligence and NLP. The maps depict the strongest pathways between pairs of counties in the a Network + Identity model, b Network-only model, and c Identity-only model. Pathways are shaded by their strength (purple is more strong, orange is less strong); if one county has more than ten pathways in this set, just the ten strongest pathways out of that county are pictured. We evaluate whether models match the empirical (i) spatial distribution of each word’s usage and (ii) spatiotemporal pathways between pairs of counties. By default, every DL ontology contains the concept “Thing” as the globally superordinate concept, meaning that all concepts in the ontology are subclasses of “Thing”. [ALL x y] where x is a role and y is a concept, refers to the subset of all individuals x such that if the pair is in the role relation, then y is in the subset corresponding to the description. [EXISTS n x] where n is an integer is a role refers to the subset of individuals x where at least n pairs are in the role relation.

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NLP-powered apps can check for spelling errors, highlight unnecessary or misapplied grammar and even suggest simpler ways to organize sentences. Natural language processing can also translate text into other languages, aiding students in learning a new language. Keeping the advantages of natural language processing in mind, let’s explore how different industries are applying this technology.

One of the most significant recent trends has been the use of deep learning algorithms for language processing. Deep learning algorithms allow machines to learn from data without explicit programming instructions, making it possible for machines to understand language on a much more nuanced level than before. This has opened up exciting possibilities for natural language processing applications such as text summarization, sentiment analysis, machine translation and question answering. The processing methods for mapping raw text to a target representation will depend on the overall processing framework and the target representations. A basic approach is to write machine-readable rules that specify all the intended mappings explicitly and then create an algorithm for performing the mappings. An alternative is to express the rules as human-readable guidelines for annotation by people, have people create a corpus of annotated structures using an authoring tool, and then train classifiers to automatically select annotations for similar unlabeled data.

For example, these techniques can be used to teach a system how to distinguish between different types of words or detect sarcasm in text. With enough data, supervised machine learning models can learn complex concepts such as sentiment analysis and entity recognition with high accuracy levels. As most of the world is online, the task of making data accessible and available to all is a challenge. Machine Translation is generally translating phrases from one language to another with the help of a statistical engine like Google Translate. The challenge with machine translation technologies is not directly translating words but keeping the meaning of sentences intact along with grammar and tenses.

People will naturally express the same idea in many different ways and so it is useful to consider approaches that generalize more easily, which is one of the goals of a domain independent representation. Fourth, word sense discrimination determines what words senses are intended for tokens of a sentence. Discriminating among the possible senses of a word involves selecting a label from a given set (that is, a classification task). Alternatively, one can use a distributed representation of words, which are created using vectors of numerical values that are learned to accurately predict similarity and differences among words. The scientific community introduced this type in 2016 as a novel type of semantic similarity measurement between two English phrases, with the assumption that they are syntactically correct.

NLU enables machines to understand natural language and analyze it by extracting concepts, entities, emotion, keywords etc. It is used in customer care applications to understand the problems reported by customers either verbally or in writing. Linguistics is the science which involves the meaning of language, language context and various forms of the language. So, it is important to understand various important terminologies of NLP and different levels of NLP. Natural language processing (NLP) has recently gained much attention for representing and analyzing human language computationally.

I hope after reading that article you can understand the power of NLP in Artificial Intelligence. So, in this part of this series, we will start our discussion on Semantic analysis, which is a level of the NLP tasks, Chat GPT and see all the important terminologies or concepts in this analysis. Understanding these terms is crucial to NLP programs that seek to draw insight from textual information, extract information and provide data.

In order to appropriately model the diffusion of language18, adoption is usage-based (i.e., agents can use the word more than once and adoption is influenced by frequency of exposure)117 and the likelihood of adoption increases when there are multiple network neighbors using it118. Although we present a model for lexical adoption on Twitter, the cognitive and social processes on which our formalism is derived likely generalize well to other forms of cultural innovation and contexts63,119,120. Semantics, the study of meaning, is central to research in Natural Language Processing (NLP) and many other fields connected to Artificial Intelligence.

This has been made possible thanks to advances in speech recognition technology as well as improvements in AI models that can handle complex conversations with humans. AI and NLP technology have advanced significantly over the last few years, with many advancements in natural language understanding, semantic analysis and other related technologies. The development of AI/NLP models is important for businesses that want to increase their efficiency and accuracy in terms of content analysis and customer interaction. Artificial intelligence (AI) and natural language processing (NLP) are two closely related fields of study that have seen tremendous advancements over the last few years.

It is also a useful tool to help with automated programs, like when you’re having a question-and-answer session with a chatbot. The most recent projects based on SNePS include an implementation using the Lisp-like programming language, Clojure, known as CSNePS or Inference Graphs[39], [40]. Clinical guidelines are statements like “Fluoxetine (20–80 mg/day) should be considered for the treatment of patients with fibromyalgia.” [42], which are disseminated in medical journals and the websites of professional organizations and national health agencies, such as the U.S. Another logical language that captures many aspects of frames is CycL, the language used in the Cyc ontology and knowledge base. While early versions of CycL were described as being a frame language, more recent versions are described as a logic that supports frame-like structures and inferences. Cycorp, started by Douglas Lenat in 1984, has been an ongoing project for more than 35 years and they claim that it is now the longest-lived artificial intelligence project[29].

However, in spite of this, the Network+Identity model is able to capture many key spatial properties. Nearly 40% of Network+Identity simulations are at least “broadly similar,” and 12% of simulations are “very similar” to the corresponding empirical distribution (Fig. 1a). https://chat.openai.com/ The Network+Identity model’s Lee’s L distribution roughly matches the distribution Grieve et al. (2019) found for regional lexical variation on Twitter, suggesting that the Network+Identity model reproduces “the same basic underlying regional patterns” found on Twitter107.

1. Knowledge-Based Similarity

What scares me is that he don’t seem to know a lot about it, for example he told me “you have to reduce the high dimension of your dataset” , while my dataset is just 2000 text fields. Ontology editing tools are freely available; the most widely used is Protégé, which claims to have over 300,000 registered users. semantic nlp The most important algorithms in this type are Manhattan Distance, Euclidean Distance, Cosine Similarity, Jaccard Index, and Sorensen-Dice Index. Calculating text similarity depends on converting text to a vector of features, and then the algorithm selects a proper features representation, like TF-IDF.

Essentially, rather than simply analyzing data, this technology goes a step further and identifies the relationships between bits of data. Because of this ability, semantic analysis can help you to make sense of vast amounts of information and apply it in the real world, making your business decisions more effective. Finally, contrary to prior theories24,25,147, properties like population size and the number of incoming and outgoing ties were insufficient to reproduce urban/rural differences. The Null model, which has the same population and degree distribution, underperformed the Network+Identity model in all types of pathways. Furthermore, as shown in Supplementary Methods 1.6.5, urban/rural dynamics are only partially explained by distributions of network and identity. The Network+Identity model was able to replicate most of the empirical urban/rural associations with network and identity (Supplementary Fig. 17), so empirical distributions of demographics and network ties likely drive many urban/rural dynamics.

By using conservative thresholds for frequency and dispersion, this algorithm has been shown to produce highly precise estimates of geolocation. Since Twitter does not supply demographic information for each user, agent identities must be inferred from their activity on the site. Instead, we estimate each agent’s identity based on the Census tract and Congressional district they reside in refs. Similar to prior work studying sociolinguistic variation on Twitter12,107, each agent’s race/ethnicity, SES, and languages spoken correspond to the composition of their Census Tract in the 2018 American Community Survey.

With its ability to quickly process large data sets and extract insights, NLP is ideal for reviewing candidate resumes, generating financial reports and identifying patients for clinical trials, among many other use cases across various industries. Now, imagine all the English words in the vocabulary with all their different fixations at the end of them. To store them all would require a huge database containing many words that actually have the same meaning. Popular algorithms for stemming include the Porter stemming algorithm from 1979, which still works well.

This ability enables us to build more powerful NLP systems that can accurately interpret real-world user input in order to generate useful insights or provide personalized recommendations. Patterns in the diffusion of innovation are often well-explained by the topology of speakers’ social networks42,43,73,74,75. Nodes (agents) and edges (ties) in this network come from the Twitter Decahose, which includes a 10% random sample of tweets between 2012 and 2020.

Lexical semantics is the study of how words and phrases relate to each other and to the world. It is essential for natural language processing (NLP) and artificial intelligence (AI), as it helps machines understand the meaning and context of human language. In this article, you will learn how to apply the principles of lexical semantics to NLP and AI, and how they can improve your applications and research. The field of natural language processing is still relatively new, and as such, there are a number of challenges that must be overcome in order to build robust NLP systems. Different words can have different meanings in different contexts, which makes it difficult for machines to understand them correctly.

They developed I-Chat Bot which understands the user input and provides an appropriate response and produces a model which can be used in the search for information about required hearing impairments. The problem with naïve bayes is that we may end up with zero probabilities when we meet words in the test data for a certain class that are not present in the training data. Ambiguity is one of the major problems of natural language which occurs when one sentence can lead to different interpretations. In case of syntactic level ambiguity, one sentence can be parsed into multiple syntactical forms.

It helps to calculate the probability of each tag for the given text and return the tag with the highest probability. Bayes’ Theorem is used to predict the probability of a feature based on prior knowledge of conditions that might be related to that feature. The choice of area in NLP using Naïve Bayes Classifiers could be in usual tasks such as segmentation and translation but it is also explored in unusual areas like segmentation for infant learning and identifying documents for opinions and facts. Anggraeni et al. (2019) [61] used ML and AI to create a question-and-answer system for retrieving information about hearing loss.

However, unlike empirical pathways, the Network+Identity model’s urban-urban pathways tend to be heavier in the presence of heavy identity pathways, since agents in the model select variants on the basis of shared identity. These results suggest that urban-urban weak-tie diffusion requires some mechanism not captured in our model, such as urban speakers seeking diversity or being less attentive to identity than rural speakers when selecting variants144,145. Empirical pathways are heaviest when there is a heavy network and light identity pathway (high levels of weak-tie diffusion) and lightest when both network and identity pathways are heavy (high levels of strong-tie diffusion) (Fig. 4, dark orange bars). In other words, diffusion between pairs of urban counties tends to occur via weak-tie diffusion—spread between dissimilar network neighbors connected by low-weight ties76. 3a, where the Network-only model best reproduces the weak-tie diffusion mechanism in urban-urban pathways; conversely, the Identity-only and Network+Identity models perform worse in urban-urban pathways, amplifying strong-tie diffusion among demographically similar ties.