Semantics of Programming Languages

There is some empirical support for the grounded cognition perspective from sensorimotor priming studies. In particular, there is substantial evidence that modality-specific neural information is activated during language-processing tasks. However, whether the activation of modality-specific information is incidental to the task and simply a result of post-representation processes, or actually part of the semantic representation itself is an important question. Yee et al. also showed that when individuals performed a concurrent manual task while naming pictures, there was more naming interference for objects that are more manually used (e.g., pencils), compared to objects that are not typically manually used (e.g., tigers). Taken together, these findings suggest that semantic memory representations are accessed in a dynamic way during tasks and different perceptual features of these representations may be accessed at different timepoints, suggesting a more flexible and fluid conceptualization (also see Yee, Lahiri, & Kotzor, 2017) of semantic memory that can change as a function of task. Therefore, it is important to evaluate whether computational models of semantic memory can indeed encode these rich, non-linguistic features as part of their representations.

One line of evidence that speaks to this behavior comes from empirical work on reading and speech processing using the N400 component of event-related brain potentials (ERPs). The N400 component is thought to reflect contextual semantic processing, and sentences ending in unexpected words have been shown to elicit greater N400 amplitude compared to expected words, given a sentential context (e.g., Block & Baldwin, 2010; Federmeier & Kutas, 1999; Kutas & Hillyard, 1980). This body of work suggests that sentential context and semantic memory structure interact during sentence processing (see Federmeier & Kutas, 1999). Other work has examined the influence of local attention, context, and cognitive control during sentence comprehension. In an eye-tracking paradigm, Nozari, Trueswell, and Thompson-Schill (2016) had participants listen to a sentence (e.g., “She will cage the red lobster”) as they viewed four colorless drawings.

Semantic analysis, on the other hand, is crucial to achieving a high level of accuracy when analyzing text. I am currently pursuing my Bachelor of Technology (B.Tech) in Computer Science and Engineering from the Indian Institute of Technology Jodhpur(IITJ). For Example, Tagging Twitter mentions by sentiment to get a sense of how customers feel about your product and can identify unhappy customers in real-time. In Sentiment analysis, our aim is to detect the emotions as positive, negative, or neutral in a text to denote urgency. Besides, Semantics Analysis is also widely employed to facilitate the processes of automated answering systems such as chatbots – that answer user queries without any human interventions.

To that end, Gruenenfelder et al. (2016) compared three distributional models (LSA, BEAGLE, and Topic models) and one simple associative model and indicated that only a hybrid model that combined contextual similarity and associative networks successfully predicted the graph theoretic properties of free-association norms (also see Richie, White, Bhatia, & Hout, 2019). Therefore, associative networks and feature-based models can potentially capture complementary information compared to standard distributional models, and may provide additional cues about the features and associations other than co-occurrence that may constitute meaning. Indeed, as discussed in Section III, multimodal and feature-integrated DSMs that use different linguistic and non-linguistic sources of information to learn semantic representations are currently a thriving area of research and are slowly changing the conceptualization of what constitutes semantic memory (e.g., Bruni et al., 2014; Lazaridou et al., 2015). In a recent article, Günther, Rinaldi, and Marelli (2019) reviewed several common misconceptions about distributional semantic models and evaluated the cognitive plausibility of modern DSMs. Although the current review is somewhat similar in scope to Günther et al.’s work, the current paper has different aims.

It is an ideal way for researchers in programming languages and advanced graduate students to learn both modern semantics and category theory. I have used a very early draft of a few chapters with some success in an advanced graduate class at Iowa State University. I am glad that Professor Gunter has added more introductory material, and also more detail on type theory. The book has a balanced treatment of operational and fixed point semantics, which reflects the growing importance of operational semantics. Pixels are labeled according to the semantic features they have in common, such as color or placement.

Moreover, the features produced in property generation tasks are potentially prone to saliency biases (e.g., hardly any participant will produce the feature for a dog because having a head is not salient or distinctive), and thus can only serve as an incomplete proxy for all the features encoded by the brain. To address these concerns, Bruni et al. (2014) applied advanced computer vision techniques to automatically extract visual and linguistic features from multimodal corpora to construct multimodal distributional semantic representations. Using a technique called “bag-of-visual-words” (Sivic & Zisserman, 2003), the model discretized visual images and produced visual units comparable to words in a text document. The resulting image matrix was then concatenated with a textual matrix constructed from a natural language corpus using singular value decomposition to yield a multimodal semantic representation.

However, the argument that predictive models employ psychologically plausible learning mechanisms is incomplete, because error-free learning-based DSMs also employ equally plausible learning mechanisms, consistent with Hebbian learning principles. Asr, Willits, and Jones (2016) compared an error-free learning-based model (similar to HAL), a random vector accumulation model (similar to BEAGLE), and word2vec in their ability to acquire semantic categories when trained on child-directed speech data. Their results indicated that when the corpus was scaled down to stimulus available to children, the HAL-like model outperformed word2vec. Other work has also found little to no advantage of predictive models over error-free learning-based models (De Deyne, Perfors, & Navarro, 2016; Recchia & Nulty, 2017).

Difference Between Keyword And Semantic Search

However, the original architecture of topic models involved setting priors and specifying the number of topics a priori, which could lead to the possibility of experimenter bias in modeling (Jones, Willits, & Dennis, 2015). Further, the original topic model was essentially a “bag-of-words” model and did not capitalize on the sequential dependencies in natural language, like other DSMs (e.g., BEAGLE). Recent work by Andrews and Vigliocco (2010) has extended the topic model to incorporate word-order information, yielding more fine-grained linguistic representations that are sensitive to higher-order semantic relationships.

Typically, Bi-Encoders are faster since we can save the embeddings and employ Nearest Neighbor search for similar texts. Cross-encoders, on the other hand, may learn to fit the task better as they allow fine-grained cross-sentence attention inside the PLM. With the PLM as a core building block, Bi-Encoders pass the two sentences separately to the PLM and encode each as a vector. The final similarity or dissimilarity score is calculated with the two vectors using a metric such as cosine-similarity. Expert.ai’s rule-based technology starts by reading all of the words within a piece of content to capture its real meaning. Finally, it analyzes the surrounding text and text structure to accurately determine the proper meaning of the words in context.

Semantic analysis allows computers to interpret the correct context of words or phrases with multiple meanings, which is vital for the accuracy of text-based NLP applications. 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. Semantic analysis helps natural language processing (NLP) figure out the correct concept for words and phrases that can have more than one meaning. When combined with machine learning, semantic analysis allows you to delve into your customer data by enabling machines to extract meaning from unstructured text at scale and in real time. Generally, with the term semantic search, there is an implicit understanding that there is some level of machine learning involved.

Therefore, exactly how humans perform the same semantic tasks without the large amounts of data available to these models remains unknown. One line of reasoning is that while humans have lesser linguistic input compared to the corpora that modern semantic models are trained on, humans instead have access to a plethora of non-linguistic sensory and environmental input, which is likely contributing to their semantic representations. Indeed, the following section discusses how conceptualizing semantic memory as a multimodal system sensitive to perceptual input represents the next big paradigm shift in the study of semantic memory.

Latent semantic analysis (sometimes latent semantic indexing), is a class of techniques where documents are represented as vectors in term space. One limitation of semantic analysis occurs when using a specific technique called explicit semantic analysis (ESA). ESA examines separate sets of documents and then attempts to extract meaning from the text based on the connections and similarities between the documents. The problem with ESA occurs if the documents submitted for analysis do not contain high-quality, structured information. Additionally, if the established parameters for analyzing the documents are unsuitable for the data, the results can be unreliable. It’s an essential sub-task of Natural Language Processing (NLP) and the driving force behind machine learning tools like chatbots, search engines, and text analysis.

The construction of a word-by-document matrix and the dimensionality reduction step are central to LSA and have the important consequence of uncovering global or indirect relationships between words even if they never co-occurred with each other in the original context of documents. For example, lion and stripes may have never co-occurred within a sentence or document, but because they often occur in similar contexts of the word tiger, they would develop similar semantic representations. Importantly, the ability to infer latent dimensions and extend the context window from sentences to documents differentiates LSA from a model like HAL. In their model, each visual scene had a distributed vector representation, encoding the features that are relevant to the scene, which were learned using an unsupervised CNN. Additionally, scenes contained relational information that linked specific roles to specific fillers via circular convolution. A four-layer fully connected NN with Gated Recurrent Units (GRUs; a type of recurrent NN) was then trained to predict successive scenes in the model.

We have a query (our company text) and we want to search through a series of documents (all text about our target company) for the best match. Semantic matching is a core component of this search process as it finds the query, document pairs that are most similar. Though generalized large language model (LLM) based applications are capable of handling broad and common tasks , specialized models based on a domain-specific taxonomy, ontology, and knowledge base design will be essential to power intelligent applications .

This intuition inspired the attention mechanism, where “attention” could be focused on a subset of the original input units by weighting the input words based on positional and semantic information. Bahdanau, Cho, and Bengio (2014) first applied the attention mechanism to machine translation using two separate RNNs to first encode the input sequence and then used an attention head to explicitly focus on relevant words to generate the translated outputs. “Attention” was focused on specific words by computing an alignment score, to determine which input states were most relevant for the current time step and combining these weighted input states into a context vector. This context vector was then combined with the previous state of the model to generate the predicted output. Bahdanau et al. showed that the attention mechanism was able to outperform previous models in machine translation (e.g., Cho et al., 2014), especially for longer sentences. This section provided a detailed overview of traditional and recent computational models of semantic memory and highlighted the core ideas that have inspired the field in the past few decades with respect to semantic memory representation and learning.

A recent example of this fundamental debate regarding the origin of the representation comes from research on the semantic fluency task, where participants are presented with a natural category label (e.g., “animals”) and are required to generate as many exemplars from that category (e.g., lion, tiger, elephant…) as possible within a fixed time period. Hills, Jones, and Todd (2012) proposed that the temporal pattern of responses produced in the fluency task mimics optimal foraging techniques found among animals in natural environments. They provided a computational account of this search process based on the BEAGLE model (Jones & Mewhort, 2007).

The accumulating evidence that meaning rapidly changes with linguistic context certainly necessitates models that can incorporate this flexibility into word representations. The success of attention-based NNs is truly impressive on one hand but also cause for concern on the other. First, it is remarkable that the underlying mechanisms proposed by these models at least appear to be psychologically intuitive and consistent with empirical work showing that attentional processes and predictive signals do indeed contribute to semantic task performance (e.g., Nozari et al., 2016). However, if the ultimate goal is to build models that explain and mirror human cognition, the issues of scale and complexity cannot be ignored. Current state-of-the-art models operate at a scale of word exposure that is much larger than what young adults are typically exposed to (De Deyne, Perfors, & Navarro, 2016; Lake, Ullman, Tenenbaum, & Gershman, 2017).

Furthermore, it is also unlikely that any semantic relationships are purely direct or indirect and may instead fall on a continuum, which echoes the arguments posed by Hutchison (2003) and Balota and Paul (1996) regarding semantic versus associative relationships. These results are especially important if state-of-the-art models like word2vec, ELMo, BERT or GPT-2/3 are to be considered plausible models of semantic memory in any manner and certainly underscore the need to focus on mechanistic accounts of model behavior. Understanding how machine-learning models arrive at answers to complex semantic problems is as important as simply evaluating how many questions the model was able to answer.

Specifically, instead of explicitly training to predict predefined or empirically determined sense clusters, ELMo first tries to predict words in a sentence going sequentially forward and then backward, utilizing recurrent connections through a two-layer LSTM. The embeddings returned from these “pretrained” forward and backward LSTMs are then combined with a task-specific NN model to construct a task-specific representation (see Fig. 6). One key innovation in the ELMo model is that instead of only using the topmost layer produced by the LSTM, it computes a weighed linear combination of all three layers of the LSTM to construct the final semantic representation. The logic behind using all layers of the LSTM in ELMo is that this process yields very rich word representations, where higher-level LSTM states capture contextual aspects of word meaning and lower-level states capture syntax and parts of speech. Peters et al. showed that ELMo’s unique architecture is successfully able to outperform other models in complex tasks like question answering, coreference resolution, and sentiment analysis among others. The success of recent recurrent models such as ELMo in tackling multiple senses of words represents a significant leap forward in modeling contextualized semantic representations.

This fundamental capability is critical to various NLP applications, from sentiment analysis and information retrieval to machine translation and question-answering systems. The continual refinement of semantic analysis techniques will therefore play a pivotal role in the evolution and advancement of NLP technologies. 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 of lexical semantics include identifying word senses, synonyms, antonyms, hyponyms, hypernyms, and morphology.

Even so, these grounded models are limited by the availability of multimodal sources of data, and consequently there have been recent efforts at advocating the need for constructing larger databases of multimodal data (Günther et al., 2019). The RNN approach inspired Peters et al. (2018) to construct Embeddings from Language Models (ELMo), a modern version of recurrent neural networks (RNNs). Peters et al.’s ELMo model uses a bidirectional LSTM combined with a traditional NN language model to construct contextual word embeddings.

While the approach of applying a process model over and above the core distributional model could be criticized, it is important to note that meaning is necessarily distributed across several dimensions in DSMs and therefore any process model operating on these vectors is using only information already contained within the vectors (see Günther et al., 2019, for a similar argument). The fifth and final section focuses on some open issues in semantic modeling, such as proposing models that can be applied to other languages, issues related to data abundance and availability, understanding the social and evolutionary roles of language, and finding mechanistic process-based accounts of model performance. These issues shed light on important next steps in the study of semantic memory and will be critical in advancing our understanding of how meaning is constructed and guides cognitive behavior. These refer to techniques that represent words as vectors in a continuous vector space and capture semantic relationships based on co-occurrence patterns. Another popular distributional model that has been widely applied across cognitive science is Latent Semantic Analysis (LSA; Landauer & Dumais, 1997), a semantic model that has successfully explained performance in several cognitive tasks such as semantic similarity (Landauer & Dumais, 1997), discourse comprehension (Kintsch, 1998), and essay scoring (Landauer, Laham, Rehder, & Schreiner, 1997). LSA begins with a word-document matrix of a text corpus, where each row represents the frequency of a word in each corresponding document, which is clearly different from HAL’s word-by-word co-occurrence matrix.

The question of how meaning is represented and organized by the human brain has been at the forefront of explorations in philosophy, psychology, linguistics, and computer science for centuries. Does knowing the meaning of an ostrich involve having a prototypical representation of an ostrich that has been created by averaging over multiple exposures to individual ostriches? Or does it instead involve extracting particular features that are characteristic of an ostrich (e.g., it is big, it is a bird, it does not fly, etc.) that are acquired via experience, and stored and activated upon encountering an ostrich? Further, is this knowledge stored through abstract and arbitrary symbols such as words, or is it grounded in sensorimotor interactions with the physical environment? The computation of meaning is fundamental to all cognition, and hence it is not surprising that considerable work has attempted to uncover the mechanisms that contribute to the construction of meaning from experience.

Error-driven learning-based DSMs

With this intelligence, semantic search can perform in a more human-like manner, like a searcher finding dresses and suits when searching fancy, with not a jean in sight. We have already seen ways in which semantic search is intelligent, but it’s worth looking more at how it is different from keyword search. Semantic search applies user intent, context, and conceptual meanings to match a user query to the corresponding content. To understand whether semantic search is applicable to your business and how you can best take advantage, it helps to understand how it works, and the components that comprise semantic search. Additionally, as with anything that shows great promise, semantic search is a term that is sometimes used for search that doesn’t truly live up to the name.

The filter transforms the larger window of information into a fixed d-dimensional vector, which captures the important properties of the pixels or words in that window. Convolution is followed by a “pooling” step, where vectors from different windows are combined into a single d-dimensional vector, by taking the maximum or average value of each of the d-dimensions across the windows. This process extracts the most important features from a larger set of pixels (see Fig. 8), or the most informative k-grams in a long sentence. CNNs have been flexibly applied to different semantic tasks like sentiment analysis and machine translation (Collobert et al., 2011; Kalchbrenner, Grefenstette, & Blunsom, 2014), and are currently being used to develop multimodal semantic models. Despite the traditional notion of semantic memory being a “static” store of verbal knowledge about concepts, accumulating evidence within the past few decades suggests that semantic memory may actually be context-dependent.

Indeed, language is inherently compositional in that morphemes combine to form words, words combine to form phrases, and phrases combine to form sentences. Moreover, behavioral evidence from sentential priming studies indicates that the meaning of words depends on complex syntactic relations (Morris, 1994). Further, it is well known that the meaning of a sentence itself is not merely the sum of the words it contains. For example, the sentence “John loves Mary” has a different meaning to “Mary loves John,” despite both sentences having the same words. Thus, it is important to consider how compositionality can be incorporated into and inform existing models of semantic memory.

Although these research efforts are less language-focused, deep reinforcement learning models have also been proposed to specifically investigate language learning. For example, Li et al. (2016) trained a conversational agent using reinforcement learning, and a reward metric based on whether the dialogues generated by the model were easily answerable, informative, and coherent. Other learning-based models have used adversarial training, a method by which a model is trained to produce responses that would be indistinguishable from human responses (Li et al., 2017), a modern version of the Turing test (also see Spranger, Pauw, Loetzsch, & Steels, 2012). However, these recent attempts are still focused on independent https://chat.openai.com/ learning, whereas psychological and linguistic research suggests that language evolved for purposes of sharing information, which likely has implications for how language is learned in the first place. Clearly, this line of work is currently in its nascent stages and requires additional research to fully understand and model the role of communication and collaboration in developing semantic knowledge. Tulving’s (1972) episodic-semantic dichotomy inspired foundational research on semantic memory and laid the groundwork for conceptualizing semantic memory as a static memory store of facts and verbal knowledge that was distinct from episodic memory, which was linked to events situated in specific times and places.

In the next step, individual words can be combined into a sentence and parsed to establish relationships, understand syntactic structure, and provide meaning. 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. With lexical semantics, the study of word meanings, semantic analysis provides a deeper understanding of unstructured text.

On the other hand, semantic relations have traditionally included only category coordinates or concepts with similar features (e.g., ostrich-emu; Hutchison, 2003; Lucas, 2000). Given these different operationalizations, some researchers have attempted to isolate pure “semantic” priming effects by selecting items that are semantically related (i.e., share category membership; Fischler, 1977; Lupker, 1984; Thompson-Schill, Kurtz, & Gabrieli, 1998) but not associatively related (i.e., based on free-association norms), although these attempts have not been successful. Specifically, there appear to be discrepancies in how associative strength is defined and the locus of these priming effects.

Code, Data and Media Associated with this Article

This was indeed the observation made by Meyer and Schvaneveldt (1971), who reported the first semantic priming study, where they found that individuals were faster to make lexical decisions (deciding whether a presented stimulus was a word or non-word) for semantically related (e.g., ostrich-emu) word pairs, compared to unrelated word pairs (e.g., apple-emu). Given that individuals were not required to access the semantic relationship between words to make the lexical decision, these findings suggested that the task potentially reflected automatic retrieval processes operating on underlying semantic representations (also see Neely, 1977). The semantic priming paradigm has since become the most widely applied task in cognitive psychology to examine semantic representation and processes (for reviews, see Hutchison, 2003; Lucas, 2000; Neely, 1977).

Instead of defining context in terms of a sentence or document like most DSMs, the Predictive Temporal Context Model (pTCM; see also Howard & Kahana, 2002) proposes a continuous representation of temporal context that gradually changes over time. Items in the pTCM are activated to the extent that their encoded context overlaps with the context that is cued. Further, context is also used to predict items that are likely to appear next, and the semantic representation of an item is the collection of prediction vectors in which it appears over time. Howard et al. showed that the pTCM successfully simulates human performance in word-association tasks and is able to capture long-range dependencies in language that are problematic for other DSMs. An alternative proposal to model semantic memory and also account for multiple meanings was put forth by Blei, Ng, and Jordan (2003) and Griffiths et al. (2007) in the form of topic models of semantic memory.

Although the technical complexity of attention-based NNs makes it difficult to understand the underlying mechanisms contributing to their impressive success, some recent work has attempted to demystify these models (e.g., Clark, Khandelwal, Levy, & Manning, 2019; Coenen et al., 2019; Michel, Levy, & Neubig, 2019; Tenney, Das, & Pavlick, 2019). For example, Clark et al. (2019) recently showed that BERT’s attention heads actually attend to meaningful semantic and syntactic information in sentences, such as determiners, objects of verbs, and co-referent mentions (see Fig. 7), suggesting that these models may indeed be capturing meaningful linguistic knowledge, which may be driving their performance. Further, some recent evidence also shows that BERT successfully captures phrase-level representations, indicating that BERT may indeed have the ability to model compositional structures (Jawahar, Sagot, & Seddah, 2019), although this work is currently in its nascent stages. Furthermore, it remains unclear how this conceptualization of attention fits with the automatic-attentional framework (Neely, 1977). Demystifying the inner workings of attention NNs and focusing on process-based accounts of how computational models may explain cognitive phenomena clearly represents the next step towards integrating these recent computational advances with empirical work in cognitive psychology.

A query like “tampa bay football players”, however, probably doesn’t need to know where the searcher is located. As you can imagine, attempting to go beyond the surface-level information embedded in the text is a complex endeavor. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. ArXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

For example, Socher, Huval, Manning, and Ng (2012) proposed a recursive NN to compute compositional meaning representations. In their model, each word is assigned a vector that captures its meaning and also a matrix that contains information about how it modifies the meaning of another word. This representation for each word is then recursively combined with other words using a non-linear composition function (an extension of work by Mitchell & Lapata, 2010). For example, in the first iteration, the words very and good may be combined into a representation Chat GPT (e.g., very good), which would recursively be combined with movie to produce the final representation (e.g., very good movie). Socher et al. showed that this model successfully learned propositional logic, how adverbs and adjectives modified nouns, sentiment classification, and complex semantic relationships (also see Socher et al., 2013). Other work in this area has explored multiplication-based models (Yessenalina & Cardie, 2011), LSTM models (Zhu, Sobhani, & Guo, 2016), and paraphrase-supervised models (Saluja, Dyer, & Ruvini, 2018).

Riordan and Jones argued that children may be more likely to initially extract information from sensorimotor experiences. However, as they acquire more linguistic experience, they may shift to extracting the redundant information from the distributional structure of language and rely on perception for only novel concepts or the unique sources of information it provides. This idea is consistent with the symbol interdependency hypothesis (Louwerse, 2011), which proposes that while words must be grounded in the sensorimotor action and perception, they also maintain rich connections with each other at the symbolic level, which allows for more efficient language processing by making it possible to skip grounded simulations when unnecessary. The notion that both sources of information are critical to the construction of meaning presents a promising approach to reconciling distributional models with the grounded cognition view of language (for similar accounts, see Barsalou, Santos, Simmons, & Wilson, 2008; Paivio, 1991). It is important to note here that while the sensorimotor studies discussed above provide support for the grounded cognition argument, these studies are often limited in scope to processing sensorimotor words and do not make specific predictions about the direction of effects (Matheson & Barsalou, 2018; Matheson, White, & McMullen, 2015). For example, although several studies show that modality-specific information is activated during behavioral tasks, it remains unclear whether this activation leads to facilitation or inhibition within a cognitive task.

It does this by incorporating real-world knowledge to derive user intent based on the meaning of queries and content. More specifically, there are enough matching letters (or characters) to tell the engine that a user searching for one will want the other. But we know as well that synonyms are not universal – sometimes two words are equivalent in one context, and not in another. We’ve already discussed that synonyms are useful in all kinds of search, and can improve keyword search by expanding the matches for queries to related content. On a group level, a search engine can re-rank results using information about how all searchers interact with search results, such as which results are clicked on most often, or even seasonality of when certain results are more popular than others. You can foun additiona information about ai customer service and artificial intelligence and NLP. Personalization will use that individual searcher’s affinities, previous searches, and previous interactions to return the content that is best suited to the current query.

Using the Chinese Restaurant Process, at each timepoint, the model evaluated its prediction error to decide if its current event representation was still a good fit. If the prediction error was high, the model chose whether it should switch to a different previously-learned event representation or create an entirely new event representation, by tuning parameters to evaluate total number of events and event durations. Franklin et al. showed that their model successfully learned complex event dynamics and simulated a wide variety of empirical phenomena. For example, the model’s ability to predict event boundaries from unannotated video data (Zacks, Kurby, Eisenberg, & Haroutunian, 2011) of a person completing everyday tasks like washing dishes, was highly correlated with grouped participant data and also produced similar levels of prediction error across event boundaries as human participants. Despite its widespread application and success, LSA has been criticized on several grounds over the years, e.g., for ignoring word transitions (Perfetti, 1998), violating power laws of connectivity (Steyvers & Tenenbaum, 2005), and for the lack of a mechanism for learning incrementally (Jones, Willits, & Dennis, 2015).

III. Grounding Models of Semantic Memory

Analyzing errors in language tasks provides important cues about the mechanics of the language system. However, computational accounts for how language may be influenced by interference or degradation remain limited. However, current state-of-the-art language models like word2vec, BERT, and GPT-2 or GPT-3 do not provide explicit accounts for how neuropsychological deficits may arise, or how systematic speech and reading errors are produced.

Memory of a document (or conversation) is the sum of all word vectors, and a “memory” vector stores all documents in a single vector. A word’s meaning is retrieved by cueing the memory vector with a probe, which activates each trace in proportion to its similarity to the probe. The aggregate of all activated traces is called an echo, where the contribution of a trace is directly weighted by its activation. Therefore, the model exhibits “context sensitivity” by comparing the activations of the retrieval probe with the activations of other traces in memory, thus producing context-dependent semantic representations without any mechanism for learning these representations.

• Indeed, there is some skepticism in the field about whether these models are truly learning something meaningful or simply exploiting spurious statistical cues in language, which may or may not reflect human learning.
• This proposal is similar to the ideas presented earlier regarding how perceptual or sensorimotor experience might be important for grounding words acquired earlier, and words acquired later might benefit from and derive their representations through semantic associations with these early experiences (Howell et al., 2005; Riordan & Jones, 2011).
• Essentially, in this position, you would translate human language into a format a machine can understand.
• There are many components in a semantic search pipeline, and getting each one correct is important.
• Carl Gunter’s Semantics of Programming Languages is a much-needed resource for students, researchers, and designers of programming languages.

Prediction is another contentious issue in semantic modeling that has gained a considerable amount of traction in recent years, and the traditional distinction between error-free Hebbian learning and error-driven Rescorla-Wagner-type learning has been carried over to debates between different DSMs in the literature. It is important to note here that the count versus predict distinction is somewhat artificial and misleading, because even prediction-based DSMs effectively use co-occurrence counts of words from natural language corpora to generate predictions. The important difference between these models is therefore not that one class of models counts co-occurrences whereas the other predicts them, but in fact that one class of models employs an error-free Hebbian learning process whereas the other class of models employs a prediction-based error-driven learning process to learn direct and indirect associations between words. Nonetheless, in an influential paper, Baroni et al. (2014) compared 36 “count-based” or error-free learning-based DSMs to 48 “predict” or error-driven learning-based DSMs and concluded that error-driven learning-based (predictive) models significantly outperformed their Hebbian learning-based counterparts in a large battery of semantic tasks. Additionally, Mandera, Keuleers, and Brysbaert (2017) compared the relative performance of error-free learning-based DSMs (LSA and HAL-type) and error-driven learning-based models (CBOW and skip-gram versions of word2vec) on semantic priming tasks (Hutchison et al., 2013) and concluded that predictive models provided a better fit to the data. They also argued that predictive models are psychologically more plausible because they employ error-driven learning mechanisms consistent with principles posited by Rescorla and Wagner (1972) and are computationally more compact.

Importantly, several of these recent approaches rely on error-free learning-based mechanisms to construct semantic representations that are sensitive to context. The following section describes some recent work in machine learning that has focused on error-driven learning mechanisms that can also adequately account for contextually-dependent semantic representations. To the extent that DSMs are limited by the corpora they are trained on (Recchia & Jones, 2009), it is possible that the responses from free-association tasks and property-generation norms capture some non-linguistic aspects of meaning that are missing from standard DSMs, for example, imagery, emotion, perception, etc.

This information can help your business learn more about customers’ feedback and emotional experiences, which can assist you in making improvements to your product or service. In semantic analysis with machine learning, computers use word sense disambiguation to determine which meaning is correct in the given context. When done correctly, semantic search will use real-world knowledge, especially through machine learning and vector similarity, to match a user query to the corresponding content. The field of NLP has recently been revolutionized by large pre-trained language models (PLM) such as BERT, RoBERTa, GPT-3, BART and others. These new models have superior performance compared to previous state-of-the-art models across a wide range of NLP tasks. But before deep dive into the concept and approaches related to meaning representation, firstly we have to understand the building blocks of the semantic system.

IV. Compositional Semantic Representations

As discussed in this section, DSMs often distinguish between and differentially emphasize these two types of relationships (i.e., direct vs. indirect co-occurrences; see Jones et al., 2006), which has important implications for the extent to which these models speak to this debate between associative vs. truly semantic relationships. The combined evidence from the semantic priming literature and computational modeling literature suggests that the formation of direct associations is most likely an initial step in the computation of meaning. However, it also appears that the complex semantic memory system does not simply rely on these direct associations but also applies additional learning mechanisms (vector accumulation, abstraction, etc.) to derive other meaningful, indirect semantic relationships. Implementing such global processes allows modern distributional models to develop more fine-grained semantic representations that capture different types of relationships (direct and indirect). However, there do appear to be important differences in the underlying mechanisms of meaning construction posited by different DSMs. Further, there is also some concern in the field regarding the reliance on pure linguistic corpora to construct meaning representations (De Deyne, Perfors, & Navarro, 2016), an issue that is closely related to assessing the role of associative networks and feature-based models in understanding semantic memory, as discussed below.

Associative, feature-based, and distributional semantic models are introduced and discussed within the context of how these models speak to important debates that have emerged in the literature regarding semantic versus associative relationships, prediction, and co-occurrence. In particular, a distinction is drawn between distributional models that propose error-free versus error-driven learning mechanisms for constructing meaning representations, and the extent to which these models explain performance in empirical tasks. Overall, although empirical tasks have partly informed computational models of semantic memory, the empirical and computational approaches to studying semantic memory have developed somewhat independently. Therefore, it appears that when DSMs are provided with appropriate context vectors through their representation (e.g., topic models) or additional assumptions (e.g., LSA), they are indeed able to account for patterns of polysemy and homonymy. Additionally, there has been a recent movement in natural language processing to build distributional models that can naturally tackle homonymy and polysemy.

• Proposed in 2015, SiameseNets is the first architecture that uses DL-inspired Convolutional Neural Networks (CNNs) to score pairs of images based on semantic similarity.
• Further, it is well known that the meaning of a sentence itself is not merely the sum of the words it contains.
• The majority of the work in machine learning and natural language processing has focused on building models that outperform other models, or how the models compare to task benchmarks for only young adult populations.
• For example, the homonym bark would be represented as a weighted average of its two meanings (the sound and the trunk), leading to a representation that is more biased towards the more dominant sense of the word.

In other words, each episodic experience lays down a trace, which implies that if an item is presented multiple times, it has multiple traces. At the time of retrieval, traces are activated in proportion to its similarity with the retrieval cue or probe. For example, an individual may have seen an ostrich in pictures or at the zoo multiple times and would store each of these instances in memory. The next time an ostrich-like bird is encountered by this individual, they would match the features of this bird to a weighted sum of all stored instances of ostrich and compute the similarity between these features to decide whether the semantic techniques new bird is indeed an ostrich. Hintzman’s work was crucial in developing the exemplar theory of categorization, which is often contrasted against the prototype theory of categorization (Rosch & Mervis, 1975), which suggests that individuals “learn” or generate an abstract prototypical representation of a concept (e.g., ostrich) and compare new examples to this prototype to organize concepts into categories. Importantly, Hintzman’s model rejected the need for a strong distinction between episodic and semantic memory (Tulving, 1972) and has inspired a class of models of semantic memory often referred to as retrieval-based models.

However, many organizations struggle to capitalize on it because of their inability to analyze unstructured data. This challenge is a frequent roadblock for artificial intelligence (AI) initiatives that tackle language-intensive processes. With the help of meaning representation, we can link linguistic elements to non-linguistic elements. Lexical analysis is based on smaller tokens but on the contrary, the semantic analysis focuses on larger chunks. Therefore, the goal of semantic analysis is to draw exact meaning or dictionary meaning from the text.

Currently, there are several variations of the BERT pre-trained language model, including , , and PubMedBERT , that have applied to BioNER tasks. 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. Depending on the industry in which you work, your responsibilities could include designing NLP systems, defining data sets for language learning, identifying the proper algorithm for NLP projects, and even collaborating with others to convey technical information to people without your background.

The concluding section advocates the need for integrating representational accounts of semantic memory with process-based accounts of cognitive behavior, as well as the need for explicit comparisons of computational models to human baselines in semantic tasks to adequately assess their psychological plausibility as models of human semantic memory. Distributional Semantic Models (DSMs) refer to a class of models that provide explicit mechanisms for how words or features for a concept may be learned from the natural environment. The principle of extracting co-occurrence patterns and inferring associations between concepts/words from a large text-corpus is at the core of all DSMs, but exactly how these patterns are extracted has important implications for how these models conceptualize the learning process.