A Survey of Semantic Analysis Approaches SpringerLink

Collectively, these recent approaches to construct contextually sensitive semantic representations (through recurrent and attention-based NNs) are showing unprecedented success at addressing the bottlenecks regarding polysemy, attentional influences, and context that were considered problematic for earlier DSMs. An important insight that is common to both contextualized RNNs and attention-based NNs discussed above is the idea of contextualized semantic representations, a notion that is certainly at odds with the traditional conceptualization of context-free semantic memory. Indeed, the following section discusses a new class of models take this notion a step further by entirely eliminating the need for learning representations or “semantic memory” and propose that all meaning representations may in fact be retrieval-based, therefore blurring the historical distinction between episodic and semantic memory. Another line of research in support of associative influences underlying semantic priming comes from studies on mediated priming. In a typical experiment, the prime (e.g., lion) is related to the target (e.g., stripes) only through a mediator (e.g., tiger), which is not presented during the task. The critical finding is that robust priming effects are observed in pronunciation and lexical decision tasks for mediated word pairs that do not share any obvious semantic relationship or featural overlap (Balota & Lorch, 1986; Livesay & Burgess, 1998; McNamara & Altarriba, 1988).

Mainly, the classes belong to the road scenes same as in the cityscapes dataset, but it has more annotations than cityscapes. Moreover, the Semantic KITTI is also used as the outclass dataset to understand the semantics of scenes [5]. It is based on the KITTI Vision with all the sequences or frames with overall angles (Table 1). PSPNet deploys a pyramid parsing module that gathers contextual image datasets at a higher accuracy rate than its predecessors. Like its predecessors, the PSPNet architecture employs the encoder-decoder approach, but where DeepLab applied upscaling to make its pixel-level calculations, PSPNet adds a new pyramid pooling layer to achieve its results.

For semantic segmentation to work, the algorithm needs to be able not only to classify pixels, but to project high-level classifications into the pixel space at different stages of the encoder. 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. The performance evaluation can vary from problem to problem when making the deep learning neural network. Mainly, traditional methods like KNN [81], Decision tree with Boosting [85], SVM [53], conditional random fields, or any statistical-based approach use accuracy or precision as performance evaluation metrics.

• In addition, NLP’s data analysis capabilities are ideal for reviewing employee surveys and quickly determining how employees feel about the workplace.
• As natural language consists of words with several meanings (polysemic), the objective here is to recognize the correct meaning based on its use.
• The weights of the signals are thus adjusted to minimize the error between the target output and the network’s output, through error backpropagation (Rumelhart, Hinton, & Williams, 1988).
• However, there do appear to be important differences in the underlying mechanisms of meaning construction posited by different DSMs.
• Now, we have a brief idea of meaning representation that shows how to put together the building blocks of semantic systems.

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. Further, there is also some evidence challenging the resounding success of predictive models. 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.

A Survey of Semantic Analysis Approaches

The overall results of the study were that semantics is paramount in processing natural languages and aid in machine learning. This study has covered various aspects including the Natural Language Processing (NLP), Latent Semantic Analysis (LSA), Explicit Semantic Analysis (ESA), and Sentiment Analysis (SA) in different sections of this study. This study also highlights the future prospects of semantic analysis domain and finally the study is concluded with the result section where areas of improvement are highlighted and the recommendations are made for the future research. This study also highlights the weakness and the limitations of the study in the discussion (Sect. 4) and results (Sect. 5). Another helpful example is detecting some diseases by taking any medical training dataset [72]. The most recent research in this particular area is to detect COVID-19 [21, 73] when you have a sample of lungs, all the images for the infected lungs you can train.

Instead, they learn an embedding space where two semantically similar images will lie closer to each other. 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.

Error-driven learning-based DSMs

The car’s computer vision must be trained to consistently recognize all of them or else it might not always tell the car to brake; its training must also be extremely accurate and precise, or else it might constantly brake after mistakenly classifying innocuous visuals as objects of concern. Semantic segmentation is a computer vision task that assigns a class label to pixels using a deep learning (DL) algorithm. It is one of three sub-categories in the overall process of image segmentation that helps computers understand visual information. Semantic segmentation identifies collections of pixels and classifies them according to various characteristics.

It goes beyond keyword matching by using information that might not be present immediately in the text (the keywords themselves) but is closely tied to what the searcher wants. Scale AI workloads, for all your data, anywhere with IBM watsonx.data, a fit-for-purpose data store built on an open data lakehouse architecture. Learn more about the differences between key terms involved in teaching computers to understand and process visual information. Discover how IBM® watsonx.data helps enterprises address the challenges of today’s complex data landscape and scale AI to suit their needs. Tickets can be instantly routed to the right hands, and urgent issues can be easily prioritized, shortening response times, and keeping satisfaction levels high.

The U-Net architecture is a modification of the original FCN architecture that was introduced in 2015 and consistently achieves better results. While the encoder stacks convolutional layers that are consistently downsampling the image to extract information from it, the decoder rebuilds the image features using the process of deconvolution. U-net architecture is primarily used in the medical field to identify cancerous and non-cancerous tumors in the lungs and brain. 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. With the help of meaning representation, we can represent unambiguously, canonical forms at the lexical level.

• Given a query of N token vectors, we learn m global context vectors (essentially attention heads) via self-attention on the query tokens.
• Semantic Segmentation is used in image manipulation, 3D modeling, facial segmentation, the healthcare industry, precision agriculture, and more.
• It can be seen that the chosen keypoints are detected irrespective of their orientation and scale.
• Search engines use semantic analysis to understand better and analyze user intent as they search for information on the web.
• The only drawback that we have evaluated is that when you take a high-resolution image, this algorithm becomes slower and also causes a delay.
• 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).

Here we can get the extract part of the image, i.e., a feature required to be extracted. Moreover, here we can see that the image size decreases, but the depth, context, or receptive field enhances [30]. In return, we can see how size increases, resulting in losing the information that where our actual matrix value was where place.

Over the past few decades, advances in the fields of psychology, computational linguistics, and computer science have truly transformed the study of semantic memory. This paper reviewed classic and modern models of semantic memory that have attempted to provide explicit accounts of how semantic knowledge may be acquired, maintained, and used in cognitive tasks to guide behavior. Table 1 presents a short summary of the different types of models discussed in this review, along with their basic underlying mechanisms.

By computing errors bidirectionally and updating the position and attention vectors with each iteration, BERT’s word vectors are influenced by other words’ vectors and tend to develop contextually dependent word embeddings. For example, the representation of the word ostrich in the BERT model would be different when it is in a sentence about birds (e.g., ostriches and emus are large birds) versus food (ostrich eggs can be used to make omelets), due to the different position and attention vectors contributing to these two representations. Importantly, the architecture of BERT allows it to be flexibly finetuned and applied to any semantic task, while still using the basic attention-based mechanism. However, considerable work is beginning to evaluate these models using more rigorous test cases and starting to question whether these models are actually learning anything meaningful (e.g., Brown et al., 2020; Niven & Kao, 2019), an issue that is discussed in detail in Section V.

The other two sub-categories of image segmentation are instance segmentation and panoptic segmentation. Robots need to map and interpret the scene they are viewing in order to do their job more efficiently. The pixel-level understanding provided by semantic segmentation helps robots better navigate the workspace. Many fields of robotics can benefit from image segmentation, from service robots and industrial robots to agricultural robots. Semantic segmentation is a type of segmentation that treats multiple objects of the same type or class as a single entity. For example, semantic segmentation might indicate the pixel boundaries of all the people in the image, or all the cars in the image.

For example, “cows flow supremely” is grammatically valid (subject — verb — adverb) but it doesn’t make any sense. However, Table 1 provides a succinct summary of the key models discussed in this review. Chatbots help customers immensely as they facilitate shipping, answer queries, and also offer personalized guidance and input on how to proceed further. Moreover, some chatbots are equipped with emotional intelligence that recognizes the tone of the language and hidden sentiments, framing emotionally-relevant responses to them.

Computer Science > Computation and Language

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. These assistants are a form of conversational AI that can carry on more sophisticated discussions. And if NLP is unable to resolve an issue, it can connect a customer with the appropriate personnel. In the form of chatbots, natural language processing can take some of the weight off customer service teams, promptly responding to online queries and redirecting customers when needed. NLP can also analyze customer surveys and feedback, allowing teams to gather timely intel on how customers feel about a brand and steps they can take to improve customer sentiment. Now that we’ve learned about how natural language processing works, it’s important to understand what it can do for businesses.

Using a low-code UI, you can create models to automatically analyze your text for semantics and perform techniques like sentiment and topic analysis, or keyword extraction, in just a few simple steps. 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 problem with FCN is that the resolution of the output feature map is downsampled by propagating through multiple alternating convolutional and pooling layers. Therefore, FCN predictions are usually performed at low resolution, and object boundaries tend to be blurry. To solve this problem, advanced FCN-based methods have been proposed, including SegNet and DeepLab-CRF.

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.

Recommenders and Search Tools

The encoder-decoder-based, i.e., Fully transformer-based network models, are very popular, and they also give us promising result [80]. Modern research has applied the fully transformer-based architectures, and some adopted the CNN-based semantic segmentation model. Moreover, the hybrid network models are also a practical approach to solve these problems [95]. Semantic segmentation tasks help machines distinguish the different object classes and background regions in an image.

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). Collectively, this research indicates that modeling the sentence structure through NN models and recursively applying composition functions can indeed produce compositional semantic representations that are achieving state-of-the-art performance in some semantic tasks. It is the basic technique to get some meaningful information to extract the required [7, 53, 92]. After getting the information, you can mention that certain specific objects belong to specific classes [9, 59, 81]. It has many more diverse applications in every field like education, medicine, weather prediction, climatic change prediction, and so on [4]. Deep learning-based segmentation methods have been widely used in other vision tasks such as video object segmentation [53,54,55, 83].

Modern approaches to modeling the representational nature of semantic memory have come very far in describing the continuum in which meaning exists, i.e., from the lowest-level input in the form of sensory and perceptual information, to words that form the building blocks of language, to high-level structures like schemas and events. However, process models operating on these underlying semantic representations have not received the same kind of attention and have developed somewhat independently from the representation modeling movement. Ultimately, combining process-based accounts with representational accounts is going to be critical in addressing some of the current challenges in the field, an issue that is emphasized in the final section of this review. Another important aspect of language learning is that humans actively learn from each other and through interactions with their social counterparts, whereas the majority of computational language models assume that learners are simply processing incoming information in a passive manner (Günther et al., 2019). Indeed, there is now ample evidence to suggest that language evolved through natural selection for the purposes of gathering and sharing information (Pinker, 2003, p. 27; DeVore & Tooby, 1987), thereby allowing for personal experiences and episodic information to be shared among humans (Corballis, 2017a, 2017b).

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).

This led to a series of rebuttals from both camps (Jones, Hills, & Todd, 2015; Nematzadeh, Miscevic, & Stevenson, 2016), and continues to remain an open debate in the field (Avery & Jones, 2018). However, Jones, Hills, and Todd (2015) argued that while free-association norms are a useful proxy for memory representation, they remain an outcome variable from a search process on a representation and cannot be a pure measure of how semantic memory is organized. Indeed, Avery and Jones (2018) showed that when the input to the network and distributional space was controlled (i.e., both were constructed from text corpora), random walk and foraging-based models both explained semantic fluency data, although the foraging model outperformed several different random walk models. Of course, these findings are specific to the semantic fluency task and adequately controlled comparisons of network models to DSMs remain limited.

One important observation from this work is that the debate is less about the underlying structure (network-based/localist or distributed) and more about the input contributing to the resulting structure. Networks and feature lists in and of themselves are simply tools to represent a particular set of data, similar to high-dimensional vector spaces. As such, cosines in vector spaces can be converted to step-based distances that form a network using cosine thresholds (e.g., Gruenenfelder, Recchia, Rubin, & Jones, 2016; Steyvers & Tenenbaum, 2005) or a binary list of features (similar to “dimensions” in DSMs). Therefore, the critical difference between associative networks/feature-based models and DSMs is not that the former is a network/list and the latter is a vector space, but rather the fact that associative networks are constructed from free-association responses, feature-based models use property norms, and DSMs learn from text corpora. Therefore, as discussed earlier, the success of associative networks (or feature-based models) in explaining behavioral performance in cognitive tasks could be a consequence of shared variance with the cognitive tasks themselves.

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. 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.

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. Therefore, it remains unclear how the mechanisms proposed by these models compare to the language acquisition and representation processes in humans, although subsequent sections make the case that recent attempts towards incorporating multimodal information, and temporal and attentional influences are making significant strides in this direction. 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. You can foun additiona information about ai customer service and artificial intelligence and NLP. 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. Specifically, two distinct psychological mechanisms have been proposed to account for associative learning, broadly referred to as error-free and error-driven learning mechanisms. This Hebbian learning mechanism is at the heart of several classic and recent models of semantic memory, which are discussed in this section.

The skater receives a ___”, the network activated the words podium and medal after the fourth sentence (“The skater receives a”) because both of these are contextually appropriate (receiving an award at the podium and receiving a medal), although medal was more activated than podium as it was more appropriate within that context. This behavior of the model was semantic techniques strikingly consistent with N400 amplitudes observed for the same types of sentences in an ERP study (Metusalem et al., 2012), indicating that the model was able to make predictive inferences like human participants. More recently, Jamieson, Avery, Johns, and Jones et al. (2018) proposed an instance-based theory of semantic memory, also based on MINERVA 2.

Recent computational network models have supported this conceptualization of semantic memory as an associative network. More recently, Kumar, Balota, and Steyvers (2019) replicated Kenett et al.’s work in a much larger corpus in English, and also showed that undirected and directed networks created by Steyvers and Tenenbaum (2005) also account for such distant priming effects. Computational network-based models of semantic memory have gained significant traction in the past decade, mainly due to the recent popularity of graph theoretical and network-science approaches to modeling cognitive processes (for a review, see Siew, Wulff, Beckage, & Kenett, 2018). Modern network-based approaches use large-scale databases to construct networks and capture large-scale relationships between nodes within the network. This approach has been used to empirically study the World Wide Web (Albert, Jeong, & Barabási, 2000; Barabási & Albert, 1999), biological systems (Watts & Strogatz, 1998), language (Steyvers & Tenenbaum, 2005; Vitevitch, Chan, & Goldstein, 2014), and personality and psychological disorders (for reviews, see Fried et al., 2017). Within the study of semantic memory, Steyvers and Tenenbaum (2005) pioneered this approach by constructing three different semantic networks using large-scale free-association norms (Nelson, McEvoy, & Schreiber, 2004), Roget’s Thesaurus (Roget, 1911), and WordNet (Fellbaum, 1998; Miller, 1995).

Moreover, with the ability to capture the context of user searches, the engine can provide accurate and relevant results. These chatbots act as semantic analysis tools that are enabled with keyword recognition and conversational capabilities. These tools help resolve customer problems in minimal time, thereby increasing customer satisfaction. Semantic analysis methods will provide companies the ability to understand the meaning of the text and achieve comprehension and communication levels that are at par with humans. Thus, as and when a new change is introduced on the Uber app, the semantic analysis algorithms start listening to social network feeds to understand whether users are happy about the update or if it needs further refinement.

With the rise of artificial intelligence (AI) and machine learning (ML), image segmentation and the creation of segmentation maps play an important role in training computers to recognize important context in digital images such as landscapes, photos of people, medical images and much more. The original FCN model is able to learn pixel-to-pixel mapping without extracting region proposals. An extension of this model is the FCN network pipeline, which allows existing CNNs to use arbitrary-sized images as input. This made possible because, unlike in a traditional CNN that ends with a fully-connected network with fixed layers, an FCN only has convolutional and pooling layers. The ability to flexibly process images of any size makes FCNs applicable to semantic segmentation tasks. We’ll discuss three segmentation techniques – region-based semantic segmentation, segmentation based on fully convolutional networks (FCN), and weakly supervised semantic segmentation.