Comparative Analysis of NLP-Based Models for Company Classification

The goal of this subevent-based VerbNet representation was to facilitate inference and textual entailment tasks. Similarly, Table 1 shows the ESL of the verb arrive, compared with the semantic frame of the verb in classic VerbNet. Different kinds of linguistic information have been analyzed, ranging from basic properties like sentence length, word position, word presence, or simple word order, to morphological, syntactic, and semantic information. Phonetic/phonemic information, speaker information, and style and accent information have been studied in neural network models for speech, or in joint audio-visual models. Natural language processing brings together linguistics and algorithmic models to analyze written and spoken human language.

• Several standards and corpora that exist in the general domain, e.g. the Brown Corpus and Penn Treebank tag sets for POS-tagging, have been adapted for the clinical domain.
• For instance, the MCORES system employs a rich feature set with a decision tree algorithm, outperforming unweighted average F1 results compared to existing open-domain systems on the semantic types Test (84%), Persons (84%), Problems (85%) and Treatments (89%) [58].
• Moreover, the system can prioritize or flag urgent requests and route them to the respective customer service teams for immediate action with semantic analysis.
• Such initiatives are of great relevance to the clinical NLP community and could be a catalyst for bridging health care policy and practice.

Moreover, QuestionPro typically provides visualization tools and reporting features to present survey data, including textual responses. These visualizations help identify trends or patterns within the unstructured text data, supporting the interpretation of semantic aspects to some extent. Chatbots, virtual assistants, and recommendation systems benefit from semantic analysis by providing more accurate and context-aware responses, thus significantly improving user satisfaction. The meaning representation can be used to reason for verifying what is correct in the world as well as to extract the knowledge with the help of semantic representation.

Named Entity Recognition and Contextual Analysis

Recently, Kazeminejad et al. (2022) has added verb-specific features to many of the VerbNet classes, offering an opportunity to capture this information in the semantic representations. These features, which attach specific values to verbs in a class, essentially subdivide the classes into more specific, semantically coherent subclasses. For example, verbs in the admire-31.2 class, which range from loathe and dread to adore and exalt, have been assigned a +negative_feeling or +positive_feeling attribute, as applicable. With the aim of improving the semantic specificity of these classes and capturing inter-class connections, we gathered a set of domain-relevant predicates and applied them across the set. Authority_relationship shows a stative relationship dynamic between animate participants, while has_organization_role shows a stative relationship between an animate participant and an organization.

It makes the customer feel “listened to” without actually having to hire someone to listen. Semantic analysis enables these systems to comprehend user queries, leading to more accurate responses and better conversational experiences. This technique is used separately or can be used along with one of the above methods to gain more valuable insights.

Why Is Semantic Analysis Important to NLP?

Often, the adversarial examples are inspired by text edits that are thought to be natural or commonly generated by humans, such as typos, misspellings, and so on (Sakaguchi et al., 2017; Heigold et al., 2018; Belinkov and Bisk, 2018). Their functions do not require access to model internals, but they do require the model prediction score. After identifying the important tokens, they modify characters with common edit operations. Generally, datasets that are constructed programmatically tend to cover less fine-grained linguistic properties, while manually constructed datasets represent more diverse phenomena. The automated process of identifying in which sense is a word used according to its context.

By far the most common event types were the first four, all of which involved some sort of change to one or more participants in the event. We developed a basic first-order-logic representation that was consistent with the GL theory of subevent structure and that could be adapted for the various types of change events. We preserved existing semantic predicates where possible, but more fully defined them and their arguments and applied them consistently across classes.

2 Linguistic Phenomena

Predicates within a cluster frequently appear in classes together, or they may belong to related classes and exist along a continuum with one another, mirror each other within narrower domains, or exist as inverses of each other. For example, we have three predicates that describe degrees of physical integration with implications for the permanence of the state. Together is most general, used for co-located items; attached represents adhesion; and mingled indicates that the constituent parts of the items are intermixed to the point that they may not become unmixed. Spend and spend_time mirror one another within sub-domains of money and time, and in fact, this distinction is the critical dividing line between the Consume-66 and Spend_time-104 classes, which contain the same syntactic frames and many of the same verbs. Similar class ramifications hold for inverse predicates like encourage and discourage.

The first technique refers to text classification, while the second relates to text extractor. Apart from these vital elements, the semantic analysis also uses semiotics and collocations to understand and interpret language. Semiotics refers to what the word means and also the meaning it evokes or communicates.

In adversarial image examples, it is fairly straightforward to measure the perturbation, either by measuring distance in pixel space, say ||x − x′|| under some norm, or with alternative measures that are better correlated with human perception (Rozsa et al., 2016). It is also visually compelling to present an adversarial image with imperceptible difference from its source image. In the text domain, measuring distance is not as straightforward, and even small changes to the text may be perceptible by humans. Some studies imposed constraints on adversarial examples to have a small number of edit operations (Gao et al., 2018). Most of the work on adversarial text examples involves modifications at the character- and/or word-level; see Table SM3 for specific references. Other transformations include adding sentences or text chunks (Jia and Liang, 2017) or generating paraphrases with desired syntactic structures (Iyyer et al., 2018).

• The values in 𝚺 represent how much each latent concept explains the variance in our data.
• Another tool focused on comparing attention alignments was proposed by Rikters (2018).
• The authors concluded that the NMT encoders learn significant syntactic information at both word level and sentence level.
• It is also a key component of several machine learning tools available today, such as search engines, chatbots, and text analysis software.
• These sites provide an unprecedented opportunity to monitor population-level health and well-being, e.g., detecting infectious disease outbreaks, monitoring depressive mood and suicide in high-risk populations, etc.
• However, there is still a gap between the development of advanced resources and their utilization in clinical settings.

Semantic analysis stands as the cornerstone in navigating the complexities of unstructured data, revolutionizing how computer science approaches language comprehension. Its prowess in both lexical semantics and syntactic analysis enables the extraction of invaluable insights from diverse sources. Semantic analysis significantly improves language understanding, enabling machines to process, analyze, and generate text with greater accuracy and context sensitivity. Indeed, semantic analysis is pivotal, fostering better user experiences and enabling more efficient information retrieval and processing. Semantic analysis forms the backbone of many NLP tasks, enabling machines to understand and process language more effectively, leading to improved machine translation, sentiment analysis, etc.

It also made the job of tracking participants across subevents much more difficult for NLP applications. Understanding that the statement ‘John dried the clothes’ entailed that the clothes began in a wet state would require that systems infer the initial state of the clothes from our representation. By including that initial state in the representation explicitly, we eliminate the need for real-world knowledge or inference, an NLU task that is notoriously difficult. A number of studies evaluated the effect of erasing or masking certain neural network components, such as word embedding dimensions, hidden units, or even full words (Li et al., 2016b; Feng et al., 2018; Khandelwal et al., 2018; Bau et al., 2018). For example, Li et al. (2016b) erased specific dimensions in word embeddings or hidden states and computed the change in probability assigned to different labels.

In image captioning, Chen et al. (2018a) modified pixels in the input image to generate targeted attacks on the caption text. Zhao et al. (2018c) used generative adversarial networks (GANs) (Goodfellow et al., 2014) to minimize the semantic analysis nlp distance between latent representations of input and adversarial examples, and performed perturbations in latent space. Since the latent representations do not need to come from the attacked model, this is a black-box attack.