Semantic Content Analysis Natural Language Processing SpringerLink

In the above sentence, the speaker is talking either about Lord Ram or about a person whose name is Ram. 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. In-Text Classification, our aim is to label the text according to the insights we intend to gain from the textual data.

• Rospocher et al. [112] purposed a novel modular system for cross-lingual event extraction for English, Dutch, and Italian Texts by using different pipelines for different languages.
• Moreover, the pairs of sentences with a semantic similarity exceeding 80% (within the 80–100% range) are counted as 6,927 pairs, approximately constituting 78% of the total amount of sentence pairs.
• Several systems and studies have also attempted to improve PHI identification while addressing processing challenges such as utility, generalizability, scalability, and inference.
• These variations, along with the high frequency of core concepts in the translations, directly contribute to differences in semantic representation across different translations.
• A statistical parser originally developed for German was applied on Finnish nursing notes [38].

But in first model a document is generated by first choosing a subset of vocabulary and then using the selected words any number of times, at least once without any order. It takes the information of which words are used in a document irrespective of number of words and order. In second model, a document is generated by choosing a set of word occurrences and arranging them in any order. This model is called multi-nominal model, in addition to the Multi-variate Bernoulli model, it also captures information on how many times a word is used in a document. The natural language processing involves resolving different kinds of ambiguity.

Languages

Anggraeni et al. (2019) [61] used ML and AI to create a question-and-answer system for retrieving information about hearing loss. 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. The extracted information can be applied for a variety of purposes, for example to prepare a summary, to build databases, identify keywords, classifying text items according to some pre-defined categories etc. For example, CONSTRUE, it was developed for Reuters, that is used in classifying news stories (Hayes, 1992) [54].

Pustejovsky and Stubbs present a full review of annotation designs for developing corpora [10]. Seunghak et al. [158] designed a Memory-Augmented-Machine-Comprehension-Network (MAMCN) to handle dependencies faced in reading comprehension. The model achieved state-of-the-art performance on document-level using TriviaQA and QUASAR-T datasets, and paragraph-level using SQuAD datasets. A strong grasp of semantic analysis helps firms improve their communication with customers without needing to talk much.

Lexical level ambiguity refers to ambiguity of a single word that can have multiple assertions. Each of these levels can produce ambiguities that can be solved by the knowledge of the complete sentence. The ambiguity can be solved by various methods such as Minimizing Ambiguity, Preserving Ambiguity, Interactive Disambiguation and Weighting Ambiguity [125]. Some of the methods proposed by researchers to remove ambiguity is preserving ambiguity, e.g. (Shemtov 1997; Emele & Dorna 1998; Knight & Langkilde 2000; Tong Gao et al. 2015, Umber & Bajwa 2011) [39, 46, 65, 125, 139]. They cover a wide range of ambiguities and there is a statistical element implicit in their approach. There we can identify two named entities as “Michael Jordan”, a person and “Berkeley”, a location.

Natural Language Processing and Network Analysis to Develop a Conceptual Framework for Medication Therapy Management Research describes a theory derivation process that is used to develop a conceptual framework for medication therapy management (MTM) research. The MTM service model and chronic care model are selected as parent theories. Review article abstracts target medication therapy management in chronic disease care that were retrieved from Ovid Medline (2000–2016). Unique concepts in each abstract are extracted using Meta Map and their pair-wise co-occurrence are determined. Then the information is used to construct a network graph of concept co-occurrence that is further analyzed to identify content for the new conceptual model. Medication adherence is the most studied drug therapy problem and co-occurred with concepts related to patient-centered interventions targeting self-management.

A company can scale up its customer communication by using semantic analysis-based tools. It could be BOTs that act as doorkeepers or even on-site semantic search engines. By allowing customers to “talk freely”, without binding up to a format – a firm can gather significant volumes of quality data. 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.

They further provide valuable insights into the characteristics of different translations and aid in identifying potential errors. By delving deeper into the reasons behind this substantial difference in semantic similarity, this study can enable readers to gain a better understanding of the text of The Analects. Furthermore, this analysis can guide translators in selecting words more judiciously for crucial core conceptual words during the translation process. Utility of clinical texts can be affected when clinical eponyms such as disease names, treatments, and tests are spuriously redacted, thus reducing the sensitivity of semantic queries for a given use case.

Automated semantic analysis works with the help of machine learning algorithms. Table 8c displays the occurrence of words denoting personal names in The Analects, including terms such as “zi, Tsz, Tzu, Lu, Yu,” and “Kung.” These terms can appear individually or in combination with other words and often represent important characters within the text. The translation of these personal names exerts considerable influence over the variations in meaning among different translations, as the interpretation of these names may vary among translators. The translation of The Analects contains several common words, often referred to as “stop words” in the field of Natural Language Processing (NLP). These words, such as “the,” “to,” “of,” “is,” “and,” and “be,” are typically filtered out during data pre-processing due to their high frequency and low semantic weight.

The relevant work done in the existing literature with their findings and some of the important applications and projects in NLP are also discussed in the paper. The last two objectives may serve as a literature survey for the readers already working in the NLP and relevant fields, and further can provide motivation to explore the fields mentioned in this paper. 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.

This study designates these sentence pairs containing “None” as Abnormal Results, aiding in the identification of translators’ omissions. These outliers scores are not employed in the subsequent semantic similarity analyses. To enable cross-lingual semantic analysis of clinical documentation, a first important step is to understand differences and similarities between clinical texts from different countries, written in different languages. Wu et al. [78], perform a qualitative and statistical comparison of discharge summaries from China and three different US-institutions.

NLP: Then and now

As delineated in Section 2.1, all aberrant outcomes listed in the above table are attributable to pairs of sentences marked with “None,” indicating untranslated sentences. When the Word2Vec and BERT algorithms are applied, sentences containing “None” typically yield low values. The GloVe embedding model was incapable of generating a similarity score for these sentences.

Semantic analysis is one of the main goals of clinical NLP research and involves unlocking the meaning of these texts by identifying clinical entities (e.g., patients, clinicians) and events (e.g., diseases, treatments) and by representing relationships among them. There has been an increase of advances within key NLP subtasks that support semantic analysis. Performance of NLP semantic analysis is, in many cases, close to that of agreement between humans. The creation and release of corpora annotated with complex semantic information models has greatly supported the development of new tools and approaches. NLP methods have sometimes been successfully employed in real-world clinical tasks. However, there is still a gap between the development of advanced resources and their utilization in clinical settings.

As we discussed, the most important task of semantic analysis is to find the proper meaning of the sentence. This article is part of an ongoing blog series on Natural Language Processing (NLP). 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, and see all the important terminologies or concepts in this analysis. Expert.ai’s rule-based technology starts by reading all of the words within a piece of content to capture its real meaning.

In recent years, the clinical NLP community has made considerable efforts to overcome these barriers by releasing and sharing resources, e.g., de-identified clinical corpora, annotation guidelines, and NLP tools, in a multitude of languages [6]. The development and maturity of NLP systems has also led to advancements in the semantic analysis in natural language processing employment of NLP methods in clinical research contexts. Information extraction is concerned with identifying phrases of interest of textual data. For many applications, extracting entities such as names, places, events, dates, times, and prices is a powerful way of summarizing the information relevant to a user’s needs.

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. With the Internet of Things and other advanced technologies compiling more data than ever, some data sets are simply too overwhelming for humans to comb through. Natural language processing can quickly process massive volumes of data, gleaning insights that may have taken weeks or even months for humans to extract. Named entity recognition (NER) concentrates on determining which items in a text (i.e. the “named entities”) can be located and classified into predefined categories. These categories can range from the names of persons, organizations and locations to monetary values and percentages.

HMM is not restricted to this application; it has several others such as bioinformatics problems, for example, multiple sequence alignment [128]. Sonnhammer mentioned that Pfam holds multiple alignments and hidden Markov model-based profiles (HMM-profiles) of entire protein domains. The cue of domain boundaries, family members and alignment are done semi-automatically found on expert knowledge, sequence similarity, other protein family databases and the capability of HMM-profiles to correctly identify and align the members. HMM may be used for a variety of NLP applications, including word prediction, sentence production, quality assurance, and intrusion detection systems [133]. NLU enables machines to understand natural language and analyze it by extracting concepts, entities, emotion, keywords etc.

As delineated in the introduction section, a significant body of scholarly work has focused on analyzing the English translations of The Analects. However, the majority of these studies often omit the pragmatic considerations needed to deepen readers’ understanding of The Analects. Given the current findings, achieving a comprehensive understanding of The Analects’ translations requires considering both readers’ and translators’ perspectives. The table presented above reveals marked differences in the translation of these terms among the five translators. These disparities can be attributed to a variety of factors, including the translators’ intended audience, the cultural context at the time of translation, and the unique strategies each translator employed to convey the essence of the original text. The term “?? Jun Zi,” often translated as “gentleman” or “superior man,” serves as a typical example to further illustrate this point regarding the translation of core conceptual terms.

Privacy protection regulations that aim to ensure confidentiality pertain to a different type of information that can, for instance, be the cause of discrimination (such as HIV status, drug or alcohol abuse) and is required to be redacted before data release. This type of information is inherently semantically complex, as semantic inference can reveal a lot about the redacted information (e.g. The patient suffers from XXX (AIDS) that was transmitted because of an unprotected sexual intercourse). Following the pivotal release of the 2006 de-identification schema and corpus by Uzuner et al. [24], a more-granular schema, an annotation guideline, and a reference standard for the heterogeneous MTSamples.com corpus of clinical texts were released [14]. The reference standard is annotated for these pseudo-PHI entities and relations. To date, few other efforts have been made to develop and release new corpora for developing and evaluating de-identification applications. 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.

With the help of semantic analysis, machine learning tools can recognize a ticket either as a “Payment issue” or a“Shipping problem”. Now that we’ve learned about how natural language processing works, it’s important to understand what it can do for businesses. However, machines first need to be trained to make sense of human language and understand the context in which words are used; otherwise, they might misinterpret the word “joke” as positive. In other words, word frequencies in different documents play a key role in extracting the latent topics. LSA tries to extract the dimensions using a machine learning algorithm called Singular Value Decomposition or SVD.

As translation studies have evolved, innovative analytical tools and methodologies have emerged, offering deeper insights into textual features. Among these methods, NLP stands out for its potent ability to process and analyze human language. Within digital humanities, merging NLP with traditional studies on The Analects translations can offer more empirical and unbiased insights into inherent textual features. This integration establishes a new paradigm in translation research and broadens the scope of translation studies. Natural language processing (NLP) has recently gained much attention for representing and analyzing human language computationally. It has spread its applications in various fields such as machine translation, email spam detection, information extraction, summarization, medical, and question answering etc.

It makes the customer feel “listened to” without actually having to hire someone to listen. In Sentiment analysis, our aim is to detect the emotions as positive, negative, or neutral in a text to denote urgency. It represents the general category of the individuals such as a person, city, etc.

Generalizability is a challenge when creating systems based on machine learning. In particular, systems trained and tested on the same document type often yield better performance, but document type information is not always readily available. Wiese et al. [150] introduced a deep learning approach based on domain adaptation techniques for handling biomedical question answering tasks. Their model revealed the state-of-the-art performance on biomedical question answers, and the model outperformed the state-of-the-art methods in domains. It is the first part of semantic analysis, in which we study the meaning of individual words. It involves words, sub-words, affixes (sub-units), compound words, and phrases also.

The profound ideas it presents retain considerable relevance and continue to exert substantial influence in modern society. The availability of over 110 English translations reflects the significant demand among English-speaking readers. Grasping the unique characteristics of each translation is pivotal for guiding future translators and assisting readers in making informed selections. This research builds a corpus from translated texts of The Analects and quantifies semantic similarity at the sentence level, employing natural language processing algorithms such as Word2Vec, GloVe, and BERT. The findings highlight semantic variations among the five translations, subsequently categorizing them into “Abnormal,” “High-similarity,” and “Low-similarity” sentence pairs.

All modules take standard input, to do some annotation, and produce standard output which in turn becomes the input for the next module pipelines. Their pipelines are built as a data centric architecture so that modules can be adapted and replaced. Furthermore, modular architecture allows for different configurations and for dynamic distribution. This study ingeniously integrates natural language processing technology into translation research.

Dissecting The Analects: an NLP-based exploration of semantic similarities and differences across English translations

They are useful in law firms, medical record segregation, segregation of books, and in many different scenarios. Clustering algorithms are usually meant to deal with dense matrix and not sparse matrix which is created during the creation of document term matrix. Using LSA, a low-rank approximation of the original matrix can be created (with some loss of information although!) that can be used for our clustering purpose.

A comparison of sentence pairs with a semantic similarity of ? 80% reveals that these core conceptual words significantly influence the semantic variations among the translations of The Analects. The second category includes various personal names mentioned in The Analects. Our analysis suggests that the distinct translation methods of the five translators for these names significantly contribute to the observed semantic differences, likely stemming from different interpretation or localization strategies. Through the analysis of our semantic similarity calculation data, this study finds that there are some differences in the absolute values of the results obtained by the three algorithms. Several factors, such as the differing dimensions of semantic word vectors used by each algorithm, could contribute to these dissimilarities.

The data presented in Table 2 elucidates that the semantic congruence between sentence pairs primarily resides within the 80–90% range, totaling 5,507 such instances. Moreover, the pairs of sentences with a semantic similarity exceeding 80% (within the 80–100% range) are counted as 6,927 pairs, approximately constituting 78% of the total amount of sentence pairs. This forms the major component of all results in the semantic similarity calculations. Most of the semantic similarity between the sentences of the five translators is more than 80%, this demonstrates that the main body of the five translations captures the semantics of the original Analects quite well. In order to employ NLP methods for actual clinical use-cases, several factors need to be taken into consideration. Many (deep) semantic methods are complex and not easy to integrate in clinical studies, and, if they are to be used in practical settings, need to work in real-time.

For example, you might decide to create a strong knowledge base by identifying the most common customer inquiries. For this tutorial, we are going to use the BBC news data which can be downloaded from here. This dataset contains raw texts related to 5 different categories such as business, entertainment, politics, sports, and tech. Finally, with the rise of the internet and of online marketing of non-traditional therapies, patients are looking to cheaper, alternative methods to more traditional medical therapies for disease management.

The sentiment is mostly categorized into positive, negative and neutral categories. Considering the aforementioned statistics and the work of these scholars, it is evident that the translation of core conceptual terms and personal names plays a significant role in shaping the semantic expression of The Analects in English. This study obtains high-resolution PDF versions of the five English translations of The Analects through purchase and download. The first step entailed establishing preprocessing parameters, which included eliminating special symbols, converting capitalized words to lowercase, and sequentially reading the PDF file whilst preserving the English text. Subsequently, this study aligned the cleaned texts of the translations by Lau, Legge, Jennings, Slingerland, and Watson at the sentence level to construct a parallel corpus. The original text of The Analects was segmented using a method that divided it into 503 sections based on natural section divisions.

Linguistics is the science of language which includes Phonology that refers to sound, Morphology word formation, Syntax sentence structure, Semantics syntax and Pragmatics which refers to understanding. Noah Chomsky, one of the first linguists of twelfth century that started syntactic theories, marked a unique position in the field of theoretical linguistics because he revolutionized the area of syntax (Chomsky, 1965) [23]. Further, Natural Language Generation (NLG) is the process of producing phrases, sentences and paragraphs that are meaningful from an internal representation.

The following codes show how to create the document-term matrix and how LSA can be used for document clustering. Table 7 provides a representation that delineates the ranked order of the high-frequency words extracted from the text. This visualization aids in identifying the most critical and recurrent themes or concepts within the translations. Furthermore, with growing internet and social media use, social networking sites such as Facebook and Twitter have become a new medium for individuals to report their health status among family and friends. 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.

• Often, these tasks are on a high semantic level, e.g. finding relevant documents for a specific clinical problem, or identifying patient cohorts.
• Finally, it analyzes the surrounding text and text structure to accurately determine the proper meaning of the words in context.
• One de-identification application that integrates both machine learning (Support Vector Machines (SVM), and Conditional Random Fields (CRF)) and lexical pattern matching (lexical variant generation and regular expressions) is BoB (Best-of-Breed) [25-26].
• This forms the major component of all results in the semantic similarity calculations.

For example, the word “Bat” is a homonymy word because bat can be an implement to hit a ball or bat is a nocturnal flying mammal also. Tickets can be instantly routed to the right hands, and urgent issues can be easily prioritized, shortening response times, and keeping satisfaction levels high. Semantic analysis also takes into account signs and symbols (semiotics) and collocations (words that often go together). You understand that a customer is frustrated because a customer service agent is taking too long to respond. You can foun additiona information about ai customer service and artificial intelligence and NLP. So, if you have a reasonably large text corpus, you should get a good result.

It has been suggested that many IE systems can successfully extract terms from documents, acquiring relations between the terms is still a difficulty. PROMETHEE is a system that extracts lexico-syntactic patterns relative to a specific conceptual relation (Morin,1999) [89]. 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. In the late 1940s the term NLP wasn’t in existence, but the work regarding machine translation (MT) had started.

Many of the most recent efforts in this area have addressed adaptability and portability of standards, applications, and approaches from the general domain to the clinical domain or from one language to another language. Naive Bayes is a probabilistic algorithm which is based on probability theory and Bayes’ Theorem to predict the tag of a text such as news or customer review. 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.

There are a multitude of languages with different sentence structure and grammar. 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. In recent years, various methods have been proposed to automatically evaluate machine translation quality by comparing hypothesis translations with reference translations. For readers, the core concepts in The Analects transcend the meaning of single words or phrases; they encapsulate profound cultural connotations that demand thorough and precise explanations. For instance, whether “?? Jun Zi” is translated as “superior man,” “gentleman,” or otherwise.

Another approach deals with the problem of unbalanced data and defines a number of linguistically and semantically motivated constraints, along with techniques to filter co-reference pairs, resulting in an unweighted average F1 of 89% [59]. To fully represent meaning from texts, several additional layers of information can be useful. Such layers can be complex and comprehensive, or focused on specific semantic problems. Bi-directional Encoder Representations from Transformers (BERT) is a pre-trained model with unlabeled text available on BookCorpus and English Wikipedia. This can be fine-tuned to capture context for various NLP tasks such as question answering, sentiment analysis, text classification, sentence embedding, interpreting ambiguity in the text etc. [25, 33, 90, 148].

Peter Wallqvist, CSO at RAVN Systems commented, “GDPR compliance is of universal paramountcy as it will be exploited by any organization that controls and processes data concerning EU citizens. In the case of syntactic analysis, the syntax of a sentence is used to interpret a text. In the case of semantic analysis, the overall context of the text is considered during the analysis. Using Syntactic analysis, a computer would be able to understand the parts of speech of the different words in the sentence. Based on the understanding, it can then try and estimate the meaning of the sentence. In the case of the above example (however ridiculous it might be in real life), there is no conflict about the interpretation.

Thus, from a sparse document-term matrix, it is possible to get a dense document-aspect matrix that can be used for either document clustering or document classification using available ML tools. The V matrix, on the other hand, is the word embedding matrix (i.e. each and every word is expressed by r floating-point numbers) and this matrix can be used in other sequential modeling tasks. However, for such tasks, Word2Vec and Glove vectors are available which are more popular. The x-axis represents the sentence numbers from the corpus, with sentences taken as an example due to space limitations.

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. ” 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.