LitCoin Natural Language Processing NLP Challenge National Center for Advancing Translational Sciences

Ambiguity in LanguageHuman language’s ambiguity significantly challenges NLP systems. Resolving pronouns and other referring expressions to their antecedents (coreference resolution) is vital for coherent understanding of text. However, the ambiguity and complexity of reference relationships pose challenges for accurate coreference resolution. Developing algorithms that effectively capture and resolve coreference is an ongoing research area. Organizations can infuse the power of NLP into their digital solutions by leveraging user-friendly generative AI platforms such as IBM Watson NLP Library for Embed, a containerized library designed to empower IBM partners with greater AI capabilities. Developers can access and integrate it into their apps in their environment of their choice to create enterprise-ready solutions with robust AI models, extensive language coverage and scalable container orchestration.

The field of Natural Language Processing (NLP) has witnessed significant advancements, yet it continues to face notable challenges and considerations. These obstacles not only highlight the complexity of human language but also underscore the need for nlp challenges careful and responsible development of NLP technologies. For these synergies to happen it is necessary to create spaces that allow humanitarians, academics, ethicists, and open-source contributors from diverse backgrounds to interact and experiment.

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Interviews, surveys, and focus group discussions are conducted regularly to better understand the needs of affected populations. Alongside these “internal” sources of linguistic data, social media data and news media articles also convey information that can be used to monitor and better understand humanitarian crises. People make extensive use of social media platforms like Twitter and Facebook in the context of natural catastrophes and complex political crises, and news media often convey information on crisis-related drivers and events. The resource availability for English has prompted the use of machine translation as a way to address resource sparsity in other languages. Google translate, were found to have the potential to reduce language bias in the preparation of randomized clinical trials reports language pairs [154]. However, it was shown to be of little help to render medical record content more comprehensible to patients [155].

CrisisBench is a benchmark dataset including social media text labeled along dimensions relevant for humanitarian action (Alam et al., 2021). You can foun additiona information about ai customer service and artificial intelligence and NLP. This dataset contains collections of tweets from multiple major natural disasters, labeled by relevance, intent (offering vs. requesting aid), and sector of interest. Lacuna Fund13 is an initiative that aims at increasing availability of unbiased labeled datasets from low- or middle-income contexts. Tools like AIDR (Imran et al., 2014) and MicroMappers—a platform and a crowdsourcing initiative for collection and annotation of social media datasets—were created with the intent of supporting social media analysis for humanitarian applications, but they are no longer maintained.

Data Redaction

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]. Using these approaches is better as classifier is learned from training data rather than making by hand. The naïve bayes is preferred because of its performance despite its simplicity (Lewis, 1998) [67] In Text Categorization two types of models have been used (McCallum and Nigam, 1998) [77]. 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 irrespective of order.

Humanitarian assistance can be provided in many forms and at different spatial (global and local) and temporal (before, during, and after crises) scales. The specifics of the humanitarian ecosystem and of its response mechanisms vary widely from crisis to crisis, but larger organizations have progressively developed fairly consolidated governance, funding, and response frameworks. In the interest of brevity, we will mainly focus on response frameworks revolving around the United Nations, but it is important to keep in mind that this is far from being an exhaustive account of how humanitarian aid is delivered in practice. In summary, the level of difficulty to build a clinical NLP application depends on various factors including the difficulty of the task itself and constraints linked to software design. Legacy systems can be difficult to adapt if they were not originally designed with a multi-language purpose. Lexicons, terminologies and annotated corpora While the lack of language specific resources is sometimes addressed by investigating unsupervised methods [46, 47], many clinical NLP methods rely on language-specific resources.

In this article, I discussed the challenges and opportunities regarding natural language processing (NLP) models like Chat GPT and Google Bard and how they will transform teaching and learning in higher education. The article highlights the potential benefits of using NLP models for personalized learning and on-demand support, such as providing customized learning plans, generating feedback and support, and offering resources to students whenever and wherever they need them. However, the article also acknowledges the challenges that NLP models may bring, including the potential loss of human interaction, bias, and ethical implications. To address the highlighted challenges, universities should ensure that NLP models are used as a supplement to, and not as a replacement for, human interaction. Institutions should also develop guidelines and ethical frameworks for the use of NLP models, ensuring that student privacy is protected and that bias is minimized. Although there is a wide range of opportunities for NLP models, like Chat GPT and Google Bard, there are also several challenges (or ethical concerns) that should be addressed.

All of the problems above will require more research and new techniques in order to improve on them. In this paper, we have provided an introduction to the emerging field of humanitarian NLP, identifying ways in which NLP can support humanitarian response, and discussing outstanding challenges and possible solutions. We have also highlighted how long-term synergies between humanitarian actors and NLP experts are core to ensuring impactful and ethically sound applications of NLP technologies in humanitarian contexts. We hope that our work will inspire humanitarians and NLP experts to create long-term synergies, and encourage impact-driven experimentation in this emerging domain. The development of reference corpora is also key for both method development and evaluation. The study of annotation methods and optimal uses of annotated corpora has been growing increasingly with the growth of statistical NLP methods [7, 60, 61].

The most popular technique used in word embedding is word2vec — an NLP tool that uses a neural network model to learn word association from a large piece of text data. However, the major limitation to word2vec is understanding context, such as polysemous words. NCATS will share with the participants an open repository containing abstracts derived from published scientific research articles and knowledge assertions between concepts within these abstracts. The participants will use this data repository to design and train their NLP systems to generate knowledge assertions from the text of abstracts and other short biomedical publication formats. Other open biomedical data sources may be used to supplement this training data at the participants’ discretion.

Event discovery in social media feeds (Benson et al.,2011) [13], using a graphical model to analyze any social media feeds to determine whether it contains the name of a person or name of a venue, place, time etc. NLP systems must comprehend and incorporate broader context from conversations or documents to correctly interpret meaning, resolve references, and capture subtle nuances. Developing models that effectively capture and utilize context remains an ongoing challenge. NLP models are typically trained on fixed vocabularies, which can pose difficulties when encountering words or phrases not present in the training data. Handling out-of-vocabulary words requires strategies such as leveraging subword units, word embeddings, or incorporating external knowledge resources to improve coverage and understanding. NLP is used for automatically translating text from one language into another using deep learning methods like recurrent neural networks or convolutional neural networks.

A recent ML-specific analogue to SPEC is MLCommons, which organises the MLPerf series of performance benchmarks focusing on model training and inference. Similar to SPEC, MLPerf has a broad base of support from academia and industry, building on previous individual efforts for measuring performance such as DeepBench by Baidu or DAWNBench by Stanford. Aravind Joshi said that without benchmarks to assess the performance of our models, we are just like “astronomers wanting to see the stars but refusing to build telescopes”. A benchmark sets a standard for assessing the performance of different systems that is agreed upon by the community. To ensure that a benchmark is accepted by the community, many recent benchmarks either select a representative set of standard tasks, such as GLUE or XTREME or actively solicit task proposals from the community, such as SuperGLUE, GEM, or BIG-Bench.

Most NLP research is about benchmarking models on small text tasks and even state-of-the-art models have a limit on the number of words allowed in the input text. As a result, scaling up NLP to extract context from huge volumes of medium to long unstructured documents remains a technical challenge. Though there has been a sharp increase in recent times of NLP datasets, these are often collected through automation or crowdsourcing. There is, therefore, the potential for incorrectly labelled data which, when used for training, can lead to memorisation and poor generalisation.

Moreover, on-demand support is a crucial aspect of effective learning, particularly for students who are working independently or in online learning environments. The NLP models can provide on-demand support by offering real-time assistance to students struggling with a particular concept or problem. It can help students overcome learning obstacles and enhance their understanding of the material. In addition, on-demand support can help build students’ confidence and sense of self-efficacy by providing them with the resources and assistance they need to succeed. These models can offer on-demand support by generating responses to student queries and feedback in real time.

Safely deploying these tools in a sector committed to protecting people in danger and to causing no harm requires developing solid ad-hoc evaluation protocols that thoroughly assess ethical risks involved in their use. First, builders of AI systems need to give greater consideration to the communities directly or indirectly https://chat.openai.com/ providing the data used in commercial and noncommercial settings for AI development. By contrast, closed models prioritize proprietary information but can limit shared innovation. Currently, one of the biggest hurdles for further development of NLP systems in public health is limited data access (82,83).

It’s vital, as it brings together linguistics, computer science, and psychology to improve NLP systems’ understanding and interaction capabilities. With targeted improvements and broader training examples, models better detect humor and sarcasm. Models are more inclusive and versatile by gathering and incorporating more extensive linguistic data for lesser-known languages. Infuse powerful natural language AI into commercial applications with a containerized library designed to empower IBM partners with greater flexibility. Thus far, we have seen three problems linked to the bag of words approach and introduced three techniques for improving the quality of features.

This paper offers the first broad overview of clinical Natural Language Processing (NLP) for languages other than English. Recent studies are summarized to offer insights and outline opportunities in this area. While still too early to make an educated guess, if big tech industries keep pushing for a “metaverse”, social media will most likely change and adapt to become something akin to an MMORPG or a game like Club Penguin or Second Life.

An additional set of concerns arises with respect to ethical aspects of data collection, sharing, and analysis in humanitarian contexts. Text data may contain sensitive information that can be challenging to automatically identify and remove, thus putting potentially vulnerable individuals at risk. One of the consequences of this is that organizations are often hesitant around open sourcing.

Natural language processing (NLP) is a subfield of computer science and artificial intelligence (AI) that uses machine learning to enable computers to understand and communicate with human language. If the training data is not adequately diverse or is of low quality, the system might learn incorrect or incomplete patterns, leading to inaccurate responses. The accuracy of NP models might be impacted by the complexity of the input data, particularly when it comes to idiomatic expressions or other forms of linguistic subtlety.

NLP models can unintentionally perpetuate biases present in the training data, leading to biased or unfair outcomes. Ensuring fairness, transparency, and ethical considerations in NLP systems is a pressing challenge. Developing techniques for bias detection, mitigation, and interpretability is crucial to address these ethical concerns. Addressing data availability, quality, and biases are crucial challenges that researchers are actively tackling. Natural Language Processing (NLP) has witnessed significant advancements in recent years, revolutionizing how computers understand and interact with human language.

Interestingly, while the overall cross-lingual retrieval performance was satisfactory, the authors found that better query translation did not necessarily yield improved retrieval performance. In this context, data extracted from clinical text and clinically relevant texts in languages other than English adds another dimension to data aggregation. The World Health Organization (WHO) is taking advantage of this opportunity with the development of IRIS [23], a free software tool for interactively coding causes of death from clinical documents in seven languages.

Furthermore, language always evolves, requiring systems to continuously adapt to new slang, terms, and usage patterns. Language Diversity and AdaptabilityThe sheer diversity of languages and dialects adds complexity. Mark contributions as unhelpful if you find them irrelevant or not valuable to the article. Become an IBM partner and infuse IBM Watson embeddable AI in your commercial solutions today. Accelerate the business value of artificial intelligence with a powerful and flexible portfolio of libraries, services and applications.

3 NLP in talk

Most NLP research and resources are primarily focused on high-resource languages, leaving low-resource languages with limited tools and models. Building effective NLP systems for low-resource languages is a challenge due to data scarcity, lack of resources, and linguistic complexities. More options include IBM® watsonx.ai™ AI studio, which enables multiple options to craft model configurations that support a range of NLP tasks including question answering, content generation and summarization, text classification and extraction. For example, with watsonx and Hugging Face AI builders can use pretrained models to support a range of NLP tasks. Moreover, over-reliance could reinforce existing biases and perpetuate inequalities in education. For example, if the system is trained on biased or incomplete data, it might generate responses reflecting these biases, thereby leading to a reinforcement of existing inequalities and a failure to challenge and disrupt discriminatory practices in higher education.

Entity recognition plays a key role in identifying and classifying entities in unstructured text. It is fundamental, for example, to the pseudonymization of personal data in documents and the analysis of unstructured text, facilitating the protection of privacy and the extraction of relevant information. In the recent past, models dealing with Visual Commonsense Reasoning [31] and NLP have also been getting attention of the several researchers and seems a promising and challenging area to work upon. These models try to extract the information from an image, video using a visual reasoning paradigm such as the humans can infer from a given image, video beyond what is visually obvious, such as objects’ functions, people’s intents, and mental states. Luong et al. [70] used neural machine translation on the WMT14 dataset and performed translation of English text to French text. The model demonstrated a significant improvement of up to 2.8 bi-lingual evaluation understudy (BLEU) scores compared to various neural machine translation systems.

Computers can be taught to make sense of written or spoken language, involving teaching computers how to understand the nuances of language. Natural Language Processing (NLP) uses AI to understand and communicate with humans in a way that seems natural. Before any analysis can take place, raw text data is preprocessed to remove noise, tokenize sentences into words, and convert them into a format suitable for analysis. Training data is a curated collection of input-output pairs, where the input represents the features or attributes of the data, and the output is the corresponding label or target. Training data is composed of both the features (inputs) and their corresponding labels (outputs).

Emerging evidence in the body of knowledge indicates that chatbots have linguistic limitations (Wilkenfeld et al., 2022). For example, a study by Coniam (2014) suggested that chatbots are generally able to provide grammatically acceptable answers. However, at the moment, Chat GPT lacks linguistic diversity and pragmatic versatility (Chaves and Gerosa, 2022). Still, Wilkenfeld et al. (2022) suggested that in some instances, chatbots can gradually converge with people’s linguistic styles.

They also need to monitor and evaluate their NLP applications for potential harms or benefits, and address any concerns or feedback from their stakeholders. This is a crucial process that is responsible for the comprehension of a sentence’s true meaning. Borrowing our previous example, the use of semantic analysis in this task enables a machine to understand if an individual uttered, “This is going great,” as a sarcastic comment when enduring a crisis. Similar to how we were taught grammar basics in school, this teaches machines to identify parts of speech in sentences such as nouns, verbs, adjectives and more. This also teaches systems to understand when a word is used as a verb and the same word is used as a noun. As with all areas of the eCommerce world, as technology advances we continue to see growth in AI fields.

How Does Natural Language Processing (NLP) Work?

There are other, smaller-scale initiatives that can contribute to creating and consolidating an active and diverse humanitarian NLP community. Compiling and sharing lists of educational resources that introduce NLP experts to the humanitarian world—and, vice versa, resources that introduce humanitarians to the basics of NLP—would be a highly valuable contribution. Similarly, sharing ideas on concrete projects and applications of NLP technology in the humanitarian space (e.g., in the form of short articles) could also be an effective way to identify concrete opportunities and foster technical progress. Collaborations between NLP experts and humanitarian actors may help identify additional challenges that need to be addressed to guarantee safety and ethical soundness in humanitarian NLP.

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.

Xie et al. [154] proposed a neural architecture where candidate answers and their representation learning are constituent centric, guided by a parse tree. Under this architecture, the search space of candidate answers is reduced while preserving the hierarchical, syntactic, and compositional structure among constituents. The objective of this section is to present the various datasets used in NLP and some state-of-the-art models in NLP. By improving translation accuracy and understanding cultural nuances, NLP can facilitate better cross-cultural communication.

These judges will evaluate the submissions for originality, innovation, and practical considerations of design, and will determine the winners of the competition accordingly. In light of the fast pace of model improvements, we are in need of more nimble mechanisms for model evaluation. Specifically, beyond dynamic single-task evaluations such as DynaBench, it would be useful to define a dynamic collection of benchmark datasets on which models have not reached human performance. This collection should be managed by the community, with datasets removed or down-weighted as models reach human performance and new challenging datasets being regularly added.

One of the more specialized use cases of NLP lies in the redaction of sensitive data. Industries like NBFC, BFSI, and healthcare house abundant volumes of sensitive data from insurance forms, clinical trials, personal health records, and more. When there are multiple instances of nouns such as names, location, country, and more, a process called Named Entity Recognition is deployed. This identifies and classifies entities in a message or command and adds value to machine comprehension. An English speaker might say, “I’m going to work tomorrow morning,” while an Italian speaker would say, “Domani Mattina vado al lavoro.” Even though these two sentences mean the same thing, NLP won’t understand the latter unless you translate it into English first. Similarly, colloquialisms, slang and dialects all complicate things for the computer systems.

If you give the system incorrect or biased data, it will either learn the wrong things or learn inefficiently. NLP models require large amounts of annotated data for training, but obtaining high-quality labeled data can be challenging. Furthermore, data sparsity and inconsistency pose significant hurdles in building robust NLP systems, leading to suboptimal performance in real-world applications. A human being must be immersed in a language constantly for a period of years to become fluent in it; even the best AI must also spend a significant amount of time reading, listening to, and utilizing a language. If you feed the system bad or questionable data, it’s going to learn the wrong things, or learn in an inefficient way. Addressing these challenges requires not only technological innovation but also a multidisciplinary approach that considers linguistic, cultural, ethical, and practical aspects.

This resource, developed remotely through crowdsourcing and automatic text monitoring, ended up being used extensively by agencies involved in relief operations on the ground. While at the time mapping of locations required intensive manual work, current resources (e.g., state-of-the-art named entity recognition technology) would make it significantly easier to automate multiple components of this workflow. While NLP systems achieve impressive performance on a wide range of tasks, there are important limitations to bear in mind. First, state-of-the-art deep learning models such as transformers require large amounts of data for pre-training. This data is hardly ever available for languages with small speaker communities, which results in high-performing models only being available for a very limited set of languages (Joshi et al., 2020; Nekoto et al., 2020). After preprocessing, the text data needs to be transformed into numerical features that can be used by machine learning models.

The Robot uses AI techniques to automatically analyze documents and other types of data in any business system which is subject to GDPR rules. It allows users to search, retrieve, flag, classify, and report on data, mediated to be super sensitive under GDPR quickly and easily. Users also can identify personal data from documents, view feeds on the latest personal data that requires attention and provide reports on the data suggested to be deleted or secured.

This raises important ethical and societal questions about the use of these models, and requires careful monitoring and control of the generated content. While current models can generate coherent and consistent text for short pieces of text, they struggle to maintain this over longer pieces of text. This is because these models Chat GPT typically generate text one word or one sentence at a time, without a clear understanding of the overall structure or theme of the text. This can lead to inconsistencies and incoherencies, especially in longer pieces of text. One of the challenges in generating human-like text is maintaining coherence and consistency.

This data can provide valuable insights, but it also presents challenges in terms of storage, processing, and analysis. The meaning of a word or a phrase can change dramatically based on the context in which it is used. For example, the word “bat” can refer to a nocturnal flying mammal, a piece of sports equipment, or an action in a game. Determining the correct meaning requires understanding the context in which the word is used, which can be a significant challenge for NLP models. As an alternative, we can develop mechanisms that allow us to identify the best systems with few examples. This is particularly crucial in settings where assessing performance of many systems is expensive, such as in human evaluation for natural language generation.

These models are then used to make predictions or perform tasks such as classification, translation, or summarization. NLP systems rely on a combination of linguistic rules, statistical models, and machine learning algorithms to process and understand human language. Advanced practices like artificial neural networks and deep learning allow a multitude of NLP techniques, algorithms, and models to work progressively, much like the human mind does.

The Standard Performance Evaluation Corporation (SPEC), established in 1988 is one of the oldest organisations dedicated to benchmarking the performance of computer hardware. Every year, it would release different benchmark sets, each composed of multiple programs, with performance measured as the geometric mean of millions of instructions per second (MIPS). Reaching human performance on influential benchmarks is often seen as a key milestone for a field. AlphaFold 2 reaching performance competitive with experimental methods on the CASP 14 competition marked a major scientific advance in the field of structural biology. As we mentioned in our previous article regarding the linguistic challenges of NLP, AI programs like AlphaGo have evolved quickly to master a broader variety of games with less predefined knowledge.

However, it is important to note that even when datasets and evaluations are adjusted for biases, this does not guarantee an equal impact across morally relevant strata. For example, use of health data available through social media platforms must take into account the specific age and socioeconomic groups that use them. A monitoring system trained on data from Facebook is likely to be biased towards health data and linguistic quirks specific to a population older than one trained on data from Snapchat (75).

It is not a static form, and in order for the NLP to keep up to date with trends, it has to be always learning and training. We believe in solving complex business challenges of the converging world, by using cutting-edge technologies. NLP systems like Google Translate and Microsoft Translator utilize advanced algorithms to accurately translate text, making cross-lingual communication seamless and efficient. Natural Language Processing (NLP) has witnessed remarkable advancements in recent years, but it still faces several challenges that hinder its full potential.

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. The human language and understanding is rich and intricated and there many languages spoken by humans.

Use this model selection framework to choose the most appropriate model while balancing your performance requirements with cost, risks and deployment needs. In another course, we’ll discuss how another technique called lemmatization can correct this problem by returning a word to its dictionary form. Next, you might notice that many of the features are very common words–like “the”, “is”, and “in”. False positives arise when a customer asks something that the system should know but hasn’t learned yet. Conversational AI can recognize pertinent segments of a discussion and provide help using its current knowledge, while also recognizing its limitations.

How can businesses overcome the challenges of natural language processing for AI?

State-of-the-art language models can now perform a vast array of complex tasks, ranging from answering natural language questions to engaging in open-ended dialogue, at levels that sometimes match expert human performance. Open-source initiatives such as spaCy1 and Hugging Face’s libraries (e.g., Wolf et al., 2020) have made these technologies easily accessible to a broader technical audience, greatly expanding their potential for application. Earlier machine learning techniques such as Naïve Bayes, HMM etc. were majorly used for NLP but by the end of 2010, neural networks transformed and enhanced NLP tasks by learning multilevel features.

For relation classification, for instance, the FewRel dataset lacks some important realistic properties, which Few-shot TACRED addresses. For binary sentiment classification on the IMDb dataset, only highly polarised positive and negative reviews are considered and labels are exactly balanced. In information retrieval, retrieving relevant before non-relevant documents is necessary but not sufficient for real-world usage.

Because nowadays the queries are made by text or voice command on smartphones.one of the most common examples is Google might tell you today what tomorrow’s weather will be. But soon enough, we will be able to ask our personal data chatbot about customer sentiment today, and how we feel about their brand next week; all while walking down the street. But with time the technology matures – especially the AI component –the computer will get better at “understanding” the query and start to deliver answers rather than search results.

For some languages, a mixture of Latin and English terminology in addition to the local language is routinely used in clinical practice. This adds a layer of complexity to the task of building resources and exploiting them for downstream applications such as information extraction. For instance, in Bulgarian EHRs medical terminology appears in Cyrillic (Bulgarian terms) and Latin (Latin and English terms).

NLP is a subfield of AI that is devoted to developing algorithms and building models capable of using language in the same way humans do (13). It is routinely used in virtual assistants like “Siri” and “Alexa” or in Google searches and translations. NLP provides the ability to analyze and extract information from unstructured sources, automate question answering and conduct sentiment analysis and text summarization (8). With natural language (communication) being the primary means of knowledge collection and exchange in public health and medicine, NLP is the key to unlocking the potential of AI in biomedical sciences. The first objective gives insights of the various important terminologies of NLP and NLG, and can be useful for the readers interested to start their early career in NLP and work relevant to its applications. The second objective of this paper focuses on the history, applications, and recent developments in the field of NLP.

Exploring various models, potential advantages, and essential best practices for ensuring success. Accordingly, your NLP AI needs to be able to keep the conversation moving, providing additional questions to collect more information and always pointing toward a solution. With the help of complex algorithms and intelligent analysis, Natural Language Processing (NLP) is a technology that is starting to shape the way we engage with the world.

A language can be defined as a set of rules or set of symbols where symbols are combined and used for conveying information or broadcasting the information. Since all the users may not be well-versed in machine specific language, Natural Language Processing (NLP) caters those users who do not have enough time to learn new languages or get perfection in it. In fact, NLP is a tract of Artificial Intelligence and Linguistics, devoted to make computers understand the statements or words written in human languages.

They tested their model on WMT14 (English-German Translation), IWSLT14 (German-English translation), and WMT18 (Finnish-to-English translation) and achieved 30.1, 36.1, and 26.4 BLEU points, which shows better performance than Transformer baselines. 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. 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.

• However, the ambiguity and complexity of reference relationships pose challenges for accurate coreference resolution.
• The model achieved state-of-the-art performance on document-level using TriviaQA and QUASAR-T datasets, and paragraph-level using SQuAD datasets.
• Text excerpts are extracted from a recent humanitarian response dataset (HUMSET, Fekih et al., 2022; see Section 5 for details).
• Lastly, we should be more rigorous in the evaluation on our models and rely on multiple metrics and statistical significance testing, contrary to current trends.
• For example, the word “bank” can refer to a financial institution, the side of a river, or a turn in aviation.

Similarly to work in English, the methods for Named Entity Recognition (NER) and Information Extraction for other languages are rule-based [82, 83], statistical, or a combination of both [84]. With access to large datasets, studies using unsupervised learning methods can be performed irrespective of language, as in Moen et al. [85] where such methods were applied for information retrieval of care episodes in Finnish clinical text. For German, extracting information from clinical narratives for cohort building using simple rules was successful [88]. Some of the work in languages other than English addresses core NLP tasks that have been widely studied for English, such as sentence boundary detection [27], part of speech tagging [28–30], parsing [31, 32], or sequence segmentation [30]. Word segmentation issues are more obviously visible in languages which do not mark word boundaries with clear separators such as white spaces. A study of automatic word segmentation in Japanese addressed the lack of spacing between words in this language [33].

However, the limitation with word embedding comes from the challenge we are speaking about — context. As the budget (and thus size) of benchmarks generally remains constant, statistical significance testing becomes even more important as it enables us to reliably detect qualitatively relevant performance differences between systems. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. Actors in the Global North have been the primary drivers of discussions about responsible AI, and they have focused such discussions on concepts like openness, privacy, and copyright protections.