#NLU

[donnellyspacebabe]

lo telling garrison he wished he had glaucoma so he couldn't see his batman tattoo on his neck is so on brand for him

[virginmiri99]

Literally crying with laughter over the memory that Love Live tried to name one of their units BEAT CATS

[rag-llm-model]

Bag of Word vs TF-IDF !

Both the Bag of Words (BoW) model or TF-IDF (Term Frequency-Inverse Document Frequency) function are used. Text-specific extractions but different purposes and characteristics.

[govindhtech]

Mastering The Power Of Natural Language Processing(NLP)

What is NLP?

Machine learning helps computers comprehend and interact with human language in Natural language processing (NLP).

NLP models human language using statistical modeling, machine learning, deep learning, and computational linguistics to help computers and technology identify, comprehend, and generate text and voice.

From big language models’ communication capacities to picture creation models’ request understanding, NLP research has led to generative AI. Natural language processing (NLP) is used in search engines, voice-activated chatbots for customer support, voice-activated GPS systems, and smartphone digital assistants like Cortana, Siri, and Alexa.

NLP is being used in corporate solutions to automate and streamline operations, enhance worker productivity, and simplify business processes. How NLP operates NLP analyzes, comprehends, and produces human language in a machine-processable manner by integrating a number of computational approaches.

How NLP works?

Here is a summary of the stages in a typical NLP pipeline:

Automation of repetitive tasks 

Natural language processing(NLP) text preparation makes unprocessed text machine-readable for analysis. The process begins with tokenization, which breaks text into words, sentences, and phrases. This simplifies complex terminology. To ensure that terms like “Apple” and “apple” are handled consistently, lowercasing is then used to standardize the text by changing all letters to lowercase.

Another popular stage is stop word removal, which filters out often used words like “is” and “the” that don’t significantly contribute sense to the text. By combining many variants of the same word together, stemming or lemmatization simplifies language analysis by reducing words to their root form (for example, “running” becomes “run”). Furthermore, text cleaning eliminates extraneous components that might complicate the analysis, such punctuation, special characters, and digits.

Following preprocessing, the text is standardized, clear, and prepared for efficient interpretation by machine learning models.

Feature extraction 

The process of turning unprocessed text into numerical representations that computers can understand and evaluate is known as feature extraction. Using Natural language processing(NLP) methods like Bag of Words and TF-IDF, which measure the frequency and significance of words in a document, this entails turning text into structured data. Word embeddings, such as Word2Vec or GloVe, are more sophisticated techniques that capture semantic links between words by representing them as dense vectors in a continuous space. By taking into account the context in which words occur, contextual embeddings improve this even further and enable richer, more complex representations.

Text analysis 

Text analysis is the process of using a variety of computer approaches to understand and extract relevant information from text data. This procedure involves tasks like named entity recognition (NER), which recognizes specified things like names, places, and dates, and part-of-speech (POS) tagging, which determines the grammatical functions of words.

Sentiment analysis establishes the text’s emotional tone by determining whether it is neutral, positive, or negative, whereas dependency parsing examines the grammatical links between words to comprehend sentence structure. Topic modeling discovers common topics in a text or group of documents. NLU is a subfield of Natural language processing(NLP) that deciphers phrases. Software can interpret words with diverse meanings or identify similar meanings in different sentences thanks to NLU. NLP text analysis uses these methods to turn unstructured material into insights.

Model training

Machine learning models are then trained using processed data to identify patterns and connections in the data. The model modifies its parameters during training in order to reduce mistakes and enhance performance. After training, the model may be applied to fresh, unknown data to produce outputs or make predictions. NLP modeling’s efficacy is continuously improved via assessment, validation, and fine-tuning to increase precision and applicability in practical settings.

Various software environments are helpful for the aforementioned procedures. Python is used to construct the Natural Language Toolkit (NLTK), a set of English tools and apps. Classification, tokenization, parsing, tagging, stemming, and semantic reasoning are supported. Models for Natural language processing(NLP) applications may be trained using TensorFlow, a free and open-source software framework for AI and machine learning. There are several certificates and tutorials available for anyone who want to get acquainted with these technologies.

NLP’s advantages

NLP helps humans and robots communicate and collaborate by letting people speak their natural language to technology. This benefits many applications and industries.

Automating monotonous tasks

Better insights and data analysis

Improved search

Creation of content

Automating monotonous tasks

Tasks like data input, document management, and customer service may be entirely or partly automated with the use of Natural Language Processing(NLP). NLP-powered chatbots, for instance, can answer standard consumer questions, freeing up human agents to deal with more complicated problems. NLP solutions may automatically categorize, extract important information, and summarize text in document processing, saving time and minimizing mistakes that come with human data management. Natural Language Processing(NLP) makes it easier to translate texts across languages while maintaining context, meaning, and subtleties.

Better insights and data analysis

By making it possible to extract insights from unstructured text data, such news articles, social media postings, and customer reviews, Natural Language Processing(NLP) improves data analysis. Natural Language Processing(NLP) may find attitudes, patterns, and trends in big datasets that aren’t immediately apparent by using text mining approaches. Sentiment analysis makes it possible to extract subjective elements from texts, such as attitudes, feelings, sarcasm, perplexity, or mistrust. This is often used to route messages to the system or the person who is most likely to respond next.

This enables companies to get a deeper understanding of public opinion, market situations, and consumer preferences. Large volumes of text may also be categorized and summarized using NLP techniques, which helps analysts find important information and make data-driven choices more quickly.

Improved search

By helping algorithms comprehend the purpose of user searches, natural language processing (NLP) improves search by producing more precise and contextually relevant results. NLP-powered search engines examine the meaning of words and phrases rather than just matching keywords, which makes it simpler to locate information even in cases when queries are complicated or ambiguous. This enhances the user experience in business data systems, document retrieval, and online searches.

Strong content creation

Advanced language models are powered by Natural language processing(NLP)to produce text that is human-like for a variety of uses. Based on user-provided prompts, pre-trained models, like GPT-4, may produce reports, articles, product descriptions, marketing copy, and even creative writing. Additionally, NLP-powered applications may help automate processes like creating legal documents, social media postings, and email drafts. NLP saves time and effort in content generation while ensuring that the created information is coherent, relevant, and in line with the intended message by comprehending context, tone, and style.

Read more on Govindhtech.com

[cmitimesnews]

Top Law Colleges In India : National Institute Ranking Framework 2024

Top Law Colleges In India: According to the National Institute Ranking Framework (NIRF) 2024, National Law School of India University, Bengaluru is the pinnacle ranked regulation university in the u . S . A . With a rating of eighty three.83. This is the seventh consecutive year that National Law School of India University (NLSU) Bengaluru has emerged as the nice law institute inside the usa.

NLU Delhi, NALSAR University of Law Hyderabad, WBNUJS have retained their 2nd, third and fourth positions respectively. The score of National Law University (NLU) Delhi, India is seventy seven.48. NALSAR University Hyderabad is at the third role with a score of seventy seven.05 and The West Bengal National University of Juridical Sciences (WBNUJS) is at the fourth position with a score of 76.39.

[wisepl]

Audio Data Annotation & Collection Services

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[droidtown]

AI 時代的語言學 - 連載之三：實作

在本系列文的連載之一裡，我們說明了人類的語言系統具有其天生的內部結構，我們可以用這個內部結構來設計一個專門計算「語言結構」的程式，讓資訊系統仿照人類處理語言的過程來推算出語言的結構。

在系列文二裡，我們則進一步利用這個結構來說明「語意 (semantics)」也可以這麼計算，並且和 LLM 做了初步的比對。然而，在系列文二裡，我們沒有特別說明「語意」的計算程式，大概會長什麼模樣。

本篇就來說明，我們要利用系列文二裡的「形式語意學 (Formal Semantics)」的表示式來計算語意，程式大概會長什麼樣子…

任何科學領域的研究，第一個要回答的問題永遠是「你的最小單位是什麼？」只要最小單位搞錯，那麼這個研究的結果大概也是…不太科學的 (客氣地說)。就像電影《鍋蓋頭 Jarhead》裡的教官在射擊訓練場特別指出的「報距離的時候，選擇適當的最小單位！不要用你的屌當計算單位，因為它太小了！」

因此，當早期的 embedding 一路選擇「詞 (word)」、「字符 (character)」直到比字符更小的「token (符元)」這樣愈來愈小的方向時，我們從「詞 (word)」就開始延伸到形式語意學的計算符號上面。

同樣地，我們用「約翰在波士頓租了一間房子」為例，以「詞」做為 embedding 的最小單位，就會將以下的內容做為訓練材料，一共有 6 個元素：["約翰", "在", "波士頓", "租了", "一間", "房子"]

而如果是以「字符」為 embedding 的最小單位，原本的「約翰在波士頓租了一間房子」，就會變成這樣的訓練內容，一共有 12 個元素：["約", "翰", "在", "波", "士", "頓", "租", "了", "一", "間", "房", "子"]

而如果是以 token 做為 embedding 的最小單位，一個中文字符大概會是三個 token，整個句子就會變成像以下的 36 個元素：["\xe7", "\xb4", "\x84", "\xe7", "\xbf", "\xb0", "\xe5", "\x9c", "\xa8", "\xe6", "\xb3", "\xa2", "\xe5", "\xa3", "\xab", "\xe9", "\xa0", "\x93", "\xe7", "\xa7", "\x9f", "\xe4", "\xba", "\x86", "\xe4", "\xb8", "\x80", "\xe9", "\x96", "\x93", "\xe6", "\x88", "\xbf", "\xe5", "\xad", "\x90"]

但我們使用邏輯表達式，例如：

FUNC_在(LOC_波士頓, ASP_了(VERB_租(PSN_約翰, CLA_一間(ENTY_房子))))

整個句子將會變成以下的模樣進行 embedding 的訓練：["FUNC_在", "(", "LOC_波士頓", "ASP_了", "(", "VERB_租", "(", "PSN_約翰", "CLA_一間", "(", "ENTY_房子", ")", ")", ")", ")"]

共有 15 個元素。這是我們在計算向量時，第一個和主流「字符/詞彙/token/byte」做為嵌入基本單位的最大不同之處。

有趣的是，我們利用形式語意表示式，可以得到最大的好處就是「主動語態和被動語態將有一樣的語意表示式」。

也就是說，「有一間在波士頓的房子被約翰租了」這個句子的語意表示式和「約翰在波士頓租了一間房子」是完全一致的。

以這些元素來計算詞向量還不夠，因為詞向量對語言結構不敏感，頂多只能呈現「這個句子在高維空間中大概分佈在哪裡」的���徵。因此我們要再加上能對語言結構敏感，並在計算過程中保持序列結構的 pairwise alignment 將每個句子彼此之間的相似程度計算。

大概會像這樣子：

[註] 涉及機密處的參數予以遮蓋，請見諒。 [註] 不同詞性的加權，可加在 alignments 這一行之後處理。 [註] 不同層次的 negation_function 的處理，也可以在這一區塊處理。

這麼一來，這兩個只差了「房子」和「屋子」一個詞的句子，相似度可以計算出為 92%。對齊的過程視覺化後如下所示：

最小單位是形式語意學提供的，而對齊的算法是 naive pairwise algorithm (常用於基因比對)。該演算法的物理意義在於「找出兩個 DNA 序列」中相同排列的段落。

若兩個物種的 DNA 序列「相同的段落愈多，且兩兩相同段落之間的���移愈少」，則這兩個物種在分子生物的層面上愈相近。

綜合地說，在 NLP/AI 的技術發展史裡，早期使用「字符」，中期使用「詞彙」，前者會遇到「雜訊過多 (多一個「的」，少一個「啊」，都會被演算法視為是不同的句子)，後者則會遇到「主動句 (我看到他匯錢) vs. 被動句 (他匯錢被我看到)」明明意義一樣，但卻被演算法視為不同句子的問題。近期使用 token/byte 的 LLM 則會遇上無法呈現語言中的邏輯因果關係的問題。

而我們可以利用語言學，先把所有的句子都先收斂成「語意表示式」，藉由：

FUNC_在(LOC_波士頓, ASP_了(VERB_租(PSN_約翰, CLA_一間(ENTY_房子))))

這樣的結構，不論其原文是「約翰在波士頓租了一間房子」或是「約翰租了一間波士頓的房子」甚至是「波士頓的一間房子被約翰租了」都是一樣的結構，再交給 embedding 和 pairwise algorithm 計算以後，就能得到「上面三個句子都是一樣的語意」的結論了。

[cmitimesnews-blog]

The AILET result 2024 has been declared by National Law University (NLU) Delhi. Candidates who appeared in All India Law Entrance Test (AILET) can check their result from the official website nationallawuniversitydelhi.in.

[emmersed360-podcast]

King Shares his Journey from West Monroe, la to playing at LSU before transferring to Northeast Louisiana University, now ULM. King would become a stand-out player before being drafted in the 2nd round in the 1995 NFL draft by the Carolina Panthers. King would be phenomenal, but the off-the-field issue would end that dream. With family and grace, King is a changed man today, Life After the Game

[modblitz]

Embracing Conversational AI: Revolutionizing the Way We Interact

In recent years, the rise of Conversational AI has transformed the way businesses and individuals interact with technology. Conversational AI, powered by advanced natural language processing and machine learning algorithms, has enabled human-like conversations with virtual assistants and chatbots. This transformative technology has gained immense popularity across various industries, offering…

[theprobeindia]

NLU Delhi has witnessed three student deaths in three months, with students and alumni highlighting systemic issues and seeking institutional accountability.

[mimi-askany]

Tất cả những gì bạn cần biết về Đại học Nông lâm TPHCM

Sơ lược về Trường Đại học nông lâm TPHCM Trả lời các câu hỏi cho sinh viên muốn thi vào đại học NLU

Điểm chuẩn đại học Nông lâm năm 2022 là bao nhiêu

Năm 2023 Đại học Nông lâm tuyển sinh theo phương thức nào

Hồ sơ đăng ký xét tuyển đại học Nông Lâm TPHCM

Đại học Nông Lâm TPHCM đào tạo những ngành nào

Địa chỉ đại học Nông Lâm TPHCM

Học phí đại học Nông Lâm TPHCM năm 2023

Đại học Nông Lâm TPHCM có kí túc xá cho sinh viên không

Đại học Nông lâm có chương trình trao đổi sinh viên với các nước khác không?

Chương trình Tiên Tiến đào tạo bằng tiếng anh của NLU có khác biệt gì so với chương trình Đào tạo thông thường Chia sẻ cơ hội nghề nghiệp từ cựu sinh viên trường NLU

Review có nên học đại học Nông Lâm không?

Tốt nghiệp đại học Nông Lâm có dễ xin việc làm không?

Sinh viên tốt nghiệp Đại học Nông lâm TPHCM sẽ làm việc ở đâu

Lương của sinh viên tốt nghiệp NLU là bao nhiêu

Cựu sinh viên ưu tú từng học tại Đại học Nông Lâm Xem thông tin chi tiết: https://topchuyengia.vn/tu-van/dai-hoc-nong-lam-tphcm

[cyberlabe]

Foundation Large Language Model Stack

[teamattorneylex]

Call for Submissions by NLUD Journal of Legal Studies [Vol V]: Submit by March 21

The Journal of Legal Studies (JLS), National Law University Delhi (NLUD) is inviting submissions for the fifth volume of the journal, due to be published in August 2023.  About the Journal The Journal of Legal Studies (JLS) is National Law University Delhi’s annual student-edited, peer-reviewed law journal. It seeks to provide a forum for engaging in discussions on varied issues of contemporary…

[arunachal-university]

Arunachal University is one of the leading law colleges in the country. Located in Namsai, the college offers a three-year full-time course leading to the degree of Bachelor of Laws (LL.B). The college is affiliated to Arunachal University and is recognized by the Bar Council of India. The college has a rich tradition of producing excellent lawyers who have made a mark in various fields such as judiciary, academia, legal practice and public service. The college has a strong commitment to providing quality legal education and has been consistently ranked among the top law college in Assam. The college offers a wide range of facilities and resources for its students.