Why you should use Natural Language Processing (NLP) test?

The Natural Language Processing (NLP) online test makes use of scenario-based questions to test for on-the-job skills as opposed to theoretical knowledge, ensuring that candidates who do well on this screening test have the relavant skills. The questions are designed to covered following on-the-job aspects: Forståelse og anvendelse af tokeniseringsteknikker Implementering af tekstklassificeringsalgoritmer Analyse og fortolkning af stemning i tekst Identificering og uddrag af navngivne enheder Brug af ordindlejringer til naturlige sprogopgaver Bygning af sprogmodeller til tekstgenerering Oversættelse af tekst mellem sprog ved hjælp af maskinoversættelse Uddrag værdifulde oplysninger fra ustruktureret tekst Oprettelse af kortfattede resume af tekstdata Opdage emner og mønstre i tekst gennem emnetmodellering

Natural Language Processing (NLP) test

Q: What topics are covered in the Natural Language Processing (NLP) test?

Tokenisering Tekstklassificering Sentimentanalyse Navngivet enhedsgenkendelse Ord indlejringer Sprogmodellering Maskinoversættelse Informationsekstraktion Tekstopsummering Emnemodellering

About the test:

NLP (Natural Language Processing) Online Test bruger scenariebaserede MCQ'er til at evaluere kandidater om deres viden om NLP-koncepter og teknikker, såsom tekstklassificering, informationsekstraktion, sentimentanalyse og navngivet enhedsgenkendelse. Testen vurderer en kandidats evne til at anvende NLP-teknikker til problemer i den virkelige verden og scenarier og designe effektive NLP-modeller.

Covered skills:

Tokenisering
Sentimentanalyse
Ord indlejringer
Maskinoversættelse
Tekstopsummering

Tekstklassificering
Navngivet enhedsgenkendelse
Sprogmodellering
Informationsekstraktion
Emnemodellering

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9 reasons why

Adaface Natural Language Processing (NLP) Assessment Test is the most accurate way to shortlist NLP Engineers

Reason #1

Tests for on-the-job skills

The Natural Language Processing (NLP) test helps recruiters and hiring managers identify qualified candidates from a pool of resumes, and helps in taking objective hiring decisions. It reduces the administrative overhead of interviewing too many candidates and saves time by filtering out unqualified candidates at the first step of the hiring process.

The test screens for the following skills that hiring managers look for in candidates:

Evne til at tokenisere tekst effektivt
Dygtighed til at klassificere tekst i forskellige kategorier
Kapacitet til at analysere følelser i tekst
Kendskab til at anerkende navngivne enheder i tekst
Ekspertise i at bruge ord indlejringer
Kendskab til at opbygge sprogmodeller
Dygtighed til at oversætte tekst fra et sprog til et andet
Evne til at udtrække information fra tekst
Ekspertise i generering af tekstoversigter
Dygtighed til at udføre emnemodellering

Reason #2

No trick questions

Traditional assessment tools use trick questions and puzzles for the screening, which creates a lot of frustration among candidates about having to go through irrelevant screening assessments.

View sample questions

The main reason we started Adaface is that traditional pre-employment assessment platforms are not a fair way for companies to evaluate candidates. At Adaface, our mission is to help companies find great candidates by assessing on-the-job skills required for a role.

Why we started Adaface

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Reason #3

Non-googleable questions

We have a very high focus on the quality of questions that test for on-the-job skills. Every question is non-googleable and we have a very high bar for the level of subject matter experts we onboard to create these questions. We have crawlers to check if any of the questions are leaked online. If/ when a question gets leaked, we get an alert. We change the question for you & let you know.

How we design questions

Dette er kun en lille prøve fra vores bibliotek med 10.000+ spørgsmål. De faktiske spørgsmål om dette Natural Language Processing (NLP) online test vil være ikke-gåbart.

🧐 Question
Medium Hate Speech Detection Challenge Text Classification Data Imbalance	Try practice test
You are working on a project to detect hate speech in social media posts. Your initial model, a basic binary classification model, has achieved high accuracy during training, but it's not performing well on the validation set. You also notice that your dataset has significantly more non-hate-speech examples than hate-speech examples. Given this situation, which of the following strategies could likely improve the performance of your model? A: Collect more data and retrain the model. B: Introduce data augmentation techniques specifically for hate-speech examples. C: Change the model architecture from binary classification to multi-class classification. D: Replace all the words in the posts with their synonyms to increase the diversity of the data. E: Remove the non-hate-speech examples from the dataset to focus on the hate-speech examples.
Easy Identifying Fake Reviews Text Classification	Try practice test
You are a data scientist at an online marketplace company. Your task is to develop a solution to identify fake reviews on your platform. You have a dataset where each review is marked as either 'genuine' or 'fake'. After developing an initial model, you find that it's accurately classifying 'genuine' reviews but performing poorly with 'fake' ones. Which of the following steps can likely improve your model's performance in this context? A: Use a more complex model to capture the intricacies of 'fake' reviews. B: Obtain more data to improve the overall performance of the model. C: Implement a cost-sensitive learning approach, placing a higher penalty on misclassifying 'fake' reviews. D: Translate the reviews to another language and then back to the original language to enhance their clarity. E: Remove the 'genuine' reviews from your training set to focus on 'fake' reviews.
Medium Sentence probability N-Grams Language Models	Try practice test
Consider the following pseudo code for calculating the probability of a sentence using a bigram language model: Assume that the bigram and unigram counts are as follows: bigram_counts = {("i", "like"): 2, ("like", "cats"): 1, ("cats", "too"): 1} unigram_counts = {"i": 2, "like": 2, "cats": 2, "too": 1} vocabulary_size = 4 What is the probability of the sentence "I like cats too" using the bigram language model?
Easy Tokenization and Stemming Stemming	Try practice test
You are working on a natural language processing project and need to preprocess the text data for further analysis. Your task is to tokenize the text and apply stemming to the tokens. Assuming you have an English text corpus, which of the following combinations of tokenizer and stemmer would most likely result in the best balance between token granularity and generalization?
Medium Word Sense Disambiguation	Try practice test
You have been provided with a pre-trained BERT model (pretrained_bert_model) and you need to perform Word Sense Disambiguation (WSD) on the word "bat" in the following sentence: "The bat flew around the room." You have also been provided with a function called cosine_similarity(vec1, vec2) that calculates the cosine similarity between two vectors. Which of the following steps should you perform to disambiguate the word "bat" in the given sentence using the BERT model and cosine similarity? 1. Tokenize the sentence and pass it through the pre-trained BERT model. 2. Extract the embeddings of the word "bat" from the sentence. 3. Calculate the cosine similarity between the "bat" embeddings and each sense's representative words. 4. Choose the sense with the highest cosine similarity. 5. Calculate the Euclidean distance between the "bat" embeddings and each sense's representative words. 6. Choose the sense with the lowest Euclidean distance.

	🧐 Question	🔧 Skill
	Medium Hate Speech Detection Challenge Text Classification Data Imbalance	2 mins Natural Language Processing	Try practice test
You are working on a project to detect hate speech in social media posts. Your initial model, a basic binary classification model, has achieved high accuracy during training, but it's not performing well on the validation set. You also notice that your dataset has significantly more non-hate-speech examples than hate-speech examples. Given this situation, which of the following strategies could likely improve the performance of your model? A: Collect more data and retrain the model. B: Introduce data augmentation techniques specifically for hate-speech examples. C: Change the model architecture from binary classification to multi-class classification. D: Replace all the words in the posts with their synonyms to increase the diversity of the data. E: Remove the non-hate-speech examples from the dataset to focus on the hate-speech examples.
	Easy Identifying Fake Reviews Text Classification	2 mins Natural Language Processing	Try practice test
You are a data scientist at an online marketplace company. Your task is to develop a solution to identify fake reviews on your platform. You have a dataset where each review is marked as either 'genuine' or 'fake'. After developing an initial model, you find that it's accurately classifying 'genuine' reviews but performing poorly with 'fake' ones. Which of the following steps can likely improve your model's performance in this context? A: Use a more complex model to capture the intricacies of 'fake' reviews. B: Obtain more data to improve the overall performance of the model. C: Implement a cost-sensitive learning approach, placing a higher penalty on misclassifying 'fake' reviews. D: Translate the reviews to another language and then back to the original language to enhance their clarity. E: Remove the 'genuine' reviews from your training set to focus on 'fake' reviews.
	Medium Sentence probability N-Grams Language Models	2 mins Natural Language Processing	Try practice test
Consider the following pseudo code for calculating the probability of a sentence using a bigram language model: Assume that the bigram and unigram counts are as follows: bigram_counts = {("i", "like"): 2, ("like", "cats"): 1, ("cats", "too"): 1} unigram_counts = {"i": 2, "like": 2, "cats": 2, "too": 1} vocabulary_size = 4 What is the probability of the sentence "I like cats too" using the bigram language model?
	Easy Tokenization and Stemming Stemming	2 mins Natural Language Processing	Try practice test
You are working on a natural language processing project and need to preprocess the text data for further analysis. Your task is to tokenize the text and apply stemming to the tokens. Assuming you have an English text corpus, which of the following combinations of tokenizer and stemmer would most likely result in the best balance between token granularity and generalization?
	Medium Word Sense Disambiguation	2 mins Natural Language Processing	Try practice test
You have been provided with a pre-trained BERT model (pretrained_bert_model) and you need to perform Word Sense Disambiguation (WSD) on the word "bat" in the following sentence: "The bat flew around the room." You have also been provided with a function called cosine_similarity(vec1, vec2) that calculates the cosine similarity between two vectors. Which of the following steps should you perform to disambiguate the word "bat" in the given sentence using the BERT model and cosine similarity? 1. Tokenize the sentence and pass it through the pre-trained BERT model. 2. Extract the embeddings of the word "bat" from the sentence. 3. Calculate the cosine similarity between the "bat" embeddings and each sense's representative words. 4. Choose the sense with the highest cosine similarity. 5. Calculate the Euclidean distance between the "bat" embeddings and each sense's representative words. 6. Choose the sense with the lowest Euclidean distance.

	🧐 Question	🔧 Skill	💪 Difficulty	⌛ Time
	Hate Speech Detection Challenge Text Classification Data Imbalance	Natural Language Processing	Medium	2 mins	Try practice test
You are working on a project to detect hate speech in social media posts. Your initial model, a basic binary classification model, has achieved high accuracy during training, but it's not performing well on the validation set. You also notice that your dataset has significantly more non-hate-speech examples than hate-speech examples. Given this situation, which of the following strategies could likely improve the performance of your model? A: Collect more data and retrain the model. B: Introduce data augmentation techniques specifically for hate-speech examples. C: Change the model architecture from binary classification to multi-class classification. D: Replace all the words in the posts with their synonyms to increase the diversity of the data. E: Remove the non-hate-speech examples from the dataset to focus on the hate-speech examples.
	Identifying Fake Reviews Text Classification	Natural Language Processing	Easy	2 mins	Try practice test
You are a data scientist at an online marketplace company. Your task is to develop a solution to identify fake reviews on your platform. You have a dataset where each review is marked as either 'genuine' or 'fake'. After developing an initial model, you find that it's accurately classifying 'genuine' reviews but performing poorly with 'fake' ones. Which of the following steps can likely improve your model's performance in this context? A: Use a more complex model to capture the intricacies of 'fake' reviews. B: Obtain more data to improve the overall performance of the model. C: Implement a cost-sensitive learning approach, placing a higher penalty on misclassifying 'fake' reviews. D: Translate the reviews to another language and then back to the original language to enhance their clarity. E: Remove the 'genuine' reviews from your training set to focus on 'fake' reviews.
	Sentence probability N-Grams Language Models	Natural Language Processing	Medium	2 mins	Try practice test
Consider the following pseudo code for calculating the probability of a sentence using a bigram language model: Assume that the bigram and unigram counts are as follows: bigram_counts = {("i", "like"): 2, ("like", "cats"): 1, ("cats", "too"): 1} unigram_counts = {"i": 2, "like": 2, "cats": 2, "too": 1} vocabulary_size = 4 What is the probability of the sentence "I like cats too" using the bigram language model?
	Tokenization and Stemming Stemming	Natural Language Processing	Easy	2 mins	Try practice test
You are working on a natural language processing project and need to preprocess the text data for further analysis. Your task is to tokenize the text and apply stemming to the tokens. Assuming you have an English text corpus, which of the following combinations of tokenizer and stemmer would most likely result in the best balance between token granularity and generalization?
	Word Sense Disambiguation	Natural Language Processing	Medium	2 mins	Try practice test
You have been provided with a pre-trained BERT model (pretrained_bert_model) and you need to perform Word Sense Disambiguation (WSD) on the word "bat" in the following sentence: "The bat flew around the room." You have also been provided with a function called cosine_similarity(vec1, vec2) that calculates the cosine similarity between two vectors. Which of the following steps should you perform to disambiguate the word "bat" in the given sentence using the BERT model and cosine similarity? 1. Tokenize the sentence and pass it through the pre-trained BERT model. 2. Extract the embeddings of the word "bat" from the sentence. 3. Calculate the cosine similarity between the "bat" embeddings and each sense's representative words. 4. Choose the sense with the highest cosine similarity. 5. Calculate the Euclidean distance between the "bat" embeddings and each sense's representative words. 6. Choose the sense with the lowest Euclidean distance.

Reason #4

1200+ customers in 75 countries

Med Adaface var vi i stand til at optimere vores indledende screeningsproces med op mod 75 %, hvilket frigjorde kostbar tid for både ansættelsesledere og vores talentanskaffelsesteam!

Brandon Lee, Leder af mennesker, Love, Bonito

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Reason #5

Designed for elimination, not selection

The most important thing while implementing the pre-employment Natural Language Processing (NLP) online test in your hiring process is that it is an elimination tool, not a selection tool. In other words: you want to use the test to eliminate the candidates who do poorly on the test, not to select the candidates who come out at the top. While they are super valuable, pre-employment tests do not paint the entire picture of a candidate’s abilities, knowledge, and motivations. Multiple easy questions are more predictive of a candidate's ability than fewer hard questions. Harder questions are often "trick" based questions, which do not provide any meaningful signal about the candidate's skillset.

Science behind Adaface tests

Reason #6

1 click candidate invites

Email invites: You can send candidates an email invite to the Natural Language Processing (NLP) online test from your dashboard by entering their email address.

Public link: You can create a public link for each test that you can share with candidates.

API or integrations: You can invite candidates directly from your ATS by using our pre-built integrations with popular ATS systems or building a custom integration with your in-house ATS.

Reason #7

Detailed scorecards & benchmarks

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Reason #8

High completion rate

Adaface tests are conversational, low-stress, and take just 25-40 mins to complete.

This is why Adaface has the highest test-completion rate (86%), which is more than 2x better than traditional assessments.

Reason #9

Advanced Proctoring

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About the Natural Language Processing (NLP) Online Test

Why you should use Pre-employment Natural Language Processing (NLP) test?

The Natural Language Processing (NLP) online test makes use of scenario-based questions to test for on-the-job skills as opposed to theoretical knowledge, ensuring that candidates who do well on this screening test have the relavant skills. The questions are designed to covered following on-the-job aspects:

Forståelse og anvendelse af tokeniseringsteknikker
Implementering af tekstklassificeringsalgoritmer
Analyse og fortolkning af stemning i tekst
Identificering og uddrag af navngivne enheder
Brug af ordindlejringer til naturlige sprogopgaver
Bygning af sprogmodeller til tekstgenerering
Oversættelse af tekst mellem sprog ved hjælp af maskinoversættelse
Uddrag værdifulde oplysninger fra ustruktureret tekst
Oprettelse af kortfattede resume af tekstdata
Opdage emner og mønstre i tekst gennem emnetmodellering

Once the test is sent to a candidate, the candidate receives a link in email to take the test. For each candidate, you will receive a detailed report with skills breakdown and benchmarks to shortlist the top candidates from your pool.

What topics are covered in the Natural Language Processing (NLP) test?

tokenization
tokenization er processen med at opdele en tekst eller sætning i individuelle tokens eller ord. Det er et vigtigt trin i NLP-opgaver, da det giver en struktureret repræsentation af tekstdata, hvilket gør det lettere til videre behandling og analyse.
tekstklassificering
Tekstklassificering involverer tildeling af foruddefineret Kategorier eller etiketter til tekstdata baseret på dets indhold. Denne færdighed er vigtig i NLP til automatisk at kategorisere store mængder tekst, hvilket muliggør effektiv information om information og organisering Stykke tekst, hvad enten det er positivt, negativt eller neutralt. Denne færdighed er værdifuld til at forstå forbrugerudtalelser, sociale medier og kundefeedback.
navngivet enhedsgenkendelse
Navngivet enhedsgenkendelse involverer at identificere og klassificere navngivne enheder, såsom navne, datoer, placeringer og organisationer inden for en tekst. Denne færdighed hjælper med at udtrække værdifuld information og relationer fra ustruktureret tekst, der hjælper med opgaver som informationsekstraktion og videngrafgenerering.
ord indlejringer
Word -indlejringer er vektorrepræsentationer af ord, der fanger semantisk og Syntaktiske forhold. Denne færdighed gør det muligt for kodning af tekst til numeriske vektorer, hvilket letter maskinlæringsalgoritmer til at forstå betydningen og konteksten af ord.
sprogmodellering
Sprogmodellering involverer at forudsige det næste ord i en række baseret på de foregående ord. Det er vigtigt i applikationer som talegenkendelse, maskinoversættelse og autofuldførelse, da det hjælper eller tale fra et sprog til et andet. Denne færdighed er afgørende for at nedbryde sprogbarrierer, hvilket muliggør kommunikation og informationsudveksling på tværs af forskellige kulturer og regioner.
informationsekstraktion
Informationsekstraktion involverer automatisk at udtrække struktureret information fra ustruktureret tekst. Denne færdighed hjælper med opgaver som at udtrække personlige detaljer fra genoptagelser, udtrække fakta fra nyhedsartikler og organisere information til videngrafbygning.
tekstopsummering
tekstopsamling er processen med at kondensere en Stor mængde tekst til en kortere og kortfattet resume, mens du bevarer de væsentlige oplysninger. Denne færdighed er nyttig til at generere udøvende resume, hvilket giver et hurtigt overblik over lange dokumenter eller artikler.
Emne -modellering
Emne -modellering er en statistisk metode, der identificerer latente emner inden for en samling af dokumenter . Denne færdighed hjælper med at opdage skjulte mønstre og temaer i tekstdata, der muliggør opgaver som indholdsanbefaling, dokumentklynge og trendanalyse.
Full list of covered topics
The actual topics of the questions in the final test will depend on your job description and requirements. However, here's a list of topics you can expect the questions for Natural Language Processing (NLP) online test to be based on.
Tokenisering
Stop ord
Stemming
Lemmatisering
Del-af-tale-tagging
N-grams
Bag-af-ord
TF-IDF
Tekstklassificeringsalgoritmer
Naive Bayes
Understøtt vektormaskiner
Neurale netværk
Sentimentanalysemetoder
Lexicon-baseret tilgang
Maskinindlæringsbaseret tilgang
Navngivne enhedsgenkendelsesteknikker
Regelbaserede metoder
Betingede tilfældige felter
Ord indlejringer
Word2Vec
Handske
FastText
Sprogmodelleringsteknikker
N-gram modeller
Gentagne neurale netværk (RNN)
SEQ2SEQ -modeller
Maskinoversættelse nærmer sig
Statistisk maskineoversættelse
Neural Machine Translation
Informationsekstraktionsmetoder
Navngivet enhedsekstraktion
Forholdsekstraktion
Tekstopsummeringsalgoritmer
Ekstraktionsbaseret opsummering
Abstrakt opsummering
Emnetmodelleringsalgoritmer
Latent Dirichlet Allocation (LDA)
Latent semantisk analyse (LSA)
Hierarkisk Dirichlet Process (HDP)
Dokumentklynger

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What roles can I use the Natural Language Processing (NLP) test for?

NLP Engineer
Machine Learning Engineer
Kunstig intelligensforsker
Business analytiker
NLP -forsker

How is the Natural Language Processing (NLP) test customized for senior candidates?

For intermediate/ experienced candidates, we customize the assessment questions to include advanced topics and increase the difficulty level of the questions. This might include adding questions on topics like

Design og udvikling af NLP-baserede applikationer
Anvendelse af avancerede teknikker til forarbejdning af tekst
Optimering af NLP -modeller til ydeevne og skalerbarhed
Håndtering af store tekstdatasæt
Bygning og implementering af NLP -rørledninger
Udvikling af algoritmer til tekstlighed og klynge
Forbedring af modelnøjagtighed gennem dataforstørrelse
Implementering af dybe læringsmodeller til NLP
Udførelse af datarensning og forarbejdning til NLP -opgaver
Analyse og forståelse af sproglige træk i tekst

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Ansættelseslederne mente, at de gennem de tekniske spørgsmål, som de stillede under panelinterviewene, var i stand til at fortælle, hvilke kandidater der havde bedre score og differentieret sig med dem, der ikke scorede så godt. De er meget tilfreds med kvaliteten af de kandidater, der er nomineret med Adaface-screeningen.

85%

Reduktion i screeningstid

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Natural Language Processing (NLP) Hiring Test Ofte stillede spørgsmål

Kan jeg kombinere flere færdigheder i en brugerdefineret vurdering?

Ja absolut. Brugerdefinerede vurderinger er oprettet baseret på din jobbeskrivelse og vil omfatte spørgsmål om alle must-have-færdigheder, du angiver.

Har du nogen anti-cheating eller proctoring-funktioner på plads?

Vi har følgende anti-cheating-funktioner på plads:

Ikke-gåbare spørgsmål
IP Proctoring
Webproctoring
Webcam Proctoring
Detektion af plagiering
Sikker browser

Læs mere om Proctoring Features.

Hvordan fortolker jeg testresultater?

Den primære ting at huske på er, at en vurdering er et elimineringsværktøj, ikke et udvælgelsesværktøj. En færdighedsvurdering er optimeret for at hjælpe dig med at eliminere kandidater, der ikke er teknisk kvalificerede til rollen, den er ikke optimeret til at hjælpe dig med at finde den bedste kandidat til rollen. Så den ideelle måde at bruge en vurdering på er at beslutte en tærskelværdi (typisk 55%, vi hjælper dig med benchmark) og inviterer alle kandidater, der scorer over tærsklen for de næste interviewrunder.

Hvilken oplevelsesniveau kan jeg bruge denne test til?

Hver Adaface -vurdering tilpasses til din jobbeskrivelse/ ideel kandidatperson (vores emneeksperter vælger de rigtige spørgsmål til din vurdering fra vores bibliotek på 10000+ spørgsmål). Denne vurdering kan tilpasses til ethvert erfaringsniveau.

Får hver kandidat de samme spørgsmål?

Ja, det gør det meget lettere for dig at sammenligne kandidater. Valgmuligheder for MCQ -spørgsmål og rækkefølgen af spørgsmål randomiseres. Vi har anti-cheating/proctoring funktioner på plads. I vores virksomhedsplan har vi også muligheden for at oprette flere versioner af den samme vurdering med spørgsmål om lignende vanskelighedsniveauer.