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: Forstå og anvende tokeniseringsteknikker Implementering av tekstklassifiseringsalgoritmer Analysere og tolke stemning i tekst Identifisere og trekke ut navngitte enheter Bruke ordinnstilling for naturlige språkoppgaver Å bygge språkmodeller for tekstgenerering Oversettelse av tekst mellom språk ved bruk av maskinoversettelse Å trekke ut verdifull informasjon fra ustrukturert tekst Opprette kortfattede sammendrag av tekstdata Oppdage emner og mønstre i tekst gjennom emnemodellering

Natural Language Processing (NLP) test

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

Tokenisering Tekstklassifisering Sentimentanalyse Kalt enhetsgjenkjenning Ordinnstilling Språkmodellering Maskinoversettelse Informasjonsutvinning Tekstoppsummering Emne modellering

About the test:

NLP (Natural Language Processing) Online Test bruker scenariobaserte MCQ-er for å evaluere kandidater om deres kunnskap om NLP-konsepter og teknikker, for eksempel tekstklassifisering, informasjonsutvinning, sentimentanalyse og navngitt enhetsgjenkjenning. Testen vurderer en kandidats evne til å anvende NLP-teknikker på problemer i den virkelige verden og scenarier og designe effektive NLP-modeller.

Covered skills:

Tokenisering
Sentimentanalyse
Ordinnstilling
Maskinoversettelse
Tekstoppsummering

Tekstklassifisering
Kalt enhetsgjenkjenning
Språkmodellering
Informasjonsutvinning
Emne modellering

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9 reasons why

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

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 å tokenisere tekst effektivt
Ferdighet i å klassifisere tekst i forskjellige kategorier
Evne til å analysere følelser i tekst
Ferdigheter i å gjenkjenne navngitte enheter i tekst
Kompetanse i å bruke ordinnstilling
Kompetanse i å bygge språkmodeller
Ferdighet i å oversette tekst fra ett språk til et annet
Evne til å hente ut informasjon fra tekst
Kompetanse i å generere tekstsammendrag
Ferdighet i å utføre 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 bare en liten prøve fra biblioteket vårt med 10.000+ spørsmål. De faktiske spørsmålene om dette Natural Language Processing (NLP) Online Test vil være ikke-googlable.

🧐 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 å optimalisere den første screeningsprosessen vår med opp mot 75 %, og frigjorde dyrebar tid for både ansettelsesledere og vårt talentanskaffelsesteam!

Brandon Lee, Leder for 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

Learn more

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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:

Forstå og anvende tokeniseringsteknikker
Implementering av tekstklassifiseringsalgoritmer
Analysere og tolke stemning i tekst
Identifisere og trekke ut navngitte enheter
Bruke ordinnstilling for naturlige språkoppgaver
Å bygge språkmodeller for tekstgenerering
Oversettelse av tekst mellom språk ved bruk av maskinoversettelse
Å trekke ut verdifull informasjon fra ustrukturert tekst
Opprette kortfattede sammendrag av tekstdata
Oppdage emner og mønstre i tekst gjennom emnemodellering

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?

Tokenisering
Tokenisering er prosessen med å dele opp en tekst eller setning til individuelle symboler eller ord. Det er et essensielt trinn i NLP-oppgaver, da det gir en strukturert representasjon av tekstdata, noe kategorier eller etiketter til tekstdata basert på innholdet. Denne ferdigheten er viktig i NLP for automatisk å kategorisere store tekstvolumer, noe som muliggjør effektiv informasjonsinnhenting og organisering.
Sentimentanalyse
Sentimentanalyse tar sikte på å bestemme den emosjonelle tonen eller følelsen uttrykt i en Tekststykke, enten det er positivt, negativt eller nøytralt. Denne ferdigheten er verdifull for å forstå forbrukernes meninger, sosiale mediers følelser og tilbakemeldinger fra kunder.
Navnet Entity Recognition
Navnet enhetsgjenkjenning innebærer å identifisere og klassifisere navngitte enheter, for eksempel navn, datoer, lokasjoner og organisasjoner, innenfor en tekst. Denne ferdigheten hjelper til med å trekke ut verdifull informasjon og relasjoner fra ustrukturert tekst, og hjelper til med oppgaver som informasjonsutvinning og kunnskapsgrafgenerering.
ordinnstilling
Word Embeddings er vektorrepresentasjoner av ord som fanger semantisk og syntaktiske forhold. Denne ferdigheten gjør det mulig for koding av tekst til numeriske vektorer, og letter maskinlæringsalgoritmer for å forstå betydningen og konteksten til ord.
Språkmodellering
Språkmodellering innebærer å forutsi neste ord i en sekvens basert på de tidligere ordene. Det er viktig i applikasjoner som talegjenkjenning, maskinoversettelse og autofullfør, da det hjelper til eller tale fra ett språk til et annet. Denne ferdigheten er avgjørende for å bryte ned språkbarrierer, noe som muliggjør kommunikasjon og informasjonsutveksling på tvers av forskjellige kulturer og regioner.
Informasjonsutvinning
Informasjonsutvinning innebærer automatisk å trekke ut strukturert informasjon fra ustrukturert tekst. Denne ferdigheten hjelper til med oppgaver som å trekke ut personlige detaljer fra CV, trekke ut fakta fra nyhetsartikler og organisere informasjon for kunnskapsgrafkonstruksjon.
Tekstoppsummering
Tekstoppsummering er prosessen med å kondensere a Stor mengde tekst til en kortere og kortfattet sammendrag mens den bevarer den essensielle informasjonen. Denne ferdigheten er nyttig for å generere utøvende sammendrag, noe . Denne ferdigheten hjelper til med å oppdage skjulte mønstre og temaer i tekstdata, og muliggjør oppgaver som innholdsanbefaling, dokumentklynging 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
Stopp ord
Stemming
Lemmatisering
Del-of-tale-tagging
N-gram
Bag-av-ord
TF-IDF
Tekstklassifiseringsalgoritmer
Naive Bayes
Støtt vektormaskiner
Nevrale nettverk
Sentimentanalysemetoder
Lexicon-basert tilnærming
Maskinlæringsbasert tilnærming
Kalt enhetsgjenkjenningsteknikker
Regelbaserte metoder
Betingede tilfeldige felt
Ordinnstilling
Word2Vec
Hanske
FastText
Språkmodelleringsteknikker
N-gram-modeller
Gjentagende nevrale nettverk (RNN)
SEQ2SEQ -modeller
Maskinoversettelsesmetoder
Statistisk maskinoversettelse
Nevrale maskinoversettelse
Informasjonsutvinningsmetoder
Kalt enhetsutvinning
Relasjonsekstraksjon
Tekstoppsummeringsalgoritmer
Ekstraksjonsbasert oppsummering
Abstrakt oppsummering
Emnetodelleringsalgoritmer
Latent Dirichlet Allocation (LDA)
Latent semantisk analyse (LSA)
Hierarkisk Dirichlet Process (HDP)
Dokumentklynging

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

NLP -ingeniør
Machine Learning Engineer
Kunstig intelligensforsker
Forretningsanalytiker
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

Designe og utvikle NLP-baserte applikasjoner
Bruke avanserte teknikker for forbehandling av tekst
Optimalisering av NLP -modeller for ytelse og skalerbarhet
Håndtering av storskala tekstdatasett
Bygge og distribuere NLP -rørledninger
Utvikle algoritmer for tekstlikhet og klynging
Forbedring av modellnøyaktighet gjennom dataforstørrelse
Implementering av dype læringsmodeller for NLP
Utføre datarengjøring og forbehandling for NLP -oppgaver
Analysere og forstå språklige funksjoner i tekst

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Ansettelseslederne mente at de gjennom de tekniske spørsmålene de stilte under panelintervjuene, var i stand til å fortelle hvilke kandidater som scoret bedre, og differensierte med de som ikke skåret like godt. De er svært fornøyd med kvaliteten på kandidatene som er på listen med Adaface-screeningen.

85%

Reduksjon i screeningstid

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Natural Language Processing (NLP) Hiring Test Vanlige spørsmål

Kan jeg kombinere flere ferdigheter til en tilpasset vurdering?

Ja absolutt. Tilpassede vurderinger er satt opp basert på stillingsbeskrivelsen din, og vil inneholde spørsmål om alle må-ha ferdigheter du spesifiserer.

Har du noen anti-juksende eller proktoreringsfunksjoner på plass?

Vi har følgende anti-juksede funksjoner på plass:

Ikke-googlable spørsmål
IP Proctoring
Nettproctoring
Webcam Proctoring
Deteksjon av plagiering
Sikker nettleser

Les mer om Proctoring -funksjonene.

Hvordan tolker jeg testresultater?

Den viktigste tingen å huske på er at en vurdering er et eliminasjonsverktøy, ikke et seleksjonsverktøy. En ferdighetsvurdering er optimalisert for å hjelpe deg med å eliminere kandidater som ikke er teknisk kvalifisert for rollen, det er ikke optimalisert for å hjelpe deg med å finne den beste kandidaten for rollen. Så den ideelle måten å bruke en vurdering på er å bestemme en terskelpoeng (vanligvis 55%, vi hjelper deg med å benchmark) og invitere alle kandidater som scorer over terskelen for de neste rundene med intervjuet.

Hvilken opplevelsesnivå kan jeg bruke denne testen til?

Hver ADAFACE -vurdering er tilpasset din stillingsbeskrivelse/ ideell kandidatperson (våre fageksperter vil velge de riktige spørsmålene for din vurdering fra vårt bibliotek med 10000+ spørsmål). Denne vurderingen kan tilpasses for ethvert opplevelsesnivå.

Får hver kandidat de samme spørsmålene?

Ja, det gjør det mye lettere for deg å sammenligne kandidater. Alternativer for MCQ -spørsmål og rekkefølgen på spørsmål er randomisert. Vi har anti-juksing/proctoring funksjoner på plass. I vår bedriftsplan har vi også muligheten til å lage flere versjoner av den samme vurderingen med spørsmål med lignende vanskelighetsnivåer.