Why you should use Natuurlijke taalverwerking (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: Het begrijpen en toepassen van tokenisatietechnieken Tekstclassificatie -algoritmen implementeren Het analyseren en interpreteren van sentiment in tekst Het identificeren en extraheren van genoemde entiteiten Gebruikmakend van woordenverbanden voor natuurlijke taaltaken Taalmodellen bouwen voor het genereren van tekst Tekst vertalen tussen talen met behulp van machine -vertaling Waardevolle informatie extraheren uit ongestructureerde tekst Beknopte samenvattingen van tekstuele gegevens maken Onderwerpen en patronen in tekst ontdekken via onderwerpmodellering

Natuurlijke taalverwerking (NLP) test

Q: What topics are covered in the Natuurlijke taalverwerking (NLP) test?

Tokenisatie Tekstclassificatie Sentiment analyse Genoemde entiteitherkenning Woordinbedding Taalmodellering Machine vertaling Informatie -extractie Tekst samenvatting Onderwerpmodellering

About the test:

De online test van NLP (Natural Language Processing) gebruikt scenario-gebaseerde MCQ's om kandidaten te evalueren op hun kennis van NLP-concepten en -technieken, zoals tekstclassificatie, informatie-extractie, sentimentanalyse en benoemde entiteitherkenning. De test beoordeelt het vermogen van een kandidaat om NLP-technieken toe te passen op real-world problemen en scenario's en effectieve NLP-modellen te ontwerpen.

Covered skills:

Tokenisatie
Sentiment analyse
Woordinbedding
Machine vertaling
Tekst samenvatting

Tekstclassificatie
Genoemde entiteitherkenning
Taalmodellering
Informatie -extractie
Onderwerpmodellering

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9 reasons why

Adaface Natuurlijke taalverwerking (NLP) Assessment Test is the most accurate way to shortlist NLP -ingenieurs

Reason #1

Tests for on-the-job skills

The Natuurlijke taalverwerking (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:

Mogelijkheid om tekst effectief te tokeniseren
Vaardigheid in het classificeren van tekst in verschillende categorieën
Vermogen om sentiment in tekst te analyseren
Vaardigheid bij het herkennen van genoemde entiteiten in tekst
Expertise bij het gebruik van woordverbanden
Vaardigheid in het bouwen van taalmodellen
Vaardigheid in het vertalen van tekst van de ene taal naar de andere
Mogelijkheid om informatie uit tekst te halen
Expertise in het genereren van tekstsamenvattingen
Vaardigheid in het uitvoeren van onderwerpmodellering

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

Dit zijn slechts een klein monster uit onze bibliotheek met meer dan 10.000 vragen. De werkelijke vragen hierover Natural Language Processing (NLP) online test zal niet-googelbaar zijn.

🧐 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

Met Adaface konden we ons eerste screeningproces met ruim 75% optimaliseren, waardoor kostbare tijd vrijkwam voor zowel de rekruteringsmanagers als ons talentacquisitieteam!

Brandon Lee, Hoofd Mensen, 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 Natuurlijke taalverwerking (NLP) Online Test

Why you should use Pre-employment Natuurlijke taalverwerking (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:

Het begrijpen en toepassen van tokenisatietechnieken
Tekstclassificatie -algoritmen implementeren
Het analyseren en interpreteren van sentiment in tekst
Het identificeren en extraheren van genoemde entiteiten
Gebruikmakend van woordenverbanden voor natuurlijke taaltaken
Taalmodellen bouwen voor het genereren van tekst
Tekst vertalen tussen talen met behulp van machine -vertaling
Waardevolle informatie extraheren uit ongestructureerde tekst
Beknopte samenvattingen van tekstuele gegevens maken
Onderwerpen en patronen in tekst ontdekken via onderwerpmodellering

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 Natuurlijke taalverwerking (NLP) test?

tekstclassificatie
Tekstclassificatie omvat het toewijzen van vooraf gedefinieerde toewijzen Categorieën of labels naar tekstgegevens op basis van de inhoud. Deze vaardigheid is belangrijk in NLP om de grote hoeveelheden tekst automatisch te categoriseren, waardoor efficiënte informatie -ophalen en organisatie mogelijk worden gemaakt.
sentimentanalyse
sentimentanalyse beoogt de emotionele toon of sentiment te bepalen in een stuk tekst, of het nu positief, negatief of neutraal is. Deze vaardigheid is waardevol voor het begrijpen van de meningen van consumenten, sociale media -sentiment en feedback van klanten.
genaamd Entity Recognition
De naam van de entiteitsherkenning omvat het identificeren en classificeren van genoemde entiteiten, zoals namen, datums, Locaties en organisaties, binnen een tekst. Deze vaardigheid helpt bij het extraheren van waardevolle informatie en relaties uit ongestructureerde tekst, helpen bij het genereren van taken zoals informatie -extractie en het genereren van kennisgrafiek. Syntactische relaties. Deze vaardigheid maakt de codering van tekst in numerieke vectoren mogelijk, waardoor algoritmen voor het leren van machines de betekenis en context van woorden kunnen begrijpen.
taalmodellering
Taalmodellering omvat het voorspellen van het volgende woord in een sequentie gebaseerd op de vorige woorden. Het is essentieel in toepassingen zoals spraakherkenning, machinevertaling en autocomplete, omdat het helpt coherente en contextueel geschikte tekst te genereren.
machine vertaling
machine vertaling verwijst naar de automatische vertaling van tekst of spraak van de ene taal naar de andere. Deze vaardigheid is cruciaal voor het afbreken van taalbarrières, waardoor communicatie en informatie -uitwisseling in verschillende culturen en regio's mogelijk worden. Deze vaardigheid helpt bij taken zoals het extraheren van persoonlijke gegevens uit cv's, het extraheren van feiten uit nieuwsartikelen en het organiseren van informatie voor de constructie van de kennisgrafiek.
tekstsamenvatting
Tekst Samenvatting is het proces van condensatie van een condensatie van een Grote hoeveelheid tekst in een kortere en beknopte samenvatting terwijl de essentiële informatie wordt bewaard. Deze vaardigheid is handig voor het genereren van uitvoerende samenvattingen, wat een snel overzicht biedt van lange documenten of artikelen.
Onderwerpmodellering
Onderwerpmodellering is een statistische methode die latent onderwerpen identificeert binnen een verzameling documenten . Deze vaardigheid helpt verborgen patronen en thema's te ontdekken in tekstgegevens, waardoor taken zoals inhoudaanbeveling, documentclustering en trendanalyse mogelijk worden gemaakt.
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.
Tokenisatie
Stop woorden
Stemmend
Lemmatisatie
Tagging van het gedeelte van de spraak
N-grammen
Woordenzak
TF-IDF
Tekstclassificatie -algoritmen
Naïeve bayes
Ondersteuning van vectormachines
Neurale netwerken
Sentimentanalysemethoden
Op lexicon gebaseerde aanpak
Op machine learning gebaseerde aanpak
Genoemde entiteitherkenningstechnieken
Regelgebaseerde methoden
Voorwaardelijke willekeurige velden
Woordinbedding
Word2Vec
Handschoen
FastText
Taalmodelleringstechnieken
N-gram modellen
Terugkerende neurale netwerken (RNN)
SEQ2SEQ -modellen
Machine -vertaling nadert
Statistische machine vertaling
Neurale machine vertaling
Informatie -extractiemethoden
Genoemde entiteitsextractie
Relatie -extractie
Tekst summierisatie -algoritmen
Op extractie gebaseerde samenvatting
Abstractieve samenvatting
Onderwerpmodelleringsalgoritmen
Latent Dirichlet Allocation (LDA)
Latente semantische analyse (LSA)
Hiërarchisch Dirichlet -proces (HDP)
Documentclustering

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What roles can I use the Natuurlijke taalverwerking (NLP) test for?

NLP -ingenieur
Ingenieur van machine learning
Kunstmatige intelligentieonderzoeker
Bedrijfsanalist
NLP Research Scientist

How is the Natuurlijke taalverwerking (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

Het ontwerpen en ontwikkelen van op NLP gebaseerde toepassingen
Geavanceerde technieken toepassen voor voorbewerking van tekst
NLP -modellen optimaliseren voor prestaties en schaalbaarheid
Grootschalige tekstgegevenssets afhandelen
NLP -pijpleidingen bouwen en implementeren
Ontwikkeling van algoritmen voor tekstovereenkomst en clustering
Verbetering van de modelnauwkeurigheid door gegevensvergroting
Implementatie van diepe leermodellen voor NLP
Gegevensreiniging en voorbewerking uitvoeren voor NLP -taken
Linguïstische kenmerken analyseren en begrijpen in tekst

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De rekruteringsmanagers waren van mening dat ze door de technische vragen die ze tijdens de panelgesprekken stelden, konden zien welke kandidaten beter scoorden, en onderscheidden ze zich met degenen die niet zo goed scoorden. Zij zijn zeer tevreden met de kwaliteit van de kandidaten op de shortlist van de Adaface-screening.

85%

Vermindering van de screeningstijd

Bekijk volledige case study

Natuurlijke taalverwerking (NLP) Hiring Test Veelgestelde vragen

Kan ik meerdere vaardigheden combineren in één aangepaste beoordeling?

Ja absoluut. Aangepaste beoordelingen zijn opgezet op basis van uw functiebeschrijving en bevatten vragen over alle must-have vaardigheden die u opgeeft.

Heeft u functies tegen latere of proctoring op hun plaats?

We hebben de volgende anti-cheating-functies op zijn plaats:

Niet-googelbare vragen
IP Proctoring
Web Proctoring
Webcam Proctoring
Plagiaatdetectie
Beveilig browser

Lees meer over de Proctoring -functies.

Hoe interpreteer ik testscores?

Het belangrijkste om in gedachten te houden is dat een beoordeling een eliminatietool is, geen selectietool. Een vaardighedenbeoordeling is geoptimaliseerd om u te helpen kandidaten te elimineren die niet technisch gekwalificeerd zijn voor de rol, het is niet geoptimaliseerd om u te helpen de beste kandidaat voor de rol te vinden. Dus de ideale manier om een beoordeling te gebruiken is om een drempelscore te bepalen (meestal 55%, wij helpen u benchmark) en alle kandidaten uit te nodigen die boven de drempel scoren voor de volgende interviewrondes.

Voor welk ervaringsniveau kan ik deze test gebruiken?

Elke ADAFACE -beoordeling is aangepast aan uw functiebeschrijving/ ideale kandidaatpersonage (onze experts van het onderwerp zullen de juiste vragen kiezen voor uw beoordeling uit onze bibliotheek van 10000+ vragen). Deze beoordeling kan worden aangepast voor elk ervaringsniveau.

Krijgt elke kandidaat dezelfde vragen?

Ja, het maakt het veel gemakkelijker voor u om kandidaten te vergelijken. Opties voor MCQ -vragen en de volgorde van vragen worden gerandomiseerd. We hebben anti-cheating/proctoring functies. In ons bedrijfsplan hebben we ook de optie om meerdere versies van dezelfde beoordeling te maken met vragen over vergelijkbare moeilijkheidsniveaus.