NLP -Test (Natural Language Processing)

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

Tokenisierung Textklassifizierung Stimmungsanalyse Genannte Entitätserkennung Worteinbettungen Sprachmodellierung Maschinenübersetzung Informationsextraktion Textübersicht Themenmodellierung

About the test:

Der Online-Test von NLP (Natural Language Processing) verwendet szenariobasierte MCQs, um Kandidaten über ihre Kenntnisse über NLP-Konzepte und -techniken wie Textklassifizierung, Informationsextraktion, Stimmungsanalyse und benannte Entitätserkennung zu bewerten. Der Test bewertet die Fähigkeit eines Kandidaten, NLP-Techniken auf reale Probleme und Szenarien anzuwenden und effektive NLP-Modelle zu entwerfen.

Covered skills:

Tokenisierung
Stimmungsanalyse
Worteinbettungen
Maschinenübersetzung
Textübersicht

Textklassifizierung
Genannte Entitätserkennung
Sprachmodellierung
Informationsextraktion
Themenmodellierung

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9 reasons why

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

Reason #1

Tests for on-the-job skills

The NLP -Test (Natural Language Processing) 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:

Fähigkeit, Text effektiv zu tokenisieren
Geschicklichkeit bei der Klassifizierung von Text in verschiedene Kategorien
Fähigkeit, das Gefühl im Text zu analysieren
Kenntnisse bei der Anerkennung benannter Entitäten im Text
Fachwissen bei der Verwendung von Wortbettendings
Kenntnisse im Aufbau von Sprachmodellen
Fähigkeit, Text von einer Sprache in eine andere zu übersetzen
Fähigkeit, Informationen aus Text zu extrahieren
Expertise bei der Generierung von Textzusammenfassungen
Fähigkeit zur Aufführung von Themenmodellierung

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

Dies sind nur ein kleines Beispiel aus unserer Bibliothek mit mehr als 10.000 Fragen. Die tatsächlichen Fragen dazu Online -Test für natürliche Sprachverarbeitung (NLP) wird nichtgänger sein.

🧐 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

Mit Adaface konnten wir unseren Erstauswahlprozess um mehr als 75 % optimieren und so wertvolle Zeit sowohl für Personalmanager als auch für unser Talentakquiseteam gewinnen!

Brandon Lee, Leiter der Menschen, Love, Bonito

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Reason #5

Designed for elimination, not selection

The most important thing while implementing the pre-employment Online -Test für natürliche Sprachverarbeitung (NLP) 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 Online -Test für natürliche Sprachverarbeitung (NLP) 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

Ansicht der Probe Scorecard

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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 NLP -Test (Natural Language Processing)

Why you should use NLP -Test (Natural Language Processing)?

The Online -Test für natürliche Sprachverarbeitung (NLP) 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:

Tokenisierungstechniken verstehen und anwenden
Implementierung von Textklassifizierungsalgorithmen
Analyse und Interpretation des Gefühls im Text
Identifizieren und Extrahieren benannter Entitäten
Verwendung von Worteinbettungen für natürliche Sprachaufgaben
Aufbau von Sprachmodellen für die Textgenerierung
Text zwischen Sprachen mithilfe von maschineller Übersetzung übersetzen
Extrahieren wertvoller Informationen aus unstrukturiertem Text
Erstellen prägnanter Zusammenfassungen von Textdaten
Entdecken Sie Themen und Muster im Text durch Themenmodellierung

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 NLP -Test (Natural Language Processing)?

Tokenisierung
Tokenisierung ist der Prozess der Aufteilung eines Textes oder Satzes in einzelne Token oder Wörter. Es ist ein wesentlicher Schritt bei NLP-Aufgaben, da es eine strukturierte Darstellung von Textdaten liefert, die es der weiteren Verarbeitung und Analyse erleichtert. Kategorien oder Beschriftungen zu Textdaten basierend auf dem Inhalt. Diese Fähigkeit ist in NLP wichtig, um große Textvolumina automatisch zu kategorisieren und ein effizientes Informationsabruf und -organisation zu ermöglichen. Textstück, ob positiv, negativ oder neutral. Diese Fähigkeit ist wertvoll, um Verbrauchermeinungen, soziale Mediengefühle und Kundenfeedback zu verstehen. Standorte und Organisationen innerhalb eines Textes. Diese Fähigkeit hilft, wertvolle Informationen und Beziehungen aus unstrukturiertem Text zu extrahieren und bei Aufgaben wie Informationsextraktion und Wissensgrafik zu helfen. syntaktische Beziehungen. Diese Fähigkeit ermöglicht die Codierung des Textes in numerische Vektoren und erleichtert Algorithmen des maschinellen Lernens, um die Bedeutung und den Kontext von Wörtern zu verstehen. basierend auf den vorherigen Wörtern. In Anwendungen wie Spracherkennung, maschineller Übersetzung und automatischer Vervollständigung ist es von entscheidender Bedeutung, da sie dazu beiträgt, kohärenten und kontextuell geeigneten Text zu generieren. oder Sprache von einer Sprache zur anderen. Diese Fähigkeit ist entscheidend, um Sprachbarrieren abzubauen und den Kommunikations- und Informationsaustausch in verschiedenen Kulturen und Regionen zu ermöglichen. Diese Fähigkeit hilft bei Aufgaben wie das Extrahieren persönlicher Details aus Lebensläufen, Extrahieren von Fakten aus Nachrichtenartikeln und Organisation von Informationen zur Konstruktion von Wissensgraphen. Eine große Menge an Text in eine kürzere und prägnante Zusammenfassung und gleichzeitig die wesentlichen Informationen. Diese Fähigkeit ist nützlich, um Executive -Zusammenfassungen zu generieren und einen kurzen Überblick über lange Dokumente oder Artikel zu bieten. . Diese Fähigkeit hilft, versteckte Muster und Themen in Textdaten zu entdecken und Aufgaben wie Inhaltsempfehlung, Dokumentclustering und Trendanalyse zu ermöglichen.
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 Online -Test für natürliche Sprachverarbeitung (NLP) to be based on.
Tokenisierung
Stoppen Sie Wörter
Stamm
Lemmatisierung
SPEC-MAGGGING
N-Gramm
Wörterbeutel
Tf-idf
Textklassifizierungsalgorithmen
Naive Bayes
Support-Vektor-Maschinen
Neuronale Netze
Stimmungsanalysemethoden
Lexikonbasierter Ansatz
Basierter Ansatz für maschinelles Lernen
Genannte Entitätserkennungstechniken
Regelbasierte Methoden
Bedingte Zufallsfelder
Worteinbettungen
Word2Vec
Handschuh
FastText
Sprachmodellierungstechniken
N-Gramm-Modelle
Wiederkehrende neuronale Netzwerke (RNN)
SEQ2SEQ -Modelle
Maschinelle Übersetzungsansätze
Statistische maschinelle Übersetzung
Neuralmaschinenübersetzung
Informationsextraktionsmethoden
Genannte Entitätsextraktion
Beziehungsextraktion
Textübersichtsalgorithmen
Extraktionsbasierte Zusammenfassung
Abstraktive Zusammenfassung
Themenmodellierungsalgorithmen
Latent Dirichlet Allocation (LDA)
Latente semantische Analyse (LSA)
Hierarchischer Dirichlet -Prozess (HDP)
Dokumentclustering

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

NLP -Ingenieur
Maschinenlerningenieur
Forscher für künstliche Intelligenz
Business Analyst
NLP -Forschungswissenschaftler

How is the NLP -Test (Natural Language Processing) 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

Entwerfen und Entwicklung von NLP-basierten Anwendungen
Anwenden fortschrittlicher Techniken für die Vorverarbeitung von Text anwenden
Optimierung von NLP -Modellen für Leistung und Skalierbarkeit
Umgang mit groß angelegten Textdatensätzen
Erstellen und Bereitstellen von NLP -Pipelines
Entwicklung von Algorithmen zur Ähnlichkeit und Clusterbildung von Text
Verbesserung der Modellgenauigkeit durch Datenerweiterung
Implementierung von Deep -Learning -Modellen für NLP
Durchführung von Datenreinigung und Vorverarbeitung für NLP -Aufgaben
Analyse und Verständnis von sprachlichen Merkmalen im Text

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Ansicht der Probe Scorecard

Die Personalmanager hatten das Gefühl, dass sie durch die technischen Fragen, die sie während der Panel-Interviews stellten, erkennen konnten, welche Kandidaten bessere Ergebnisse erzielten, und sie von denen unterscheiden konnten, die nicht so gut abschnitten. Sie sind Sehr zufrieden mit der Qualität der Kandidaten, die beim Adaface-Screening in die engere Auswahl kommen.

85%

Verringerung der Screening -Zeit

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NLP -Test (Natural Language Processing) FAQs

Kann ich mehrere Fähigkeiten zu einer benutzerdefinierten Bewertung kombinieren?

Ja absolut. Basierend auf Ihrer Stellenbeschreibung werden benutzerdefinierte Bewertungen eingerichtet und enthalten Fragen zu allen von Ihnen angegebenen Must-Have-Fähigkeiten.

Haben Sie Anti-Cheating- oder Proctoring-Funktionen?

Wir haben die folgenden Anti-Cheating-Funktionen:

Nicht-Googling-Fragen
IP -Verbreitung
Web -Verbreitung
Webcam -Proctoring
Plagiaterkennung
sicherer Browser

Lesen Sie mehr über die Proctoring -Funktionen.

Wie interpretiere ich die Testergebnisse?

Die wichtigste Sache, die Sie beachten sollten, ist, dass eine Bewertung ein Eliminierungswerkzeug ist, kein Auswahlwerkzeug. Eine Bewertung der Qualifikationsbewertung wird optimiert, um Ihnen zu helfen, Kandidaten zu beseitigen, die technisch nicht für die Rolle qualifiziert sind. Sie ist nicht optimiert, um Ihnen dabei zu helfen, den besten Kandidaten für die Rolle zu finden. Die ideale Möglichkeit, eine Bewertung zu verwenden, besteht also darin, einen Schwellenwert zu entscheiden (in der Regel 55%, wir helfen Ihnen bei der Benchmark) und alle Kandidaten einladen, die für die nächsten Interviewrunden über dem Schwellenwert punkten.

Für welche Erfahrung kann ich diesen Test verwenden?

Jede Adaface -Bewertung ist an Ihre Stellenbeschreibung/ ideale Kandidatenpersönlichkeit angepasst (unsere Experten für Fache werden die richtigen Fragen für Ihre Bewertung aus unserer Bibliothek mit über 10000 Fragen auswählen). Diese Einschätzung kann für jede Erfahrungsstufe angepasst werden.

Bekommt jeder Kandidat die gleichen Fragen?

Ja, es macht es Ihnen viel einfacher, Kandidaten zu vergleichen. Optionen für MCQ -Fragen und die Reihenfolge der Fragen werden randomisiert. Wir haben Anti-Cheating/Proctoring Funktionen. In unserem Unternehmensplan haben wir auch die Möglichkeit, mehrere Versionen derselben Bewertung mit Fragen mit ähnlichen Schwierigkeitsgraden zu erstellen.