Are you grappling with the formidable task of developing a text matching system for question matching, as outlined in the assignment below? Crafting a robust system that utilizes both TF-IDF with inverted files and word embeddings to match similar questions for online forums can be a challenging endeavor. Whether you are a student aiming to master the intricacies of data loading, text preprocessing, and building retrieval systems or a professional seeking to enhance your expertise in text matching, CodersArts is here to provide expert guidance tailored to your specific needs.
What We Offer
At CodersArts, we understand the complexities of building effective text retrieval systems, and our expertise extends to both TF-IDF-based approaches and those leveraging word embeddings. In this assignment, we tackle the task of developing a system that matches questions using these two methodologies. Our code covers data loading, text preprocessing, building inverted indices with TF-IDF, and implementing text matching with sentence embeddings. We explore different strategies for creating sentence representations, ensuring a comprehensive approach to the assignment's requirements.
Get Assignment Solution
For a sample assignment solution or assistance with similar tasks, we provide a preview below. If you require a complete solution or have specific questions, don't hesitate to reach out. CodersArts specializes in delivering comprehensive solutions for complex assignments in fields such as text matching, natural language processing, and more. Contact us today to explore how CodersArts can provide you with top-notch solutions tailored to your unique project.
Sample Assignment
Submission
Instructions and submission guidelines:
You must sign an assessment declaration coversheet to submit with your assignment.
Submit your assignment via the Canvas MyUni.
Task
You are required to write code for building a text retrieval/matching system to matching similar questions. This system could be used for online question forums. You need to build this system based on both TF-IDF (with inverted file) and word embeddings. You are encouraged to explore different ways of creating sentence representations from the pretrained word embedding. Examples include directly averaging word embedding, downweight frequent words, or using the method in
Sanjeev Arora, Yingyu Liang, Tengyu Ma, A SIMPLE BUT TOUGH-TO-BEAT BASELINE FOR SENTENCE EMBEDDINGS, ICLR 2017
Specifically, the task included:
Write code for data loading and text preprocessing 5%
Write code for building inverted index with TF-IDF and performing text matching 25%
Write code for building text matching with sentence embedding. The sentence embedding is calculated by averaging word embedding. 10%
Write code for building text matching with sentence embedding. The sentence embedding is calculated by using an alternative strategy other than averaging word embedding, e.g., downweight frequent words, or using the method in Sanjeev Arora, Yingyu Liang, Tengyu Ma, A SIMPLE BUT TOUGH-TO-BEAT BASELINE FOR SENTENCE EMBEDDINGS, ICLR 2017. 10%
Task 1 to 3
The implementation has major error, e.g., misunderstanding of key concepts: 30% of total marks The implementation is partially correct, and has minor mistakes. Depending on the type of mistaken, you can earn 30%-90% of the total markcorrect implementation 100% of the total mark
Task 4
The implementation has major error, e.g., misunderstanding of key concepts: 30% of total marksImplement one sentence embedding approach that is different from average embedding. The implementation is partially correct and has minor mistake. you can earn 30%-70% of the total mark
Programming requirement
You will use Python to write the program. Third party packages are allowed in the following scenarios: (1) read file (2) text preprocessing (3) get word count statistics (4) SVD calculation or other matrix operations.
You need to write code without using the third party packages for (1) calculating TF-IDF (2) building Inverted index for retrieval and search with inverted index. (3) calculating sentence embedding from word embeddings. (4) calculating the sentence similarity
You are also required to submit a report (<10 pages in PDF format), which should have the following sections (report contributes 50% to the mark; code 50%):
A description of how to build sentence representations for both methods (25%)
Comparing the TF-IDF based and sentence embedding based sentence matching system. 15%
Comparing different ways of creating sentence embedding. 10% In summary, you need to submit (1) the code and (2) a report in PDF
Data and Evaluation
The dataset is provided at MyUni. The dataset is a tsv file with following columns:
Id, qid1, qid2, question 1, question 2, is_duplicate
Is_duplicate indicates whether question 1 is similar to question 2.
We will use questions in question 1 as queries and find matched questions in question 2. Specifically, we will use the first 100 questions with is_duplicate = 1 to form 100 queries. Each of those queries will be matched against all the questions in question 2.
The system will be tested by using the following protocol: use each query question as the query and run the system to return a list of questions ranked by their relevance to the query. Check whether the ground-truth answer is ranked among the top 5. Using the probability that ground-truth ranked into top 2 and top 5 as two metrics for the retrieval system.
Your code should support the following interface
>> Python SearchQuestion “your question”
Then the top 5 matched questions will be returned.
Hint
You could try the following command to read the tsv file Data = pd.read_csv(‘data.tsv’,sep=’\t’,error_bad_lines=False)
You may encounter duplicated questions. In that case, you can simply remove the duplicated question
Deliverables You Can Expect
When you choose CodersArts for assistance with assignments like this one, you can expect a complete package. We will provide complete code with a basic detailed report of this project.
How We Can Help You Overcome Challenges
CodersArts offers customized solutions to conquer the complexities of the text matching project:
Expert Guidance: Our seasoned experts in text matching provide comprehensive guidance for tackling intricate tasks using TF-IDF and word embeddings.
Efficient Data Processing: Learn techniques to efficiently load, preprocess, and match extensive datasets, optimizing your text matching tasks for better performance.
Error Handling: Receive timely assistance in debugging and resolving issues that might arise during the development and execution of your text matching solution.
Tailored Support: We provide one-on-one support, tailored to your specific project needs, ensuring you have the resources and guidance necessary to succeed.
Why Choose CodersArts Expertise
Our team comprises seasoned experts with a wealth of experience in text matching and related fields.
Tailored Solutions: We customize our support to your proficiency level and the unique demands of your project.
Timely Support: We recognize the importance of meeting deadlines and provide swift assistance to keep your project on track.
In-Depth Understanding: Our commitment goes beyond completing your assignment; we ensure you thoroughly comprehend the core concepts of text matching.
If you're in search of a solution for this assignment or require assistance with similar projects, feel free to get in touch with us via email at contact@codersarts.com. We're dedicated to providing you with the best solutions tailored to your specific needs. Your success is our priority, and we look forward to being your trusted partner in conquering complex assignments and projects. Reach out to us today and experience the difference CodersArts can make in your academic and professional journey.