NLP stands for Natural Language Processing. When writing an NLP assignment, you will be required to solve complex problems and create innovative applications per the recent technologies. However, there are certain challenges that will lead you to get poor results and waste all your resources.
But, if you use the right approach for your NLP problem, you can avoid these mistakes and finish your work smoothly. We have covered 8 basic mistakes to avoid when working on your NLP papers and have also provided you with solutions if you come across any.
What’s more? Taking help from a team of experts at a reliable assignment writing service such as The Academic Papers UK will also make the job easier for you. Let’s get back to the topic now.
8 Common Mistakes to Avoid When Writing NLP Assignments – 2024 Edition
When writing an NLP assignment, modelling, data and process issues can withhold you from making your machine learning efforts, but they should not have to. Here are examples of some common pitfalls to avoid when working on Natural Language Processing (NLP) papers.
1. Not Considering the Data
The most common issues you will face when handling the machine learning models for your Natural Language Processing assignments are data issues. Considering such problems is important because if you do not view the text data carefully, you will surely miss out on useful insights.
You must also avoid picking the wrong set of data for your observation and experimentation. For example, if you choose the values of the dependent variable, you may fail to see the correct relationship between the independent and dependent variables due to the scattered nature of the data.
2. Not being Attentive towards Data Leakage
Data leakage in NLP tasks usually occurs when there are certain clues in the training data set that are usually not available at the time when you are making predictions. In a Kaggle competition insight, you can see the example of data leakage where the Area Under the Curve (AUC) shifted from 0.9973 to 0.59 after the identification of three kinds of data leakages.
As a rule of thumb, if you are getting extremely good results, i.e. around 90% of best outcomes, you should suspect data leakage in your NLP-based papers.
3. Development of the Test Set
If you want to drive up the performance of the NLP techniques while using a single test data set, you will only be fooling yourself by thinking that your results are better and more accurate than many.
In the figure below, which expresses The dangers of overfitting: a Kaggle post-mortem, you can easily figure out the users who secured good performance/low ranks on the leaderboard.
Make sure that you know that your outcomes on the data test set will differ significantly from the tests you run on a blind set. It can also be seen from the figure below:
4. Not Considering the Model
If you do not even look at your model, how will you know how it is performing? Neural network visualisation tips help you understand the performance of your selected model. To get a better understanding of this concept, look at the figure below.
It shows how Model B has better performance than Model A because it attended to the crucial parts of a review in a better way than Model A. You must also consider using many techniques to better visualise your models.
5. Not Contrasting to a Baseline Model
It is quite understandable for the students to want to work with a complex model in their NLP assignment at the start. But sometimes, just a single neuron has the capacity to perform as well as an intricate neural network with 6 inner layers.
The image below, taken from Neural Network Applications in Earthquake Prediction (1994-2019): Meta-Analytic Insight on their Limitations, shows how only four neural networks were used to predict the locations of aftershocks after an earthquake.
The NLP assignment solution here is to not start with a complex model. Start with a basic baseline model instead, and after learning your way with it, gradually move on to complex machine learning algorithms.
6. Not Understanding the User Requirements
Have you thought about what your users really need you to do when relying on your NLP skills? You will be potentially able to improve your system and, hence, the quality of your work by first identifying what the users want. This goes for both traditional software, as well as machine learning.
You should focus on using user-based design techniques, such as interviews, focus groups, surveys, Wizard of Oz experiments, and others. When developing such a system, make sure that it has the following qualities:
- Desirable
- Useful
- Usable
- Findable
- Credible
- Accessible
- Valuable
7. Not Running the Failure Analysis
If you do not conduct failure analysis or do not know how to do so, you must resolve to take NLP assignment help from your peers. Conducting a failure analysis will help you see the frequency of different sorts of failures occurring in your work.
Consider this chart taken from Towards a Quantitative, Platform-Independent Analysis of Knowledge Systems. It shows the complete failure analysis of a system in response to answering the chemistry exam questions. By running such semantic analysis on different word sense disambiguation in machine translation, you will be best able to enhance the quality of your work.
8. Not Implementing the Previous Solutions
So, do you know all of all the machine learning techniques and solutions to address your issues in the NLP assignment ideas? You must explore a lot of online software to get a grip on language modelling and choose the solutions that work well based on your subject.
What’s more? If you want to automate the machine learning process, you should go on to take help from the tools stated in the Capital One analysis Towards Automated Machine Learning: Evaluation and Comparison of AutoML Approaches and Tools.
To avoid this mistake in your work, you should stay aware of what is happening in the research arena of your field and keep reading recent research articles using search engines such as Semantic Scholar and Google Scholar.
Conclusion
So, this was all about the points and important steps to take care of when working on your Natural Language Processing assignments. The overall concept of machine learning incorporates the basic steps we have discussed in detail, and the correctness of the results depends on the accuracy of these steps.
Once you are analysing the production phase of your work, run the sentiment analysis and perform text classification as a part of speech tagging operations. It will facilitate the writing process for you. Also, be sure to choose the NLP assignment topics which are valuable to your field.
If you need further help with your Natural Language Processing assignment, you should contact The Academic Papers UK. It is the best assignment writing service in the UK and guarantees 100% client satisfaction.