Why NLP is difficult?

Longer documents can cause an increase in the size of the vocabulary as well. This article will discuss how to prepare text through vectorization, hashing, tokenization, and other techniques, to be compatible with machine learning and other numerical algorithms. Google Cloud Natural Language API allows you to extract beneficial insights from unstructured text.

In the beginning of the year 1990s, NLP started growing faster and achieved good process accuracy, especially in English Grammar. In 1990 also, an electronic text introduced, which provided a good resource for training and examining natural language programs. Other factors may include the availability of computers with fast CPUs and more memory. The major factor behind the advancement of natural language processing was the Internet. On a single thread, it’s possible to write the algorithm to create the vocabulary and hashes the tokens in a single pass. However, effectively parallelizing the algorithm that makes one pass is impractical as each thread has to wait for every other thread to check if a word has been added to the vocabulary .

Vocabulary based hashing

In English, there are a lot of words that appear very frequently like “is”, “and”, “the”, and “a”. Stop words might be filtered out before doing any statistical analysis. It is used in applications, such as mobile, home automation, video recovery, dictating to Microsoft Word, voice biometrics, voice user interface, and so on. Microsoft Corporation provides word processor software like MS-word, PowerPoint for the spelling correction. NLU mainly used in Business applications to understand the customer’s problem in both spoken and written language.

This approach, however, doesn’t take full advantage of the benefits of parallelization. Additionally, as mentioned earlier, the vocabulary can become large very quickly, especially for large corpuses containing large documents. A common choice of tokens is to simply take words; in this case, a document is represented as a bag of words .

Advantages of vocabulary based hashing

NLP stands for Natural Language Processing, which is a part of Computer Science, Human language, and Artificial Intelligence. It is the technology that is used by machines to understand, analyse, manipulate, and interpret human’s languages. While doing vectorization by hand, we implicitly created a hash function. Assuming a 0-indexing system, we assigned our first index, 0, to the first word we had not seen. Our hash function mapped “this” to the 0-indexed column, “is” to the 1-indexed column and “the” to the 3-indexed columns.

Lemmatization is the text conversion process that converts a word form into its basic form – lemma. It usually uses vocabulary and morphological analysis and also a definition of the Parts of speech for the words. The stemming and lemmatization object is to convert different word forms, and sometimes derived words, into a common basic form. Natural Language Processing usually signifies the processing of text or text-based information . An important step in this process is to transform different words and word forms into one speech form. Also, we often need to measure how similar or different the strings are.

Tokenization

He has experience in data science and scientific programming life cycles from conceptualization to productization. Edward has developed and deployed numerous simulations, optimization, and machine learning models. His experience includes building software to optimize processes for refineries, pipelines, ports, and drilling companies. In addition, he’s worked on projects to detect abuse in programmatic advertising, forecast retail demand, and automate financial processes. Till the year 1980, natural language processing systems were based on complex sets of hand-written rules. After 1980, NLP introduced machine learning algorithms for language processing.

There are a few disadvantages with vocabulary-based hashing, the relatively large amount of memory used both in training and prediction and the bottlenecks it causes in distributed training. In this article we have reviewed a number of different Natural Language Processing concepts that allow to analyze the text and to solve a number of practical tasks. We highlighted such concepts as simple similarity metrics, text normalization, vectorization, word embeddings, popular algorithms for NLP . All these things are essential for NLP and you should be aware of them if you start to learn the field or need to have a general idea about the NLP. Generally, the probability of the word’s similarity by the context is calculated with the softmax formula. This is necessary to train NLP-model with the backpropagation technique, i.e. the backward error propagation process.

NLP Tutorial

Before getting into the details of how to assure that rows align, let’s have a quick look at an example done by hand. We’ll see that for a short example it’s fairly easy to ensure this alignment as a human. Still, eventually, we’ll have to consider the hashing part of the algorithm to be thorough enough to implement — I’ll cover this after going over the more intuitive part. The results of the same algorithm for three simple sentences with the TF-IDF technique are shown below. Vectorization is a procedure for converting words into digits to extract text attributes and further use of machine learning algorithms.

The results of calculation of cosine distance for three texts in comparison with the first text show that the cosine value tends to reach one and angle to zero when the texts match. Text processing – define all the proximity of words that are near to some text objects. Text Analysis API by AYLIEN is used to derive meaning and insights from the textual content. Syntactic Ambiguity exists in the presence of two or more possible meanings within the sentence. Named Entity Recognition is the process of detecting the named entity such as person name, movie name, organization name, or location.

What is NLP?

Usually, in this case, we use various metrics showing the difference between words. It mainly focuses on the literal meaning of words, phrases, and sentences. For Example, intelligence, intelligent, and intelligently, all these words are originated with a single root word “intelligen.” In English, the word “intelligen” do not have any meaning. Information extraction is one of the most important applications of NLP. It is used for extracting structured information from unstructured or semi-structured machine-readable documents.

This API allows you to perform entity recognition, sentiment analysis, content classification, and syntax analysis in more the 700 predefined categories. It also allows you to perform text analysis in multiple languages such as English, French, Chinese, and German. The Cloud NLP API is used to improve the capabilities of the application using natural language processing technology. It allows you to carry various natural language processing functions like sentiment analysis and language detection.

If we observe that certain tokens have a negligible effect on our prediction, we can remove them from our vocabulary to get a smaller, more efficient and more concise model. In NLP, a single instance is called a document, while a corpus refers to a collection of instances. Depending on the problem at hand, a document may be as simple as a short phrase or name or as complex as an entire book. The first problem one has to solve for NLP is to convert our collection of text instances into a matrix form where each row is a numerical representation of a text instance — a vector. But, in order to get started with NLP, there are several terms that are useful to know. After all, spreadsheets are matrices when one considers rows as instances and columns as features.

Gated recurrent units – the “forgetting” and input filters integrate into one “updating” filter , and the resulting LSTM model is simpler and faster than a standard one. For today Word embedding is one of the best NLP-techniques for text analysis. So, lemmatization procedures provides higher context matching compared with basic stemmer. The algorithm for TF-IDF calculation for one word is shown on the diagram.

Depending on how we map a token to a column index, we’ll get a different ordering of the columns, but no meaningful change in the representation. In other words, the NBA assumes the existence of any feature in the class does not correlate with any other feature. The advantage of this classifier is the small data volume for model training, parameters estimation, and classification. The Translation API by SYSTRAN is used to translate the text from the source language to the target language. You can use its NLP APIs for language detection, text segmentation, named entity recognition, tokenization, and many other tasks.

• Assuming a 0-indexing system, we assigned our first index, 0, to the first word we had not seen.
• The most popular vectorization method is “Bag of words” and “TF-IDF”.
• For Example, intelligence, intelligent, and intelligently, all these words are originated with a single root word “intelligen.” In English, the word “intelligen” do not have any meaning.
• Generally, the probability of the word’s similarity by the context is calculated with the softmax formula.

A vocabulary-based hash function has certain advantages and disadvantages. Although the use of mathematical hash functions can reduce the time taken to produce feature vectors, it does come at a cost, namely the loss of interpretability and explainability. Because it is impossible to map back from a feature’s index to the corresponding tokens efficiently when using a hash function, we can’t determine which token corresponds to which feature. So we lose this information and therefore interpretability and explainability. One downside to vocabulary-based hashing is that the algorithm must store the vocabulary. With large corpuses, more documents usually result in more words, which results in more tokens.

We can therefore interpret, explain, troubleshoot, or fine-tune our model by looking at how it uses tokens to make predictions. We can also inspect important tokens to discern whether their inclusion introduces inappropriate bias to the model. It is used to group different inflected forms of the word, called Lemma. The main difference between Stemming and lemmatization is that it produces the root word, which has a meaning. Stemming is used to normalize words into its base form or root form. A better way to parallelize the vectorization algorithm is to form the vocabulary in a first pass, then put the vocabulary in common memory and finally, hash in parallel.

• Machine translation is used to translate text or speech from one natural language to another natural language.
• Augmented Transition Networks is a finite state machine that is capable of recognizing regular languages.
• Document similarity; A cosine-based similarity measure, and TF-IDF calculations, are available in the NLP.Similarity.VectorSim module.
• The first problem one has to solve for NLP is to convert our collection of text instances into a matrix form where each row is a numerical representation of a text instance — a vector.
• In other words, for any two rows, it’s essential that given any index k, the kth elements of each row represent the same word.

Most words in the corpus will not appear for most documents, so there will be many zero counts for many tokens in a particular document. Conceptually, that’s essentially it, but an important practical consideration to ensure that the columns align in the same way for each row when we form the vectors from these counts. In other words, for any two rows, it’s essential that given any index k, the kth nlp algorithms elements of each row represent the same word. One has to make a choice about how to decompose our documents into smaller parts, a process referred to as tokenizing our document. The set of all tokens seen in the entire corpus is called the vocabulary. IBM Watson API combines different sophisticated machine learning techniques to enable developers to classify text into various custom categories.