Quite often in natural language processing, two entities are mentioned in close proximity. For example, a sentence may mention that the place is near a certain location or that it’s nearby. In cases such as these, entity reference resolution (ERR) algorithms are useful to determine whether the entities mentioned are the same or different. Such algorithms identify which tokens refer to which other tokens to resolve an ambiguity as quickly and accurately as possible. Let’s take a look at how this works with examples.
Introduction to Natural Language Processing
Natural language processing (NLP) is a field of computer science and engineering that studies how computers can successfully understand, process, and respond to human language. NLP tools can be used to analyze and model textual data, including written and spoken language. There are many different types of NLP problems, each with their own requirements, so it can be a bit overwhelming to know where to start. The first step is to understand the difference between text processing and natural language processing. Text processing is about extracting information from texts, while NLP is about understanding and extracting information from texts. The key terms to know before diving into NLP are unsupervised learning and supervised learning.
How ERR Algorithms Work?
Entity reference resolution algorithms are used to determine the relationship between two entities. Let’s say, for example, we’re analyzing tweets about the movie Fifty Shades of Grey and we encounter the sentence, “Grey and Anastasia in the elevator.” We would like to know whether Grey is the name of the movie, or the actor in the movie, so the entity relationship would be resolved for this tweet. To solve this problem, we would use a collection of algorithms to help us determine which tokens refer to which other tokens. Let’s walk through these algorithms one by one. First, we would use a linguistics library to look up the lemmatization of the text and then use that to search the corpus for the word Grey. We would then use a POS-based n-gram model to determine the position of the next word and the entity-specific tagger to find the words that are tagged with the entity Grey. Once all those tokens have been found in the corpus, we would use a word embedding model to create word vectors that are word vectors for the words in the tweet. With those vectors, we would then use a unsupervised neural network to create a model that would identify new patterns in the tweets that would tell us the next tokens to look out for. This process of identifying patterns in the data is unsupervised learning, and the model is an example of a feature. If a model is a feature, then what are the features for the entities mentioned in this tweet? To find that out, we would use a supervised learning algorithm. In a supervised learning algorithm, we would use the model to predict which tokens to look out for in the next batch of tweets and use the prediction to look them up in the corpus. Once we’ve found all the tokens, we would then use a dictionary to determine the relationship between Grey and Anastasia.
BLEU Scores and Word vectors
BLEU scores are a way to measure the performance of entity reference resolution algorithms. BLEU stands for “Benchmark for the Evaluation of English,” and the scores are calculated using a variety of metrics to evaluate how well an algorithm performs. BLEU scores include a mean-average-standard-deviation (MADS) value, which is a statistical value that shows how well an algorithm performs at different times. In entity reference resolution, we would like to work out which word belongs to which entity. The first step is to create word vectors that represent each word in the corpus along with the character probabilities for each word. Pairs of words with similar character probabilities are considered to be similar in terms of syntactic structure, which makes them suitable for unsupervised learning. These word vectors are then used to create word embeddings, which are a method of storing words as vectors. Word embeddings use a technique called max-margin embedding that assigns each word an optimal amount of space while taking into account the syntactic structure of the sentence. The next step is to use this word embedding model to create word embedding vectors that represent each token in the tweet.
POS Tagger and Named Entity Recognizers
Now that we have word embeddings for the entities mentioned in the tweet, we can use them to find the probability of each token in the tweet. We would then use a POS-based n-gram model to tag each token with the position of the next word in the tweet. The final step would be to use a named entity recognizer to identify each named entity mentioned in the tweet. Named entity recognition has become a necessary part of entity reference resolution, as the models for POS tagging don’t work for some of the named entities, like locations and people.
Entity reference resolution is an important aspect of natural language processing used to resolve which tokens refer to which other tokens. Unsupervised neural network models are used to identify new patterns in the data, which reveals the next tokens to look out for. BLEU scores are a way to measure the performance of entity reference resolution algorithms, and word embeddings are used to tag tokens with word vectors. Related article: Coreference resolution