Bigram in NLP
Two words in a row are called a bigram. When ‘n’ is the number of consecutive words in the sequence, this is the most typical example of an n-gram. Bigrams capture word pairs and offer some information about word order and local context, whereas unigrams represent individual words (n=1).
- Bigram in NLP is produced from neighbouring phrase words. The bigrams for “I am learning NLP” are “I am,” “am learning,” and “learning Natural Language Processing.” The bigrams() method may construct a list of consecutive word pairs from the input list [‘more’, ‘is’,’said’, ‘than’, ‘done’]. TextBlob‘s ngrams() function extracts bigrams when n=2.
- A key element of n-gram language models are bigrams. A bigram language model uses just the identity of the word that comes right before it to estimate the likelihood of a word. There is a Markov assumption behind this. By multiplying the odds of each word given its previous word, one may approximate the likelihood of a phrase or word sequence. A distinct start-of-sentence marker, is frequently used as the preceding word to handle the first word in a sentence. In a similar manner, an end-of-sentence token is employed to ensure that the total probability of all strings equals.
- It is possible to estimate the probability of a particular bigram, P(wn|w\sub>n-1), by applying the Maximum Likelihood Estimate (MLE). The count of the bigram C(w\sub>n-1w\sub>n) is divided by the count of the word that comes before it, C(w\sub>n-1), to determine this. The count C(wn-1) is how many bigrams there are that begin with that word. Bigram count matrices, on the other hand, are frequently quite sparse, which means that many potential bigrams in a given corpus have zero counts. Assigning a zero probability to an unobserved Bigram in NLP would result in the entire sequence having zero probability, which poses a serious challenge for language models.
- Smoothing techniques are employed to solve the sparsity and zero count issues. A certain amount of probability mass is redistributed from seen to unobserved n-grams by smoothing.
- Laplace smoothing, also known as add-one smoothing, is a straightforward technique that modifies the denominator by adding the total vocabulary size, V, to the unigram count and adding one to each Bigram in NLP count. This approach may occasionally give unseen occurrences an excessive amount of probability mass, particularly when dealing with big vocabularies.
- Discounting techniques redistribute probability mass, usually among unseen n-grams, by taking it from observed n-grams. The count of observed n-grams is subtracted from a preset value using absolute discounting.
- More complex techniques, such as Kneser-Ney smoothing and Good-Turing estimation, estimate probability for unseen bigrams using lower-order n-grams (such as unigrams) or take into account the “versatility” of words.
- Using weighted sums, interpolated n-gram language models aggregate probability from many n-gram models (such as bigram and unigram). More reliable probability estimations may result from this. Backoff models are an additional method in which the model “backs off” to a lower-order model, such a bigram or unigram model, if a higher-order n-gram probability is zero or unreliable.
- Bigram in NLP models are essential for comprehending basic language modelling principles, although being less complex than contemporary neural language models. Because they capture more context, higher-order n-grams, such as trigrams (3-grams), 4-grams, and 5-grams, are frequently utilised in practice, especially when there is enough training data. However, because to their sparsity and memory requirements, really high-order n-grams are not feasible since the number of parameters increases exponentially with ‘n’.
Bigrams are employed in additional NLP tasks outside language modelling:
Collocations: Collocations are word pairs that occur together exceptionally frequently and are frequently resistant to replacement, whereas bigrams simply represent any two-word sequence. Collocations can be found by starting with frequent bigrams, however methods to normalise for word frequency are required.
Part-of-Speech Tagging: In addition to the word itself, Bigram in NLP taggers also issue tags based on the likelihood that a tag will be awarded the tag that comes just before it. For tag transitions, this is frequently presented as a Hidden Markov Model (HMM) with a bigram assumption. Errors can be caused by minor problems, such as unseen words or tag sequences. Moreover, Trigram taggers are employed.
Feature Representation: Bigrams can be used into NLP models as features. A “bag-of-bigrams” representation, which can be more effective than a bag-of-words (unigram) representation, counts or shows the presence of bigrams in a text without taking into account their order. Because letter-bigrams (character pairings) are more resilient to words that are not in the lexicon than word-bigrams, they may also be utilized as features, especially for language classification.
Tokenization: Character bigrams, or pairs of characters, are repeatedly combined to create a vocabulary of subword units in Byte Pair Encoding (BPE), a subword tokenization technique.
Evaluation: The overlap of bigrams between a candidate translation and a reference translation is measured by a metric called “bigram precision,” which is used to assess the quality of machine translation. It should be noted that sources that discuss evaluation appear to be more interested in bigrams than unigram precision, even though the BLEU metric incorporates both. To be more precise: BLEU combined unigram, bigram, trigram, and 4-gram accuracy, according to my earlier comment on unigrams. Word-Bigram in NLP and trigrams are discussed in the source, which emphasises bigrams as being more informative than individual words.
It also mentions 4-grams and 5-grams for letters but seldom for words. It does not, however, say BLEU specifically. Bigram assessment in language modelling with Perplexity is mentioned in other places. “Using the Web to obtain frequencies for unseen bigrams” is one of the paper titles listed in the source. The same document is listed as a source. “Critical questions for big data” are mentioned in the source in reference to bigrams. In language modelling, it is common practice to assess model performance using Bigram in NLP probabilities. Papers by Keller and Lapata, Paskin, Church and Gale, and Church are among the sources that provide specific references on bigrams.