What is Semantic Similarity? An Engineer's Guide
Semantic similarity refers to the degree of overlap or resemblance in meaning between two pieces of text, phrases, sentences, or larger chunks of text, even if they are phrased differently.
Uses for Semantic Similarity
Semantic similarity has diverse applications, such as:
Search Engine Optimization
Answering Questions: Semantic similarity can be used as a form of fuzzy logic to answer a question similar to that posed by the user. Often, as the user navigates to the exact solution they want, their question is not precise. Semantic similarity prepares answers to the user’s question that are close to the question posed.
Retrieving Information: The search process finds information relevant to the subject of a query, and then ranking the results based on their relevance to the query. The search can include big data databases and other local and remote information sources. Many search engines use some kind of AI, and Microsoft recently announced that Microsoft Edge uses AI techniques to retrieve information.
Another application of semantic similarity is to ensure that the intended meaning is transferred correctly to a target language during translation. AI is being used widely in this area.
Evaluating Originality - Detecting Plagiarism
Semantic similarity is used to identify sentences or phrases that convey similar meanings to each other, but are phrased differently. One specific use is to detect plagiarism where an author merely rephrased the source text. Teachers and others can also use semantic similarity to detect instances of plagiarism in which content is directly copied.
NLP and Text Representation
NLP focuses on the interaction between computers and human language to enable machines to understand, interpret, and generate human language.
Text representation is a fundamental aspect of NLP, as it involves converting raw text into a format that can be processed and understood by machine learning algorithms. Correct text representation is crucial for tasks like sentiment analysis, machine translation, document classification, and semantic similarity measurement. It is key to the operation of search engines. Following are some key methods of text representation in NLP.
Bag of Words (BoW)
BoW is a simple text representation method that treats a document as a collection of words, ignoring grammar and word order. It creates a vocabulary of unique words from the entire body of text under consideration, and represents each document as a vector where each element corresponds to the count or presence of a word in the vocabulary. BoW is straightforward but lacks context and semantic meaning.
Term Frequency-Inverse Document Frequency (TF-IDF)
TF-IDF is an enhancement of the BoW model that takes into account the importance of words in a document relative to the entire corpus. It assigns a weight to each word in a document based on its frequency in the document relative to its frequency across the entire corpus. Words that appear frequently in a document but rarely in the corpus receive higher weights.
Word embeddings are dense, continuously-valued vector representations of words in a high-dimensional space. Methods like Word2Vec, GloVe (Global Vectors for Word Representation), and FastText learn embeddings by considering the context in which words appear in a large corpus. These embeddings capture semantic relationships between words. Word embeddings are used for tasks like word analogy, lexical similarity, and text classification.
It might seem at first glance that there is little or no difference between analogy and similarity. However, there is a difference which will affect how two pieces of text are related.
An analogy is a comparison between two things or concepts that are different in many aspects but share certain similarities in one or more features. It's a way of explaining or understanding something complex by drawing parallels to something simpler or more familiar. Analogies help convey abstract or complex ideas by connecting them to more easily understandable concepts.
Similarity, on the other hand, refers to the degree of likeness or resemblance between two or more things or concepts. It focuses on the shared characteristics or qualities that make them alike, even if they aren't directly related or comparable in the same way as analogies.
In summary, an analogy is a form of comparison used to explain complex ideas by likening them to simpler concepts, while similarity is about identifying common traits or features between two or more things, regardless of whether they are directly related or used in a comparison.
Contextual embeddings are word representations that capture the meaning of words in context. Google developed BERT (bidirectional encoder representations from transformers). Another generative model is GPT (generative pre-trained transformer). While these models are similar, the fundamental approach is different, taking into account the surrounding context of a word within a sentence. However, both models capture nuances in meaning and sentence structure by pre-training on massive amounts of text data. The intention is to create rich representations.
In some cases, the text under consideration uses complex constructions, including prefixes, roots, and suffixes, or rarely used vocabulary elements. In this case, contextual embedding is not sufficient and subword representations break down words into smaller units, such as character n-grams or byte-pair encodings. This is especially useful for handling out-of-vocabulary words and morphologically rich languages.
Sentence embeddings aim to capture the meaning of entire sentences or phrases. Methods like InferSent and Universal Sentence Encoder use various techniques, including recurrent neural networks (RNNs), convolutional neural networks (CNNs), and attention mechanisms.
Document embeddings represent entire documents using vectors. Techniques like Doc2Vec extend the idea of word embeddings to capture the context and meaning of entire documents.
Some approaches combine different levels of text representation to create hybrid models. For example, using techniques such as word embeddings and sentence embeddings together creates hybrid models that capture both local and global contexts.
The choice of text representation method depends on several factors. These include the task at hand, the amount of available training data, and the desired level of linguistic information to be captured. More recent models, like BERT and GPT, have achieved state-of-the-art performance across various NLP tasks due to their ability to capture context and semantics effectively. There are several types of hybrid models:
Ensemble methods combine the outputs of multiple models to make a final prediction. For semantic similarity, this could involve combining scores from models that use different types of features or techniques.
Machine Learning Fusion
Machine learning techniques, like decision trees, random forests, or neural networks can learn to combine individual model scores based on patterns in the training data.
By using predefined rules, you can combine the outputs of different models in specific ways to capture different aspects of similarity.
Some hybrid models use meta-features, such as the confidence scores of individual models, to guide the final similarity score calculation.
Learning to Rank
In some cases, hybrid models are trained to predict a ranking of text pairs based on human-annotated similarity scores. These models can then be used to rank new pairs of text.
Thus, hybrid models are usually implemented by the sequential application of several specific methods. Each method in the hybrid focuses on a specific aspect of the text under evaluation.
Measuring Semantic Similarity
Several methods exist to quantify semantic similarity. Some common techniques include:
Measures the cosine of the angle between two vectors in the vector space. Higher values indicate greater similarity.
Word Embedding-Based Methods
Utilize pre-trained word embeddings to measure similarity based on vector distances.
Deep learning architectures that learn to predict whether two inputs are similar or dissimilar.
These models attend to specific words in both sentences, emphasizing the important parts for comparison.
Challenges for Semantic Similarity Models
Achieving accurate semantic similarity measurements is challenging due to nuances in language, context, idiomatic expressions, and cultural differences. Additionally, the effectiveness of methods may vary across languages and subject matter areas.
Evaluating Models of Semantic Similarity
Engineers must evaluate the performance of semantic similarity models using appropriate benchmark datasets and metrics. Common evaluation metrics include Pearson correlation, Spearman's rank correlation, and mean squared error.