Building a subcorpus can be a useful way to focus on a specific subset of a larger corpus, allowing for more targeted analysis and research. Here are some steps you can take to effectively build your subcorpus:
Define your research question or topic: Before you start building your subcorpus, it's important to have a clear research question or topic in mind. This will help you determine which texts or documents are relevant to your analysis and which ones are not.
Select your criteria: Once you have a research question or topic in mind, you need to determine the criteria for selecting texts or documents for your subcorpus. This could include criteria such as publication date, author, genre, or content.
Gather your texts: Once you have defined your criteria, you can begin gathering texts that meet those criteria. This may involve searching for relevant documents in a larger corpus or manually selecting texts from a specific source.
Clean and preprocess your data: Once you have gathered your texts, it's important to clean and preprocess the data to ensure that it is ready for analysis. This may involve removing punctuation, converting all text to lowercase, and removing stop words.
Analyze your subcorpus: Once you have built your subcorpus, you can begin analyzing it using whatever methods are appropriate for your research question or topic.
Evaluate your subcorpus: After analyzing your subcorpus, it's important to evaluate whether it is representative of the larger corpus and whether it meets your criteria. If necessary, you may need to adjust your criteria or gather additional texts to improve the quality of your subcorpus.
Overall, building an effective subcorpus requires careful planning and attention to detail. By following these steps, you can create a subcorpus that is well-suited to your research question or topic and provides valuable insights for your analysis.
Yes, it is possible to merge subcorpora into a single corpus in Sketch Engine. Here's how you can do it:
First, make sure that you have created and saved all the subcorpora that you want to merge.
Go to the "Corpora" tab in Sketch Engine and select the "Manage Corpora" option.
In the "Manage Corpora" window, select the subcorpora that you want to merge by checking the boxes next to their names.
Once you have selected all the subcorpora, click on the "Merge" button at the top of the window.
In the "Merge Corpora" window, you can specify the name and description for the new merged corpus.
You can also select additional options such as whether to include the original subcorpora in the new merged corpus or to create a new subcorpus for the merged data.
Once you have specified all the options, click on the "Merge" button to create the new merged corpus.
The new merged corpus will appear in your "Corpora" list, and you can use it just like any other corpus in Sketch Engine.
Note that when you merge subcorpora, the resulting corpus will contain all the data from the original subcorpora, so you may want to consider the size of the resulting corpus and how it may affect your analysis.
BERT (Bidirectional Encoder Representations from Transformers) is a powerful language model that has been shown to perform well on a wide range of natural language processing tasks, including sentiment analysis, question answering, and language generation. However, distinguishing between the literal and idiomatic use of expressions can be challenging for any language model, including BERT.
In general, language models like BERT rely on statistical patterns in the training data to make predictions about the meaning of language. This means that BERT may be able to recognize certain idiomatic expressions based on their frequency and context in the training data, but it may not always be able to distinguish between literal and idiomatic use in all cases.
For example, consider the idiom "kick the bucket", which means to die. BERT may be able to recognize this as an idiomatic expression based on its frequency and context in the training data, but it may not always be able to distinguish between literal and idiomatic uses in all cases. If the expression "kick the bucket" appeared in a context where it was being used literally, such as "I accidentally kicked the bucket and spilled water all over the floor", BERT might have difficulty recognizing the literal meaning.
Overall, while BERT and other language models have shown impressive performance on a range of natural language processing tasks, it's important to keep in mind that they are not perfect and may not always be able to distinguish between literal and idiomatic uses of expressions with complete accuracy.
GPT (Generative Pre-trained Transformer) is a powerful language model that has been trained on a large corpus of text and can generate high-quality text in response to various prompts. However, like other language models, GPT may have difficulty distinguishing between literal and idiomatic use of expressions, depending on the specific context.
GPT is trained on a large corpus of text, which means it can recognize idiomatic expressions based on their frequency and context in the training data. However, the ability to distinguish between literal and idiomatic uses of expressions requires a more sophisticated understanding of language and context. For example, consider the idiom "kick the bucket", which means to die. GPT may recognize this as an idiomatic expression based on its frequency and context in the training data, but it may have difficulty recognizing the literal meaning of the expression in certain contexts.
In some cases, GPT may be able to identify idiomatic expressions based on the surrounding context, but this would require a nuanced understanding of the meaning and use of language. For example, if the expression "kick the bucket" appeared in a context where it was being used metaphorically, such as "I kicked the bucket on that project", GPT may be able to recognize the idiomatic meaning based on the surrounding text.
Overall, while GPT and other language models are capable of generating high-quality text, they may have difficulty distinguishing between literal and idiomatic uses of expressions in some contexts.
To measure the difference between literal and idiomatic use of some expressions, you will need a corpus that includes a wide range of text genres and styles. Ideally, the corpus should also include examples of the expressions you are interested in studying, in both literal and idiomatic contexts.
One corpus that could be useful for this type of analysis is the Corpus of Contemporary American English (COCA). COCA is a large corpus of American English that includes over 1 billion words from a wide range of sources, including newspapers, magazines, fiction, academic journals, and spoken language. This corpus is updated regularly and includes a range of search tools and filters that can help you find examples of specific expressions in different contexts.
Another corpus that may be useful is the British National Corpus (BNC), which is a large corpus of British English that includes a wide range of text genres and styles. Like COCA, the BNC includes search tools and filters that can help you find examples of specific expressions in different contexts.
When selecting a corpus, it's important to keep in mind that no corpus is perfect, and each corpus has its own strengths and weaknesses. You may want to consider factors such as the size of the corpus, the range of text genres and styles included, and the availability of search tools and filters when selecting a corpus for your analysis.
To create different word embeddings for idiomatic vs literal expressions, you will need to train separate models on a suitable corpus of text.
Here are some steps you can follow:
Select a suitable corpus: You will need a corpus of text that includes examples of the expressions you are interested in studying, in both literal and idiomatic contexts. The corpus should be large enough to provide sufficient examples for training your models.
Preprocess the corpus: Before training your models, you will need to preprocess the corpus by tokenizing the text, removing stop words, and possibly lemmatizing or stemming the words. This will help to reduce the size of the vocabulary and make the training process more efficient.
Train separate models: Once you have preprocessed the corpus, you can train separate models on the literal and idiomatic expressions. There are a variety of word embedding models you can use, such as Word2Vec or GloVe. You can use the same model for both the literal and idiomatic expressions, or you can use different models to see how they compare.
Evaluate the models: Once you have trained the models, you can evaluate their performance by measuring how well they capture the semantic differences between literal and idiomatic expressions. One way to do this is to use a test set of expressions that includes both literal and idiomatic examples, and measure how well the models can distinguish between them.
Fine-tune the models: Depending on the performance of the models, you may need to fine-tune them by adjusting the parameters or training them on additional data.
To create different word embeddings based on the literal and idiomatic use of an expression, you can follow these steps:
Collect a corpus: Collect a corpus of text that includes the expression you are interested in studying in both literal and idiomatic contexts. The corpus should be large enough to provide sufficient examples for training your models.
Preprocess the corpus: Preprocess the corpus by tokenizing the text, removing stop words, and possibly lemmatizing or stemming the words. This will help to reduce the size of the vocabulary and make the training process more efficient.
Identify the literal and idiomatic instances of the expression: Identify the instances of the expression in the corpus that are being used literally and those that are being used idiomatically. You can do this manually by annotating the instances, or you can use automated methods such as part-of-speech tagging or syntactic parsing.
Train separate models: Train separate word embedding models on the literal and idiomatic instances of the expression. There are several models that you can use, such as Word2Vec, GloVe, or fastText.
Evaluate the models: Evaluate the performance of the models by measuring how well they capture the semantic differences between the literal and idiomatic instances of the expression. You can use a test set of expressions that includes both literal and idiomatic examples and measure how well the models can distinguish between them.
Fine-tune the models: Fine-tune the models by adjusting the hyperparameters or training them on additional data if necessary.
By training separate word embedding models on the literal and idiomatic instances of an expression, you can capture the different meanings and nuances of the expression in different contexts. This approach can be useful for a variety of natural language processing tasks, such as sentiment analysis, text classification, or machine translation.
I apologize for any confusion. Part-of-speech tagging (POS tagging) itself does not differentiate between idiomatic and literal meanings of expressions. However, it can help identify instances of the expression in the corpus and provide information about the context in which they are used. This information can be used to determine whether an expression is being used idiomatically or literally.
For example, consider the expression "kick the bucket." If you POS tag the sentence "I kicked the bucket," you would see that "kicked" is a past tense verb and "bucket" is a noun. This information alone does not indicate whether the expression is being used idiomatically or literally. However, if you look at the surrounding words and the overall context of the sentence, you may be able to infer the meaning of the expression. If the sentence is "I kicked the bucket and spilled the water everywhere," it is likely that the expression is being used literally, as part of a description of a physical action. On the other hand, if the sentence is "I'm afraid my grandfather has kicked the bucket," it is more likely that the expression is being used idiomatically, as a euphemism for "died."
So while POS tagging alone may not be sufficient to distinguish between idiomatic and literal uses of expressions, it can be a useful tool in identifying instances of the expression in the corpus and providing context for further analysis.
Automatic differentiation between idiomatic and literal meanings of expressions is a challenging task in natural language processing, and there is no straightforward or reliable way to do it with high accuracy. However, there are some approaches that have been proposed in the literature that attempt to address this problem.
One approach is to use supervised machine learning algorithms to classify expressions as either idiomatic or literal based on their context in the corpus. This requires a large annotated dataset of expressions with labels indicating whether they are idiomatic or literal. The machine learning algorithm can then learn patterns in the data and use them to predict the classification of new expressions.
Another approach is to use distributional semantics to capture the semantic differences between idiomatic and literal uses of expressions. This involves analyzing the co-occurrence patterns of words in a large corpus to build representations of words that capture their semantic similarity and differences. By comparing the distributional representations of an expression in its idiomatic and literal contexts, it may be possible to identify the differences in meaning.
Finally, some studies have explored the use of psycholinguistic features to distinguish between idiomatic and literal meanings of expressions. These features include measures of concreteness, imageability, and familiarity, which are thought to be associated with literal meanings, as well as measures of ambiguity and familiarity, which are thought to be associated with idiomatic meanings.
Overall, while there is no perfect solution for automatically differentiating between idiomatic and literal meanings of expressions, these approaches offer some promising avenues for future research.
BERT and GPT are deep learning models that have been trained on large corpora to perform a wide range of natural language processing tasks. While these models are very powerful, they are not specifically designed to differentiate between idiomatic and literal meanings of expressions.
However, it is possible to fine-tune BERT or GPT for a specific classification task, such as identifying whether an expression is being used idiomatically or literally. This would require a large annotated dataset of expressions with labels indicating whether they are idiomatic or literal, as well as a training procedure to fine-tune the model for the task.
To fine-tune BERT or GPT for this task, you would typically follow a process similar to the one I described earlier for using supervised machine learning algorithms. This involves splitting the dataset into training, validation, and test sets, fine-tuning the model on the training set, evaluating its performance on the validation set, and then testing it on the test set to get a final measure of performance.
It's worth noting that this approach would require a significant amount of annotated data and computational resources to fine-tune the model, and it may not always produce accurate results for all expressions. Additionally, the fine-tuned model may only work well on expressions that are similar to those in the training data and may not generalize well to new expressions or contexts.
Yes, if you have a specific set of expressions that you want to differentiate between idiomatic and literal uses for, it would be better to annotate the sentences by yourself or by working with a team of annotators. This would give you a labeled dataset that you can use to train and evaluate machine learning models such as BERT or GPT.
Annotating a dataset can be a time-consuming and labor-intensive process, but it can also be very valuable because it allows you to ensure that the annotations are accurate and reflect the specific meanings and nuances of the expressions in your context.
When annotating a dataset for idiomatic and literal use, it's important to establish clear guidelines and criteria for making the annotations, so that the annotations are consistent across the annotators. This may involve providing definitions or examples of idiomatic and literal uses of the expressions, or specific instructions on how to identify idiomatic or literal meanings in context.
Once you have your annotated dataset, you can use it to train and evaluate machine learning models, such as BERT or GPT, to differentiate between idiomatic and literal uses of the expressions in your dataset. Fine-tuning these models on your annotated dataset may improve the accuracy of the predictions for idiomatic and literal uses of the expressions in new contexts.
Yes, if you have a specific set of expressions that you want to differentiate between idiomatic and literal uses for, it would be better to annotate the sentences by yourself or by working with a team of annotators. This would give you a labeled dataset that you can use to train and evaluate machine learning models such as BERT or GPT.
Annotating a dataset can be a time-consuming and labor-intensive process, but it can also be very valuable because it allows you to ensure that the annotations are accurate and reflect the specific meanings and nuances of the expressions in your context.
When annotating a dataset for idiomatic and literal use, it's important to establish clear guidelines and criteria for making the annotations, so that the annotations are consistent across the annotators. This may involve providing definitions or examples of idiomatic and literal uses of the expressions, or specific instructions on how to identify idiomatic or literal meanings in context.
Once you have your annotated dataset, you can use it to train and evaluate machine learning models, such as BERT or GPT, to differentiate between idiomatic and literal uses of the expressions in your dataset. Fine-tuning these models on your annotated dataset may improve the accuracy of the predictions for idiomatic and literal uses of the expressions in new contexts.
I'm not aware of any corpus that specifically differentiates between literal and idiomatic use of expressions. Most corpora are annotated with part-of-speech tags, syntactic parses, or semantic labels, but not with idiomaticity labels.
However, there are some existing resources that may be useful for identifying idiomatic expressions in text:
The Idiomatic Expression Corpus (IDEC): This is a corpus of English texts that have been annotated with idiomatic expressions. The corpus includes a wide variety of text genres, including news articles, fiction, academic texts, and blogs.
The FrameNet corpus: This is a corpus of English texts that have been annotated with semantic frames, which are sets of related words and concepts that describe a particular aspect of meaning. Some frames are closely related to idiomatic expressions, such as the "idiomatic_expression" frame, which includes expressions like "kick the bucket" and "spill the beans".
The English Idioms Corpus: This is a corpus of English texts that have been annotated with a large number of idiomatic expressions. The corpus includes examples of idioms used in context, as well as definitions and explanations of their meanings.
Keep in mind that these resources may not provide explicit annotations of idiomatic and literal uses of expressions in context, but they may still be useful for identifying idiomatic expressions in text and understanding their meanings.
I'm not aware of any Italian corpus that specifically differentiates between literal and idiomatic use of expressions. However, here are some Italian resources that may be useful for identifying idiomatic expressions in text:
The MultiWordNet Italian corpus: This is a lexical resource that provides information on Italian words and their semantic relationships. It includes a large number of multiword expressions, which may include idiomatic expressions. The corpus is available for free download from the MultiWordNet website.
The Valico Project: This is a collection of annotated Italian texts, including both literary and non-literary genres, that have been annotated with part-of-speech tags, syntactic parses, and semantic roles. While this corpus does not specifically annotate idiomatic expressions, it may still be useful for identifying multiword expressions that include idiomatic expressions.
The ItTenTen corpus: This is a large web corpus of Italian text, collected from a range of sources on the web. While this corpus does not specifically annotate idiomatic expressions, it may still be useful for identifying patterns of language use that are associated with idiomatic expressions.
It's worth noting that creating a corpus that specifically differentiates between literal and idiomatic uses of expressions can be a challenging task, and may require manual annotation by linguists or native speakers of the language.
Once you have a corpus of text that is annotated with idiomatic and literal expressions, you can use different methods for creating word embeddings and computing their distances. The choice of method will depend on various factors such as the size of the corpus, the specific research question, and the resources available.
Word2Vec is a popular method for creating word embeddings and is often used for tasks such as word similarity and analogy. It works by training a neural network on a large corpus of text to predict the context in which words appear. Word2Vec is relatively easy to implement and can work well for small to medium-sized corpora.
BERT and GPT are pre-trained language models that have achieved state-of-the-art performance on a variety of natural language processing tasks. These models can also be fine-tuned for specific tasks, including creating word embeddings. However, fine-tuning these models can be computationally expensive and may require specialized hardware.
In terms of computing the distances between word embeddings, the Euclidean distance is a common measure of similarity, but other measures such as cosine similarity can also be used. The choice of distance measure will depend on the specific research question and the properties of the embeddings.
Overall, there is no single "best" method for creating word embeddings and computing their distances. The choice of method will depend on various factors, and it may be helpful to experiment with different methods to see which works best for your particular task.
Word2Vec is a relatively older technique for creating word embeddings compared to BERT or GPT, which are more recent and advanced methods for natural language processing.
Word2Vec was first introduced in 2013 and quickly became a popular method for creating word embeddings, thanks to its simplicity and effectiveness. It works by training a neural network on a large corpus of text to predict the context in which words appear. This results in a set of high-dimensional vectors that represent words in a way that captures their semantic relationships.
BERT and GPT, on the other hand, are more recent and advanced techniques for creating word embeddings. BERT was introduced in 2018 and is a pre-trained language model that has achieved state-of-the-art performance on a variety of natural language processing tasks. It works by training a transformer-based neural network on a large corpus of text to predict missing words or predict the next word in a sentence. GPT, which stands for "Generative Pre-trained Transformer," is a similar pre-trained language model that is based on the transformer architecture and was introduced in 2018.
Overall, while Word2Vec is still a widely used and effective method for creating word embeddings, BERT and GPT are more advanced and have achieved state-of-the-art performance on a wide range of natural language processing tasks.
One Italian corpus that you might find useful for your research is the ItWac corpus, which is a large web corpus of Italian texts that has been annotated with part-of-speech tags and lemmas. While this corpus does not have explicit annotations for idiomatic vs. literal expressions, it could still be a useful resource for identifying such expressions and creating your own annotations.
Another corpus that you might consider is the EVALITA 2018 Idiom Identification Shared Task Corpus, which is a corpus of Italian sentences that have been manually annotated with information about whether or not they contain idiomatic expressions. This corpus could be a valuable resource for developing and testing methods for automatically identifying idiomatic expressions in Italian text.
Additionally, the PAROLE corpus, a large-scale Italian corpus developed by the Istituto di Linguistica Computazionale "Antonio Zampolli" (ILC-CNR), may also contain examples of idiomatic and literal expressions. However, it may not have explicit annotations for these expressions, so you may need to manually annotate the data yourself.
For English, there are several corpora that you may find useful for identifying idiomatic expressions and distinguishing between their literal and figurative meanings:
The English portion of the EVALITA 2018 Idiom Identification Shared Task Corpus, which is a manually annotated corpus of sentences containing idiomatic expressions.
The English portion of the Multiword Expressions in Theory and Practice (METP) Corpus, which contains manually annotated examples of multiword expressions, including idioms.
The English Idiom Corpus, which is a corpus of idiomatic expressions in English that has been manually annotated with information about their structure, meaning, and frequency of use.
The British National Corpus and the Corpus of Contemporary American English both contain large amounts of English text and may include examples of idiomatic expressions. While these corpora do not have explicit annotations for idiomatic vs. literal use of expressions, they could still be useful resources for identifying and analyzing such expressions.
The SemEval-2007 Task 7 Corpus, which is a corpus of English sentences containing various types of figurative language, including idiomatic expressions. This corpus is part of the SemEval series of shared tasks in natural language processing, which have produced a number of useful resources for language analysis.
Note that some of these corpora may require special permissions or access, and some may require payment or registration. Additionally, depending on your research question and methodology, you may need to create your own annotations or develop your own criteria for distinguishing between literal and figurative uses of expressions.
You can create word embeddings with GPT in Python using the transformers library from Hugging Face. Here are the steps you can follow:
- Install the transformers library:
pip install transformers
- Load the GPT model using the
GPT2Modelclass:
pythonfrom transformers import GPT2Model
model = GPT2Model.from_pretrained('gpt2')
This loads the pre-trained GPT-2 model.
- Tokenize your text using the
GPT2Tokenizerclass:
pythonfrom transformers import GPT2Tokenizer
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
text = "This is an example sentence."
tokens = tokenizer.tokenize(text)
- Convert the tokens to input IDs and create an input tensor:
pythoninput_ids = tokenizer.convert_tokens_to_ids(tokens) input_tensor = torch.tensor([input_ids])
Note that this requires the PyTorch library.
- Generate the word embeddings using the GPT model:
pythonoutputs = model(input_tensor) embeddings = outputs.last_hidden_state
This generates the word embeddings for the input text.
- You can then use these embeddings for various tasks, such as computing similarity between words or sentences.
Note that the above code is a basic example and you may need to modify it based on your specific use case. Additionally, training your own GPT model may require a significant amount of computational resources and data.
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