framenet_tools.utils package

Submodules

framenet_tools.utils.postagger module

class framenet_tools.utils.postagger.PosTagger(use_spacy: bool)

Bases: object

PosTagger provides options for assigning POS-tags to sentences.

Either by spacy or nltk.

get_tags(sentence: List[str])

Returns the POS-tags of a given sentence.

Parameters:sentence – The sentence, given as a list of words
Returns:A list of POS-tags
get_tags_nltk(tokens: List[str])

Gets lemma, pos and NE for each token

Parameters:tokens – A list of tokens from a sentence
Returns:A 2d-Array containing lemma, pos and NE for each token
get_tags_spacy(tokens: List[str])

The spacy version of the get_tags method

:param tokens:The sentence, given as a list of words :return: A list of POS-tags

framenet_tools.utils.postagger.get_pos_constants(tag: str)

Static function for tag conversion

Parameters:tag – The given pos tag
Returns:The corresponding letter

framenet_tools.utils.static_utils module

framenet_tools.utils.static_utils.download(url: str)

Downloads and extracts a file given as a url.

NOTE: The paths should NOT be changed in order for pyfn to work NOTE: Only extracts 7z files

Parameters:url – The url from where to get the file
Returns:
framenet_tools.utils.static_utils.download_file(url: str, file_path: str)

Downloads a file and saves at a given path

Parameters:
  • url – The URL of the file to download
  • file_path – The destination of the file
Returns:
framenet_tools.utils.static_utils.download_frame_embeddings()

Checks if the needed frame embeddings are already downloaded, if not they are downloaded.

Returns:
framenet_tools.utils.static_utils.download_resources()

Checks if the required resources from nltk are installed, if not they are downloaded.

Returns:
framenet_tools.utils.static_utils.extract7z(path: str)

Extracts 7z Archive

Parameters:path – The path of the archive
Returns:
framenet_tools.utils.static_utils.extract_file(file_path: str)

Extracts a zipped file

Parameters:file_path – The file to extract
Returns:
framenet_tools.utils.static_utils.get_sentences(raw: str, use_spacy: bool = False)

Parses a raw string of text into structured sentences. This is either done via nltk or spacy; default being nltk.

Parameters:
  • raw – A raw string of text
  • use_spacy – True to use spacy, otherwise nltk
Returns:

A list of sentences, consisting of tokens
framenet_tools.utils.static_utils.get_sentences_nltk(raw: str)

The nltk version of the get_sentences method.

Parameters:raw – A raw string of text
Returns:A list of sentences, consisting of tokens
framenet_tools.utils.static_utils.get_sentences_spacy(raw: str)

The spacy version of the get_sentences method.

Parameters:raw – A raw string of text
Returns:A list of sentences, consisting of tokens
framenet_tools.utils.static_utils.get_spacy_en_model()

Installs the required en_core_web_sm model

NOTE: Solution for Windows? TODO :return:

framenet_tools.utils.static_utils.load_pkl_from_path(str_path_file: str)

Taken from: https://public.ukp.informatik.tu-darmstadt.de/repl4nlp17-frameEmbeddings/reader.py

Parameters:str_path_file – The path of the pickle file to load the dict from
Returns:The loaded dict
framenet_tools.utils.static_utils.pos_to_int(pos: str)

Converts a pos tag to an integer according to the static dictionary.

Parameters:pos – The pos tag
Returns:The index of the pos tag
framenet_tools.utils.static_utils.print_dict_to_txt(str_path_file: str, dict_to_print: dict)

Taken from: https://public.ukp.informatik.tu-darmstadt.de/repl4nlp17-frameEmbeddings/reader.py

Parameters:
  • str_path_file – The path of the dict to save to
  • dict_to_print – The dict to save
Returns:
framenet_tools.utils.static_utils.shuffle_concurrent_lists(l: List[List[object]])

Shuffles multiple concurrent lists so that pairs of (x, y) from different lists are still at the same index.

Parameters:l – A list of concurrent lists
Returns:The list of shuffled concurrent lists

Module contents