input_data.md

Input samples format

Input data might be in a form of .jsoln [example] or csv [example].

Input represents list/rows of samples. Every sample contains text and mentioned opinion in it, i.e. source->target relation.

Sample contain the following mandatory parameters:

• id (type:uint) -- sample identifier
• label (type: int) -- for training only, denotes a class;
  • value in range [0, c], where c corresponds to classes amount.
• text (type: str or list) -- string of terms, separated by (whitespace), or list of terms in case of jsonl fomat;

Optional parameters:

• s_ind (type: int) -- index of the source term in text string/list;
• t_ind (type: int) -- index of the target term in text string/list;
• opinion_id (type: uint) -- used for grouping opinions, denoting index in group;
• entities (type: str) -- comma separated term indices which corresponds to entities, in order of their appearance in text
• frames (type: str) -- comma separated term indices which corresponds to connotation frames, in order of their appearance in text; important for sentiment-classification related tasks;
• frame_connots_uint (type: str) -- comma separated scores of the in set of three scale int values {-1, 0, 1};
• syn_subjs (type: str) -- comma separated indices, synonymous to source s_ind;
• syn_objs (type: str) -- comma separated indices, synonymous to target t_ind;
• pos_tags (type: str) -- comma separated part-of-speech tags, with length exact the same as terms count of text;

Embedding details

We support model.txt format, which provides:

• first row -- shape of the embedding matrix
• word and its vector in every row

Embeddings could be obtained from NLPL repository

• See how we do this in the following downloading script

Text-based embeddings will be then converted into vocab.txt and embedding.npz matrix. [see code implementation]