MaxGSEO

from collections import Counter
import pandas as pd

# Define scoring variables
core_set_score = 5
second_tier_score = 3
third_tier_score = 1
grace_zone_boost = 1
entity_boost = 1
double_entity_boost = 2

MaxGSEO

from sklearn.feature_extraction.text import TfidfVectorizer
from nltk.corpus import stopwords
from collections import Counter
import pandas as pd
import os

# Read text from files
texts = {}
for filename in os.listdir('./texts/'):  # Assuming the files are in a folder called 'texts'
    if filename.endswith('.txt'):

MaxGSEO

// Constants
const API_KEY = "YOUR API KEY";
const MODEL_TYPE = "gpt-3.5-turbo"; //chatGPT model
const MAX_TOKENS = 4096;

function CHATGPT(promptCellRef, contentRangeRef) {
  const sheet = SpreadsheetApp.getActiveSpreadsheet().getActiveSheet();
  
  // Get the prompt from the specified cell
  const promptCell = sheet.getRange(promptCellRef);

MaxGSEO

<script type="application/ld+json">
[
    {
        "about": [
            {
                "@context": "http://schema.org",
                "@type": "Thing",
                "name": "Semantic publishing",
                "description": "Semantic publishing on the Web, or semantic web publishing, refers to publishing information on the web as documents accompanied by semantic markup. Semantic publication provides a way for computers to understand the structure and even the meaning of the published information, making information search and data integration more efficient.",
                "sameAs": [

MaxGSEO

<script type="application/ld+json">
[
    {
        "mentions": [
            {
                "@context": "http://schema.org",
                "@type": "Thing",
                "name": "Named-entity recognition",
                "description": "Named-entity recognition (NER) (also known as (named) entity identification entity chunking and entity extraction) is a subtask of information extraction that seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories such as person names organizations locations medical codes time expressions quantities monetary values percentages etc. Most research on NER/NEE systems has been structured as taking an unannotated block of text such as this one: Jim bought 300 shares of Acme Corp",
                "SameAs": [