QuestionAnsweringSystemviaSemanticRoleLabeling.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "### Imports"
      ],
      "metadata": {
        "id": "7C0jtkan_tmz"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4BzgGVDcDK-V",
        "outputId": "436ee41a-fb23-467b-f3a2-1e46695e2b2f",
        "collapsed": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Requirement already satisfied: click<9.0.0,>=7.1.1 in /usr/local/lib/python3.10/dist-packages (from typer<0.10.0,>=0.3.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (8.1.7)\n",
            "Requirement already satisfied: cloudpathlib<0.17.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from weasel<0.4.0,>=0.1.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.16.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.1.5)\n",
            "Requirement already satisfied: marisa-trie>=0.7.7 in /usr/local/lib/python3.10/dist-packages (from language-data>=1.2->langcodes<4.0.0,>=3.2.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.1.1)\n",
            "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
            "You can now load the package via spacy.load('en_core_web_sm')\n",
            "\u001b[38;5;3m⚠ Restart to reload dependencies\u001b[0m\n",
            "If you are in a Jupyter or Colab notebook, you may need to restart Python in\n",
            "order to load all the package's dependencies. You can do this by selecting the\n",
            "'Restart kernel' or 'Restart runtime' option.\n"
          ]
        }
      ],
      "source": [
        "!pip install spacy sentence-transformers\n",
        "!python -m spacy download en_core_web_sm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YwVCvICyDaGm",
        "outputId": "425659a7-6627-4b3d-b9e9-ed53ffda2e2c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]     /root/nltk_data...\n",
            "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
            "[nltk_data]       date!\n",
            "[nltk_data] Downloading package treebank to /root/nltk_data...\n",
            "[nltk_data]   Package treebank is already up-to-date!\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "import nltk\n",
        "import string\n",
        "from nltk import pos_tag\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.corpus import treebank\n",
        "from nltk.tree import Tree\n",
        "from nltk.corpus import stopwords\n",
        "\n",
        "\n",
        "# Download required resources\n",
        "nltk.download('punkt')\n",
        "nltk.download('averaged_perceptron_tagger')\n",
        "nltk.download('treebank')\n",
        "nltk.download('stopwords')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import string\n",
        "import spacy\n",
        "from transformers import AutoTokenizer, AutoModel, AutoModelForTokenClassification, pipeline\n",
        "from sentence_transformers import SentenceTransformer, util\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "SeiTQdP9AVrc"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Utility functions"
      ],
      "metadata": {
        "id": "SNXu9w9a_0vb"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "cZC9KGv1DTVl"
      },
      "outputs": [],
      "source": [
        "def remove_punctuation(text):\n",
        "    \"\"\"\n",
        "    Removal of punction marks if any.\n",
        "\n",
        "    Args:\n",
        "    - text (str): String of a statement.\n",
        "\n",
        "    Returns:\n",
        "    - _ (str): String without punctuation marks.\n",
        "    \"\"\"\n",
        "\n",
        "    return text.translate(str.maketrans('', '', string.punctuation))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# load bert NER model and create pipeline for NER\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"dslim/bert-large-NER\")\n",
        "model = AutoModelForTokenClassification.from_pretrained(\"dslim/bert-large-NER\")\n",
        "\n",
        "# creation of pipeline\n",
        "ner = pipeline(\"ner\", model=model,  tokenizer=tokenizer,  aggregation_strategy=\"first\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388,
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        "id": "1NOghX87Ajg4",
        "outputId": "e8f3e7de-5a4e-4594-a9b7-3aa2d4c756e6"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/40.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7c79e5fb55234eab94b599e2ba44fa08"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/1.45k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "53c03cc9b30146929ff7c3faf18b2c41"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/213k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8720740265194306af89037452843b85"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "75edf31b6cb440fbbd74ddd1b00c0a67"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/1.33G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "518eb2cd8ce74b01b69dce3e6c8bf5a1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at dslim/bert-large-NER were not used when initializing BertForTokenClassification: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight']\n",
            "- This IS expected if you are initializing BertForTokenClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def bert_ner(text):\n",
        "    \"\"\"\n",
        "    Retrieval of Named Entity Recognition(NER) Tags from BERT pretrained model.\n",
        "\n",
        "    Args:\n",
        "    - text (str): String of a statement.\n",
        "\n",
        "    Returns:\n",
        "    - ent (dict): Dictionary of a word mapped with it's NER tag if any.\n",
        "    \"\"\"\n",
        "\n",
        "    # initialization\n",
        "    ent = {}\n",
        "    # get result from the pipeline\n",
        "    ner_results = ner(text)\n",
        "    # append the required result to dictionary\n",
        "    for e in ner_results:\n",
        "        ent[e['word']] = e['entity_group']\n",
        "    return ent"
      ],
      "metadata": {
        "id": "QJOxvER5A02d"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def aggregate_phrases(text):\n",
        "    \"\"\"\n",
        "    Aggregation of related words and convert them into phrases for easy retrieval of answers.\n",
        "\n",
        "    Args:\n",
        "    - text (str): String of a statement.\n",
        "\n",
        "    Returns:\n",
        "    - noun_phrases (list): List of chunk of words that represents a noun.\n",
        "    - verb_phrases (list): List of chunk of words that represents a verb.\n",
        "    \"\"\"\n",
        "\n",
        "    doc = nlp(text)\n",
        "    noun_phrases = [chunk.text for chunk in doc.noun_chunks]\n",
        "    verb_phrases = [token.lemma_ for token in doc if token.pos_ == \"VERB\"]\n",
        "    return noun_phrases, verb_phrases"
      ],
      "metadata": {
        "id": "YNlezTOgA31t"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def perform_pos_tagging(text):\n",
        "    \"\"\"\n",
        "    Retrieval of Parts Of Speech (POS) Tags from spacy pretrained model.\n",
        "\n",
        "    Args:\n",
        "    - text (str): String of a statement.\n",
        "\n",
        "    Returns:\n",
        "    - pos_tags (dict): Dictionary of a word mapped with it's POS tag.\n",
        "    \"\"\"\n",
        "\n",
        "    doc = nlp(text)\n",
        "    pos_tags = {token.text: token.pos_ for token in doc}\n",
        "    return pos_tags"
      ],
      "metadata": {
        "id": "-1uEnIAvA6Hs"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def perform_dependency_parsing(text):\n",
        "    \"\"\"\n",
        "    Retrieval of dependency parse labels from spacy pretrained model.\n",
        "\n",
        "    Args:\n",
        "    - text (str): String of a statement.\n",
        "\n",
        "    Returns:\n",
        "    - dependencies (dict): Dictionary of a word mapped with it's dependency label.\n",
        "    - heads (dict): Dictionary of a word mapped with it's head word in dependency parsing.\n",
        "    \"\"\"\n",
        "\n",
        "    doc = nlp(text)\n",
        "    # storing dependencies and it's heads separately\n",
        "    dependencies = {token.text: token.dep_ for token in doc}\n",
        "    heads = {token.text: token.head.text for token in doc}\n",
        "\n",
        "    # optional\n",
        "    # Visualize the dependency tree\n",
        "    # displacy.render(doc, style='dep', jupyter=True)\n",
        "\n",
        "    return dependencies, heads"
      ],
      "metadata": {
        "id": "TLaIn4lnA9Ep"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load SpaCy model for dependency parsing and NER\n",
        "nlp = spacy.load(\"en_core_web_sm\")\n",
        "\n",
        "# Load pre-trained Sentence Transformer model for embeddings\n",
        "sentence_model = SentenceTransformer('all-MiniLM-L6-v2')"
      ],
      "metadata": {
        "id": "g-nt9iGlINao",
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    {
      "cell_type": "code",
      "source": [
        "def get_embedding(text):\n",
        "    \"\"\"\n",
        "    Implementation of text embedding using sentence transformer pretrained model.\n",
        "\n",
        "    Args:\n",
        "    - text (str): String of question/statement.\n",
        "\n",
        "    Returns:\n",
        "    - encoded_text (list): List of floating point values obtained after encoding the text.\n",
        "    \"\"\"\n",
        "\n",
        "    encoded_text = sentence_model.encode(text, convert_to_tensor=True)\n",
        "    return encoded_text"
      ],
      "metadata": {
        "id": "bmCpaA2TIZYn"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def cosine_similarity(embedding1, embedding2):\n",
        "    \"\"\"\n",
        "    Implementation of cosine similarity.\n",
        "\n",
        "    Args:\n",
        "    - embedding1 (list): Array of embedding of question.\n",
        "    - embedding2 (list): Array of embedding of sentences.\n",
        "\n",
        "    Returns:\n",
        "    - similarity (float): Value of cosine similarity between embedding of a particular statement and a question.\n",
        "    \"\"\"\n",
        "\n",
        "    similarity = util.pytorch_cos_sim(embedding1, embedding2).item()\n",
        "    return similarity"
      ],
      "metadata": {
        "id": "LsTK_t-lIbfE"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Prime function for semantic role labeling"
      ],
      "metadata": {
        "id": "7p3FwFeRAApI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def semantic_roles(s):\n",
        "    \"\"\"\n",
        "    Implementation of Semantic Role Labeling system.\n",
        "\n",
        "    Args:\n",
        "    - s (str): String of sentence from which semantic roles must be extracted.\n",
        "\n",
        "    Returns:\n",
        "    - roles (dict): Dictionary of semantic roles mapped to the relevant phrase.\n",
        "    \"\"\"\n",
        "\n",
        "    # initialization\n",
        "    roles = {}\n",
        "\n",
        "    # preprocessing - only removing punctuation as all other features are necessary to understand the sentence\n",
        "    s = remove_punctuation(s)\n",
        "\n",
        "    # NER using bert\n",
        "    entities = bert_ner(s)\n",
        "    # Dependency parsing using spacy\n",
        "    dependencies, heads = perform_dependency_parsing(s)\n",
        "    # Word aggregation: Words -> Phrases\n",
        "    aggregates, _ = aggregate_phrases(s)\n",
        "    # POS tagging\n",
        "    pos_tagging = perform_pos_tagging(s)\n",
        "\n",
        "    # tokenization\n",
        "    doc = nlp(s)\n",
        "    # finding phrases for nouns if exists\n",
        "    noun_phrases = [chunk.text for chunk in doc.noun_chunks]\n",
        "    # finding phrases for words if exists\n",
        "    word_to_phrase = {word: phrase for phrase in noun_phrases for word in phrase.split()}\n",
        "\n",
        "    # iteration through aggregates of words i.e, phrases\n",
        "    for agg in aggregates:\n",
        "        words = agg.split()\n",
        "        entity_label = None\n",
        "        for word in words:\n",
        "            if word in entities:\n",
        "                entity_label = entities[word]\n",
        "                break\n",
        "        # extract semantic roles based on Named Entities if present\n",
        "        if entity_label:\n",
        "            for word in words:\n",
        "                dep = dependencies.get(word)\n",
        "                pos = pos_tagging.get(word)\n",
        "                head = heads.get(word)\n",
        "                phrase = word_to_phrase.get(word, word)\n",
        "\n",
        "                if entity_label == 'PER':\n",
        "                    if pos in ['NOUN', 'PRON', 'PROPN']:\n",
        "                        if dep == 'nsubj':\n",
        "                            roles['Agent'] = phrase\n",
        "                        elif dep in ['dobj', 'pobj']:\n",
        "                            roles['Patient'] = phrase\n",
        "                elif entity_label in ['LOC']:\n",
        "                    if pos in ['NOUN', 'PRON', 'PROPN']:\n",
        "                        if dep in ['dobj', 'pobj']:\n",
        "                            roles['Destination'] = phrase\n",
        "                        elif dep == 'nsubj':\n",
        "                            roles['Source'] = phrase\n",
        "                elif entity_label in ['MISC']:\n",
        "                    if pos in ['NOUN', 'PRON', 'PROPN']:\n",
        "                        if dep in ['compound', 'pobj']:\n",
        "                            roles['Instrument'] = phrase\n",
        "        else:\n",
        "            # extract semantic roles based on POS tags and Dependency tags\n",
        "            for word in words:\n",
        "                dep = dependencies.get(word)\n",
        "                pos = pos_tagging.get(word)\n",
        "                head = heads.get(word)\n",
        "                phrase = word_to_phrase.get(word, word)\n",
        "\n",
        "                if pos in ['NOUN', 'PRON', 'PROPN']:\n",
        "                    if dep in ['nsubj','compound','nsubjpass']:\n",
        "                        if 'Agent' not in roles:\n",
        "                            roles['Agent'] = phrase\n",
        "                    elif dep == 'dobj':\n",
        "                        roles['Patient'] = phrase\n",
        "                    elif dep == 'pobj':\n",
        "                        if head == 'with':\n",
        "                            roles['Instrument'] = phrase\n",
        "                        elif head == 'for':\n",
        "                            roles['Beneficiary'] = phrase\n",
        "                        elif head == 'from':\n",
        "                            roles['Source'] = phrase\n",
        "                        elif head == 'to' :\n",
        "                            roles['Destination'] = phrase\n",
        "                        elif head == 'by':\n",
        "                            roles['Patient'] = phrase\n",
        "                elif pos in ['VERB']:\n",
        "                    roles['Predicate'] = phrase\n",
        "\n",
        "    # Handle prepositional phrases separately\n",
        "    for token in doc:\n",
        "        phrase = word_to_phrase.get(token.text, token.text)\n",
        "\n",
        "        # Extract semantic roles based on POS tags\n",
        "        if token.pos_ == 'NOUN':\n",
        "            if token.dep_ == 'nsubj' and token.head.dep_ == 'ROOT':\n",
        "                if 'Agent' not in roles:\n",
        "                    roles['Agent'] = phrase\n",
        "            elif token.dep_ == 'dobj':\n",
        "                roles['Patient'] = phrase\n",
        "\n",
        "        # Extract semantic roles based on dependency labels\n",
        "        if token.dep_ == 'ROOT':\n",
        "            roles['Predicate'] = token.lemma_\n",
        "        elif token.dep_ == 'prep' and token.text in ['to','in']:\n",
        "            for child in token.children:\n",
        "                if child.dep_ == 'pobj':\n",
        "                    roles['Destination'] = word_to_phrase.get(child.text, child.text)\n",
        "        elif token.dep_ == 'prep' and token.text == 'with':\n",
        "            for child in token.children:\n",
        "                if child.dep_ == 'pobj':\n",
        "                    roles['Instrument'] = word_to_phrase.get(child.text, child.text)\n",
        "        elif token.dep_ == 'prep' and token.text == 'for':\n",
        "            for child in token.children:\n",
        "                if child.dep_ == 'pobj':\n",
        "                    roles['Beneficiary'] = word_to_phrase.get(child.text, child.text)\n",
        "        elif token.dep_ == 'prep' and token.text in ['on', 'in']:\n",
        "            for child in token.children:\n",
        "                if child.dep_ in ['pobj', 'dobj']:\n",
        "                    roles['Patient'] = word_to_phrase.get(child.text, child.text)\n",
        "        elif token.dep_ == 'prep' and token.text == 'from':\n",
        "            for child in token.children:\n",
        "                if child.dep_ == 'pobj':\n",
        "                    roles['Source'] = word_to_phrase.get(child.text, child.text)\n",
        "\n",
        "    return roles"
      ],
      "metadata": {
        "id": "W4JqxZ4l_5NY"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Application of semantic role labeling: Question Answering system"
      ],
      "metadata": {
        "id": "EHejccfoA1B7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def answer_question(question, text):\n",
        "    \"\"\"\n",
        "    Implementation of QA system.\n",
        "\n",
        "    Args:\n",
        "    - question (str): String of question.\n",
        "    - text (str): String of paragraph on which question is asked.\n",
        "\n",
        "    Returns:\n",
        "    - answer (str): String of relevant phrase from the paragraph for the given question.\n",
        "    \"\"\"\n",
        "\n",
        "    # Tokenization and dependency parsing for both the question and the text passage\n",
        "    question_doc = nlp(question)\n",
        "    text_doc = nlp(text)\n",
        "\n",
        "    # get embeddings for the text and question\n",
        "    question_embedding = get_embedding(question)\n",
        "    text_sentences = [sent.text for sent in text_doc.sents]\n",
        "    text_embeddings = get_embedding(text_sentences)\n",
        "\n",
        "    # Finding the most relevant sentence in the text based on cosine similarity\n",
        "    similarities = util.pytorch_cos_sim(question_embedding, text_embeddings).squeeze()\n",
        "    most_relevant_idx = np.argmax(similarities)\n",
        "    most_relevant_sentence = text_sentences[most_relevant_idx]\n",
        "\n",
        "    # Extracting semantic roles from the most relevant sentence\n",
        "    text_roles = semantic_roles(most_relevant_sentence)\n",
        "    print(\"Semantic role labeling of relevant sentence: \",text_roles)\n",
        "\n",
        "    # Initialize the answer\n",
        "    answer = \"\"\n",
        "\n",
        "    # Analyze the question to determine the type of information being asked for\n",
        "    if any(word in question.lower() for word in ['who', 'whose']):\n",
        "        if 'Agent' in text_roles:\n",
        "            answer = text_roles['Agent']\n",
        "    elif any(word in question.lower() for word in ['how', 'with what']):\n",
        "        if 'Instrument' in text_roles:\n",
        "            answer = text_roles['Instrument']\n",
        "    elif any(word in question.lower() for word in ['what', 'which']):\n",
        "        # in case of ambiguity, predict multiple answers\n",
        "        if 'Patient' in text_roles:\n",
        "            answer = text_roles['Patient']\n",
        "            answer += \", \"\n",
        "        if 'Predicate' in text_roles:\n",
        "            answer += text_roles['Predicate']\n",
        "    elif any(word in question.lower() for word in ['whom']):\n",
        "        if 'Patient' in text_roles:\n",
        "            answer = text_roles['Patient']\n",
        "    elif any(word in question.lower() for word in ['where']):\n",
        "        if 'Destination' in text_roles:\n",
        "            answer = text_roles['Destination']\n",
        "            answer += \", \"\n",
        "        if 'Patient' in text_roles:\n",
        "            answer += text_roles['Patient']\n",
        "    elif any(word in question.lower() for word in ['how']):\n",
        "        if 'Predicate' in text_roles:\n",
        "            answer = text_roles['Predicate']\n",
        "    elif any(word in question.lower() for word in ['when']):\n",
        "        if 'Beneficiary' in text_roles:\n",
        "            answer = text_roles['Beneficiary']\n",
        "            answer += \", \"\n",
        "        if 'Patient' in text_roles:\n",
        "            answer += text_roles['Patient']\n",
        "\n",
        "    return answer"
      ],
      "metadata": {
        "id": "hPYzZ1Tz-U33"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Example usages"
      ],
      "metadata": {
        "id": "8L-c17UpZ1Oh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Example usage\n",
        "paragraph = \"\"\"John was the one behind the mass protest held recently in Mumbai. John sold a car to Mary in the Bangalore. He is very good at hypnotizing people.\"\"\"\n",
        "question = \"Where did John sell the car?\"\n",
        "print(\"Answer: \", answer_question(question, paragraph))"
      ],
      "metadata": {
        "id": "yQ-U_wKw-0qm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cdccd130-f3dd-430d-c75b-076e24834ed5"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Semantic role labeling of relevant sentence:  {'Agent': 'John', 'Patient': 'a car', 'Destination': 'the Bangalore', 'Predicate': 'sell'}\n",
            "Answer:  the Bangalore, a car\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Example usage\n",
        "paragraph = \"\"\"Alice visited the museum on Tuesday afternoon. The stock market experienced a significant drop last week. Bob enjoys playing the guitar in his free time. The weather forecast predicts rain for the weekend. Sarah bought a new dress for the upcoming party. The new cafe downtown serves the best coffee in the city. Mike is planning a hiking trip next month. The movie was released to critical acclaim. The cat slept peacefully on the windowsill. Jenny is taking a course on digital marketing.\"\"\"\n",
        "question = \"What did Alice do?\"\n",
        "print(\"Answer: \", answer_question(question, paragraph))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sPvZI8qoI9dV",
        "outputId": "22523afc-0eea-4b1a-c775-e44a227e3986"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Semantic role labeling of relevant sentence:  {'Agent': 'Alice', 'Patient': 'Tuesday afternoon', 'Predicate': 'visit'}\n",
            "Answer:  Tuesday afternoon, visit\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Example usage\n",
        "paragraph = \"\"\"Alice visited the museum on Tuesday afternoon. The stock market experienced a significant drop last week. Bob enjoys playing the guitar during free time. The weather forecast predicts rain for the weekend. Sarah bought a new dress for the upcoming party. The new cafe downtown serves the best coffee in the city. Mike is planning a hiking trip next month. The movie was released to critical acclaim. The cat slept peacefully on the windowsill. Jenny is taking a course on digital marketing.\"\"\"\n",
        "question = \"Who enjoys playing guitar?\"\n",
        "print(\"Answer: \", answer_question(question, paragraph))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e19667fe-1217-4e0a-be5b-e4e3dd3492fb",
        "id": "IcUFrAyoUggF"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Semantic role labeling of relevant sentence:  {'Agent': 'Bob', 'Patient': 'the guitar', 'Predicate': 'enjoy'}\n",
            "Answer:  Bob\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Example usage\n",
        "paragraph = \"\"\"Alice visited the museum on Tuesday afternoon. The stock market experienced a significant drop last week. Bob enjoys playing the guitar in his free time. The weather forecast predicts rain for the weekend. Sarah bought a new dress for the upcoming party. The new cafe downtown serves the best coffee in the city. Mike is planning a hiking trip next month. The movie was released to critical acclaim. The cat slept peacefully on the windowsill. Jenny is taking a course on digital marketing.\"\"\"\n",
        "question = \"What did Sarah buy for the upcoming party?\"\n",
        "print(\"Answer: \", answer_question(question, paragraph))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lSqT4hSFUkGc",
        "outputId": "e66582d9-bf5a-4166-ad53-d9029ce58efa"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Semantic role labeling of relevant sentence:  {'Agent': 'Sarah', 'Patient': 'a new dress', 'Beneficiary': 'the upcoming party', 'Predicate': 'buy'}\n",
            "Answer:  a new dress, buy\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Example usage\n",
        "paragraph = \"\"\"Alice visited the museum on Tuesday afternoon. The stock market experienced a significant drop last week. Bob enjoys playing the guitar in his free time. The weather forecast predicts rain for the weekend. Sarah bought a new dress for the upcoming party. The new cafe downtown serves the best coffee in the city. Mike is planning a hiking trip next month. The movie was released to critical acclaim. The cat slept peacefully on the windowsill. Jenny is taking a course on digital marketing with her laptop.\"\"\"\n",
        "question = \"Who serves the best coffee in the city?\"\n",
        "print(\"Answer: \", answer_question(question, paragraph))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "23cd21ae-68f8-4e51-e448-9980490726d8",
        "id": "CdDbJUpfVwB1"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Semantic role labeling of relevant sentence:  {'Agent': 'The new cafe downtown', 'Patient': 'the best coffee', 'Predicate': 'serve', 'Destination': 'the city'}\n",
            "Answer:  The new cafe downtown\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Example usage\n",
        "paragraph = \"\"\"Alice visited the museum on Tuesday afternoon. The stock market experienced a significant drop last week. Bob enjoys playing the guitar in his free time. The weather forecast predicts rain for the weekend. Sarah bought a new dress for the upcoming party. The new cafe downtown serves the best coffee in the city. Mike is planning a hiking trip next month. The movie was released to critical acclaim. The cat slept peacefully on the windowsill. Jenny is taking a course on digital marketing with her laptop.\"\"\"\n",
        "question = \"How is Jenny taking her course?\"\n",
        "print(\"Answer: \", answer_question(question, paragraph))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a5656c8a-71c8-4b9e-d60c-d7fb833ebe93",
        "id": "AvUy0a-kWVB0"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Semantic role labeling of relevant sentence:  {'Agent': 'Jenny', 'Patient': 'digital marketing', 'Instrument': 'her laptop', 'Predicate': 'take'}\n",
            "Answer:  her laptop\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Example usage\n",
        "paragraph = \"\"\"Alice visited the museum on Tuesday afternoon. The stock market experienced a significant drop last week. Bob enjoys playing the guitar in his free time. The weather forecast predicts rain for the weekend. Sarah bought a new dress for the upcoming party. The new cafe downtown serves the best coffee in the city. Mike is planning a hiking trip next month. The movie was released to critical acclaim. The cat slept peacefully on the windowsill. Jenny is taking a course on digital marketing with her laptop.\"\"\"\n",
        "question = \"Who serves the best coffee in the city?\"\n",
        "print(\"Answer: \", answer_question(question, paragraph))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b963a09d-e277-4b5f-a16d-fe1cbd5270f0",
        "id": "zOtNrS5BWi-6"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Semantic role labeling of relevant sentence:  {'Agent': 'The new cafe downtown', 'Patient': 'the best coffee', 'Predicate': 'serve', 'Destination': 'the city'}\n",
            "Answer:  The new cafe downtown\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Example usage\n",
        "paragraph = \"\"\"Alice visited the museum on Tuesday afternoon. The stock market experienced a significant drop last week. Bob enjoys playing the guitar in his free time. The weather forecast predicts rain for the weekend. Sarah bought a new dress for the upcoming party. The new cafe downtown serves the best coffee in the city. Mike is planning a hiking trip next month. The movie was released to critical acclaim. The cat slept peacefully on the windowsill. Jenny is taking a course on digital marketing with her laptop.\"\"\"\n",
        "question = \"When is rain predicted?\"\n",
        "print(\"Answer: \", answer_question(question, paragraph))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "52730801-50ff-4904-fac0-de8039919272",
        "id": "Mfvrd4otW9xz"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Semantic role labeling of relevant sentence:  {'Agent': 'The weather forecast', 'Patient': 'rain', 'Beneficiary': 'the weekend', 'Predicate': 'predict'}\n",
            "Answer:  the weekend, rain\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Example usage\n",
        "paragraph = \"\"\"Alice visited the museum on Tuesday afternoon. The stock market experienced a significant drop last week. Bob enjoys playing the guitar in his free time. The weather forecast predicts rain for the weekend. Sarah bought a new dress for the upcoming party. The new cafe downtown serves the best coffee in the city. Mike is planning a hiking trip next month. The movie was released to critical acclaim. The cat slept peacefully on the windowsill. Jenny is taking a course on digital marketing with her laptop.\"\"\"\n",
        "question = \"When did Alice visit museum?\"\n",
        "print(\"Answer: \", answer_question(question, paragraph))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e5341a93-2aab-4598-f4eb-263dcd7e5c9a",
        "id": "pY32_P_GXcgS"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Semantic role labeling of relevant sentence:  {'Agent': 'Alice', 'Patient': 'Tuesday afternoon', 'Predicate': 'visit'}\n",
            "Answer:  Tuesday afternoon\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Example usage\n",
        "paragraph = \"\"\"Alice visited the museum on Tuesday afternoon. The stock market experienced a significant drop last week. Bob enjoys playing the guitar in his free time. The weather forecast predicts rain for the weekend. Sarah bought a new dress for the upcoming party. The new cafe downtown serves the best coffee in the city. Mike is planning a hiking trip next month. The movie was released to critical acclaim. The cat slept peacefully on the windowsill. Jenny is taking a course on digital marketing with her laptop.\"\"\"\n",
        "question = \"Where was cat sleeping?\"\n",
        "print(\"Answer: \", answer_question(question, paragraph))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c34f0a30-06ec-48d0-e805-f379e25370e7",
        "id": "cS7-4q3XYfXj"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Semantic role labeling of relevant sentence:  {'Agent': 'The cat', 'Predicate': 'sleep', 'Patient': 'the windowsill'}\n",
            "Answer:  the windowsill\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Evaluation metrics"
      ],
      "metadata": {
        "id": "sYvnLJ-5BKai"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import precision_recall_fscore_support, accuracy_score"
      ],
      "metadata": {
        "id": "luV4BM-tZx1Z"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def evaluate_qa_system_with_cosine_similarity(y_pred, ground_truth_answers):\n",
        "    \"\"\"\n",
        "    Evaluate the QA system using cosine similarity.\n",
        "\n",
        "    Args:\n",
        "    - y_pred (list of str): List of preddicted answers.\n",
        "    - ground_truth_answers (list of str): List of ground truth answers.\n",
        "\n",
        "    Returns:\n",
        "    - float: Average cosine similarity score.\n",
        "    \"\"\"\n",
        "\n",
        "    # initializations\n",
        "    embeddings_true = []\n",
        "    embeddings_pred = []\n",
        "    cosine_similarities = []\n",
        "\n",
        "    # computing cosine similarity for each predicted output with ground truth\n",
        "    for yt, yp, i in zip(ground_truth_answers, y_pred, range(len(y_pred))):\n",
        "        embeddings_true.append(sentence_model.encode(yt[i]))\n",
        "        embeddings_pred.append(sentence_model.encode(yp[i]))\n",
        "        cosine_similarities.append(cosine_similarity(embeddings_true[i], embeddings_pred[i]))\n",
        "    print(\"Cosine similarity: \",cosine_similarities)\n",
        "\n",
        "    # computing average cosine similarity for given samples\n",
        "    average_cosine_similarity = np.mean([cosine_similarities[i] for i in range(len(y_pred))])\n",
        "\n",
        "    return average_cosine_similarity"
      ],
      "metadata": {
        "id": "Pt9juqfYZbgK"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Example usage\n",
        "paragraphs = [paragraph]\n",
        "questions = [\"What did Alice do?\", \"Who enjoys playing guitar?\", \"What did Sarah buy for the upcoming party?\", \"How is Jenny taking her course?\", \"Who serves the best coffee in the city?\", \"When is rain predicted?\",\"When did Alice visit museum?\", \"Where was cat sleeping?\" ]\n",
        "ground_truth_answers = [\"visit museum\", \"Bob\", \"a new dress\", \"through her laptop\", \"the new cafe\", \"the weekend\", \"tuesday afternoon\", \"in the windowsill\"]\n",
        "y_pred = [\"Tuesday afternoon, visit\", \"Bob\",\"a new dress, buy\",\"her laptop\", \"The new cafe downtown\", \"the weekend, rain\", \"Tuesday afternoon\", \"the windowsill\"]\n",
        "metrics = evaluate_qa_system_with_cosine_similarity(y_pred, ground_truth_answers)\n",
        "print(\"Average cosine similarity achieved: \", metrics)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H1eQItOtZeXb",
        "outputId": "649a1bbf-538c-4dc0-8173-88d355f7d723"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cosine similarity:  [0.40277010202407837, 0.9999999403953552, 1.000000238418579, 0.575816810131073, 1.000000238418579, 1.0000001192092896, 1.0, 0.5372871160507202]\n",
            "Average cosine similarity achieved:  0.8144843205809593\n"
          ]
        }
      ]
    }
  ],
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