20190926 14일차 a (Crawler and Text Analysis, Inverted Index and Query processing, DTM, TDM).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  오늘 공부 내용\n",
    "- Web Crawling and Basic Text Analysis\n",
    "- http://www.cs.virginia.edu/~hw5x/Course/IR2015/_site/docs/PDFs/Crawler%20and%20Text%20Analysis.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**헷갈리기 쉬운 용어 정의** : 같은 뜻으로 사용되는 용어들\n",
    "- Collection = Corpus \n",
    "- Vocabulary = Lexicon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Browsing vs Querying 의 차이**\n",
    "\n",
    "- Browsing \n",
    "    - 내가 뭘 찾고 싶은지 모를때 좋다.\n",
    "    - 정보 제공 방식: Push (with query)\n",
    "    - 예시: 네이버 메인페이지 처럼 검색하지 않아도 뉴스, 잡지 등의 정보가 노출되어있는 경우.\n",
    "    - 분류모델로 사용할 수 있다.\n",
    "\n",
    "- Querying\n",
    "    - 내가 뭘 찾고 싶은지 알때 좋다.\n",
    "    - 정보 제공 방식: Pull (without query)\n",
    "    - 예시: 구글 메인페이지처럼 사용자가 검색하기 전에는 아무런 정보가 제공되지 않음. 검색하면 해당하는 정보만 보여주는 경우."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Web Crawler는 ranking이나 indexer에 모두 필요하므로 IR(정보검색)에서 중요한 역할을 한다.**\n",
    "    - ranking: 각 page마다 link를 찾으므로 page link ranking을 낼 수 있다.\n",
    "    - indexer: 각 page의 단어를 tagging하기 위해서 돌아다녀야한다.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "focused crawling이 필요한 이유\n",
    "    - 모든 웹사이트를 크로울링하기는 불가능하다.\n",
    "    - 모든 document의 가치는 동등하지 않다. \n",
    "    \n",
    "    => 중요한 document만 크로울링하는 것이 효과적이다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Crawling Policy\n",
    "    - Avoid duplicate visit: 방문했던 페이지를 재방문하지 않음.\n",
    "        - visited list를 만들어서 제한함.\n",
    "    - Re-visit policy: 시간이 지남에 따라 web page가 Create/Update/Delete 될 수 있으므로 크로울링을 다시해야할 필요가 생김\n",
    "        - 크로울링할때 방문 시간을 기록해서 일정 시간 지난 후에 다시 크로울링하도록 자동화\n",
    "    - Depth: 최초 발견한 웹페이지로부터 링크를 너무 많이 타고 들어가지 않도록 제한함.\n",
    "    - Domain: 특정 도메인 내에서만 크로울링하도록 제한함"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Tokenize\n",
    "    - 단어, 구, 문장 등 여러가지 방법으로 token을 만들 수 있다.\n",
    "        - 우리 수업에서 배운 N-gram, Byte Pair Encoding을 쓸 수도 있고, 어제 배운 stanford 형태소분석기를 쓸 수도 있고, 정규식을 쓸 수도 있고시간/장소를 포함하는 tokenizer도 있고 여러가지 방법이 존재한다.\n",
    "    - token을 어떻게 만드느냐에 따라서 성능이 달라진다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bag Of Words(BOW)\n",
    "- 단어들의 순서는 고려 X 단지 빈도수만 가지고 오겠다!!\n",
    "각 단어에 고유한 인덱스를 부여함.\n",
    "그리고 카운트를 하게 된다.\n",
    "- 모든 단어는 순서가 상관이 없고 서로 independent하다는 가정\n",
    "- 컴퓨터가 이해할 수 있는 형태의 매트릭스\n",
    "- 장점: 설계하기 쉽고 만들기 간단하다.\n",
    "- 단점: 순서가 반영되지 않으므로, 문법적인 요소를 다 잃어버린다. 단, 문서가 엄청나게 많으면 패턴이 많이 등장해서 단점이 극복될 수 있다.\n",
    "    - NLP 쪽은 그래서 확률통계 기반 모델이 많다.\n",
    "    - 순수 NLP 쪽은 문법적인 요소를 무시할 수 없으므로 시간 순서가 중요하다. RNN 등의 딥러닝 쪽으로 시도할 수 있다."
   ]
  },
  {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Document Term Matrix (DTM)**\n",
    "- Bag of words로 만든 Representation\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Full text indexing(전문 색인)\n",
    ": 모든 단어를 indexing한다.\n",
    "\n",
    "- N-gram을 사용하면: \n",
    "    - 장점: 문맥을 고려할 수 있게 된다.\n",
    "    - 단점: token 수가 너무 많이 늘어난다.\n",
    "- 모든 단어를 indexing 하면:\n",
    "    - 장점: 모든 정보를 이용할 수 있음, 완전 자동화 가능\n",
    "    - 단점: storage 문제, vocabulary gap(cars vs car)\n",
    "- 해결책 1: 현질(메모리, 코어 등..)-최근에는 컴퓨팅능력(time complexity, space complexity)은 문제가 아니게 됨.\n",
    "- 해결책 2: **Construct controlled vocabulary**\n",
    "    - 모든 단어가 아니라 조금 줄이자.\n",
    "    - zipf's law를 이용해서 상위 20개 빈도 단어만 삭제해도 엄청난 수의 단어가 줄어들 것이다. (구조상 필요하고 의미를 갖지 않는 of, the 같은 단어들)\n",
    "    - zipf's law를 이용해서 하위 저빈도 단어도 삭제\n",
    "    - stopword(불용어) 처리\n",
    "    - stemming(어간 추출)로 의미있는 단어만 살리기\n",
    "    - lemantation?\n",
    "    - 신조어 처리\n",
    "    - 한글은 음절의 길이도 중요하다\n",
    "    - 영어에서는 .lower로 대소문자를 통일시켰음."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalization\n",
    "- 사람이 해야한다. (컴퓨터가 해줄 수 없는 부분)\n",
    "- .lower() 소문자로 통일\n",
    "- 여러가지 표현을어떻게 표준화 시킬 것인지\n",
    "    - U.S.A를 USA로 인식시키기 위해서 .을 지워버리면 문장의 마침표도 사라지는 문제가 생긴다.\n",
    "- 결국, 데이터를 보고 데이터에 따라서 연구자가 Normalization 정책을 결정해야한다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Automatic text indexing\n",
    "- 현대는 컴퓨팅 파워가 좋으니, 문법적 손실이 생길 위험을 감수하지말고 데이터를 다 쓰자. \n",
    "- modern IR 방식"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NER(named entity recognition)\n",
    "- 어제 다운 받은 stanford 형태소 분석기 압축 파일 중에 있다.\n",
    "- 국문 버전은 etri에 있다.\n",
    "- Automatic text indexing에서 최근에 성능이 좋은 방법.\n",
    "- 전문색인에서 사용한다.(Full Automatic Text Indexing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 오늘 공부 내용\n",
    "\n",
    "- **Inverted Index and Query processing**\n",
    "- http://www.cs.virginia.edu/~hw5x/Course/IR2015/_site/docs/PDFs/Inverted%20Index.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DTM의 단점\n",
    "- 3페이지\n",
    "- query term에서 단어들을 다 한번씩 찾아본다.\n",
    "- document는 엄청 많을 것이므로, 메모리에 다 올릴 수가 없어서 하나씩 열어보고 일치하는 단어가 있으면 candidtate에 추가한다. \n",
    "- document가 1000000개라고 하면 100000번 열어보므로 시간이 오래 걸릴 것이다.\n",
    "- query term이 길수록(검색 단어가 길수록) 오래 걸린다. query term의 길이만큼 문서를 열어봄\n",
    "- 각 단어에 대한 candidte를 교집합하거나 합집합해서 결과를 만든다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 얼마나 비효율적인가 봅시다.\n",
    "\n",
    "from konlpy.corpus import kobill\n",
    "from konlpy.tag import Kkma\n",
    "\n",
    "Collction = dict()\n",
    "for _ in kobill.fileids():\n",
    "    Collction[_] = kobill.open(_).read()\n",
    "    \n",
    "kkma = Kkma()\n",
    "Vocabulary = list()\n",
    "for d, t in Collction.items():\n",
    "    Vocabulary.extend([_ for _ in kkma.morphs(t) \n",
    "                       if 1 < len(_) < 7])\n",
    "    \n",
    "Vocabulary = list(set(Vocabulary))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1155"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Vocabulary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "t2i = lambda t: Vocabulary.index(t)\n",
    "i2t = lambda i:Vocabulary[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['안녕', '하', '세요']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# morphs는 품사태그 없이 반환함.\n",
    "\n",
    "Kkma().morphs(\"안녕하세요\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "DocumentRepresentation = list(\n",
    "    list(0 for _ in range(len(Vocabulary)))\n",
    "for _ in range(len(Collction)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 10)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(DocumentRepresentation), \\\n",
    "len(DocumentRepresentation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "d2i = lambda d: list(Collction.keys()).index(d)\n",
    "i2d = lambda i: list(Collction.keys())[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "for d, t in Collction.items():\n",
    "    row = d2i(d)\n",
    "    for _ in kkma.morphs(t):\n",
    "        if _ in Vocabulary:\n",
    "            col = t2i(_)\n",
    "            DocumentRepresentation[row][col] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "271"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(DocumentRepresentation[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "법안\n",
      "의원\n"
     ]
    }
   ],
   "source": [
    "Query = \"법안 의원\"\n",
    "QueryRepresentation = list(0 \n",
    "                           for _ in range(len(Vocabulary)))\n",
    "for _ in kkma.morphs(Query):\n",
    "    print(_)\n",
    "    if _ in Vocabulary:\n",
    "        col = t2i(_)\n",
    "        QueryRepresentation[col] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# O(4 * 10 * 1144)\n",
    "CandidateList = list()\n",
    "for i in range(len(Vocabulary)):\n",
    "    if QueryRepresentation[i] == 1:\n",
    "        tempCandidate = []\n",
    "        \n",
    "        # bottle neck 구간\n",
    "        for d in range(len(Collction)):\n",
    "            for t in range(len(Vocabulary)):\n",
    "                if DocumentRepresentation[d][t] == 1 and \\\n",
    "                t == i:\n",
    "                    tempCandidate.append(i2d(d))\n",
    "                    break\n",
    "        # bottle neck 구간\n",
    "        CandidateList.append(tempCandidate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['1809890.txt',\n",
       "  '1809891.txt',\n",
       "  '1809892.txt',\n",
       "  '1809893.txt',\n",
       "  '1809894.txt',\n",
       "  '1809895.txt',\n",
       "  '1809896.txt',\n",
       "  '1809899.txt'],\n",
       " ['1809890.txt', '1809891.txt', '1809892.txt', '1809893.txt']]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CandidateList"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1809891.txt', '1809890.txt', '1809892.txt', '1809893.txt']"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resultList = CandidateList[0]\n",
    "for _ in CandidateList[1:]:\n",
    "    resultList = list(set(resultList).intersection(_))\n",
    "resultList"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 검색 결과에서 relevant를 알 수 가 없다. ranking을 매길 수가 없다.\n",
    ">\n",
    "> 최신순, 가나다 순으로 밖에 정렬할 수 없다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 지금은 문서양이 얼마 없어서 빨라보이지만 굉장히 느린 시스템이다. \n",
    ">\n",
    "> timeit해보면 알 수 있다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### inverted indexing(역 색인)\n",
    " \n",
    "TDM(Term Document Matrix)\n",
    "    - 단어에 대하여 등장한 document를 매칭한다.\n",
    "    - Query를 미리 정해둔다.\n",
    "    - time complexity가 linear하게 나온다. "
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 위에서 만든 DTM을 TDM으로 바꿔봅시다.\n",
    "\n",
    "TDM = [list() for _ in range(len(Vocabulary))]\n",
    "for d, t in Collction.items():\n",
    "    row = d2i(d)\n",
    "    for _ in kkma.morphs(t):\n",
    "        if _ in Vocabulary:\n",
    "            col = t2i(_)\n",
    "            if row not in TDM[col]:\n",
    "                TDM[col].append(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[6]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TDM[t2i(\"규제\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# O(4 * |L|)\n",
    "CandidateList = list()\n",
    "for q in [_ for _ in kkma.morphs(Query)\n",
    "         if _ in Vocabulary]:\n",
    "    CandidateList.append([i2d(_) for _ in TDM[t2i(q)]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1809891.txt', '1809890.txt', '1809892.txt', '1809893.txt']"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resultList = CandidateList[0]\n",
    "for _ in CandidateList[1:]:\n",
    "    resultList = list(set(resultList).intersection(_))\n",
    "resultList"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 이론 설명"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### stay in memory\n",
    "- Dictionary 방식: 빠른 랜덤 액세스가 필요할때 좋음.\n",
    "- 메모리에 다 올림.\n",
    "- 예시\n",
    "    - hash table\n",
    "    - B-tree(balanced tree)\n",
    "    - trie(tree 구조인데, 자식이 2개 이상/tree는 원래 자식이 2개)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### linked list\n",
    "- Posting 방식\n",
    "- TDM에서 Document(postings)를 디스크에 따로 둔다. (메모리에 올리지 않아도 됨)\n",
    "- 포인터 개념으로 posting에 효율적인 액세스 구조\n",
    "- 내일 배움!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### map reduce\n",
    "- 분산처리\n",
    "- 구글이 만듦\n",
    "- 나중에 배움!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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