Text file books

Every entry has 6 fields:. Please help me. Thanks in advance! Create Book class add book id, title, author, loan days, availability and member code fields to it. While you are reading the lines when you read line "Book" create a book object and fill it, when you read a "TextBook" create a TextBook and fill that.

Instead of keeping a String array create two Lists for Book and TextBook and add your objects to lists. Stack Overflow for Teams — Collaborate and share knowledge with a private group. Create a free Team What is Teams? Collectives on Stack Overflow. Learn more. Reading and storing data from text file in java Ask Question. Asked 6 years, 4 months ago. Active 6 years, 4 months ago. Viewed times. Russell, S.

Synsets also come with a prose definition and some example sentences:. Although definitions help humans to understand the intended meaning of a synset, the words of the synset are often more useful for our programs. To eliminate ambiguity, we will identify these words as car. This pairing of a synset with a word is called a lemma. We can get all the lemmas for a given synset , look up a particular lemma , get the synset corresponding to a lemma , and get the "name" of a lemma :.

Unlike the words automobile and motorcar , which are unambiguous and have one synset, the word car is ambiguous, having five synsets:.

For convenience, we can access all the lemmas involving the word car as follows. Your Turn: Write down all the senses of the word dish that you can think of. Now, explore this word with the help of WordNet, using the same operations we used above. WordNet synsets correspond to abstract concepts, and they don't always have corresponding words in English. These concepts are linked together in a hierarchy. Some concepts are very general, such as Entity , State , Event — these are called unique beginners or root synsets.

Others, such as gas guzzler and hatchback , are much more specific. A small portion of a concept hierarchy is illustrated in 2. WordNet makes it easy to navigate between concepts. For example, given a concept like motorcar , we can look at the concepts that are more specific; the immediate hyponyms. We can also navigate up the hierarchy by visiting hypernyms.

Some words have multiple paths, because they can be classified in more than one way. There are two paths between car. Explore the WordNet hierarchy by following the hypernym and hyponym links. Hypernyms and hyponyms are called lexical relations because they relate one synset to another. These two relations navigate up and down the "is-a" hierarchy. Another important way to navigate the WordNet network is from items to their components meronyms or to the things they are contained in holonyms.

To see just how intricate things can get, consider the word mint , which has several closely-related senses. We can see that mint. There are also relationships between verbs. For example, the act of walking involves the act of stepping , so walking entails stepping. Some verbs have multiple entailments:.

Some lexical relationships hold between lemmas, e. You can see the lexical relations, and the other methods defined on a synset, using dir , for example: dir wn. We have seen that synsets are linked by a complex network of lexical relations. Given a particular synset, we can traverse the WordNet network to find synsets with related meanings. Knowing which words are semantically related is useful for indexing a collection of texts, so that a search for a general term like vehicle will match documents containing specific terms like limousine.

Recall that each synset has one or more hypernym paths that link it to a root hypernym such as entity. Two synsets linked to the same root may have several hypernyms in common cf 2. If two synsets share a very specific hypernym — one that is low down in the hypernym hierarchy — they must be closely related. Of course we know that whale is very specific and baleen whale even more so , while vertebrate is more general and entity is completely general. We can quantify this concept of generality by looking up the depth of each synset:.

Similarity measures have been defined over the collection of WordNet synsets which incorporate the above insight.

Comparing a synset with itself will return 1. Consider the following similarity scores, relating right whale to minke whale , orca , tortoise , and novel. Although the numbers won't mean much, they decrease as we move away from the semantic space of sea creatures to inanimate objects.

Several other similarity measures are available; you can type help wn for more information. It can be accessed with nltk. Hundreds of annotated text and speech corpora are available in dozens of languages. Non-commercial licences permit the data to be used in teaching and research.

For some corpora, commercial licenses are also available but for a higher fee. Corpora List is a mailing list for discussions about corpora, and you can find resources by searching the list archives or posting to the list.

Of 7, languages, only a few dozen have substantial digital resources suitable for use in NLP. This chapter has touched on the field of Corpus Linguistics. The original description of WordNet is Fellbaum, The goal of this chapter is to answer the following questions: What are some useful text corpora and lexical resources, and how can we access them with Python?

Which Python constructs are most helpful for this work? How do we avoid repeating ourselves when writing Python code? Note In 1. Text nltk. Note Most NLTK corpus readers include a variety of access methods apart from words , raw , and sents.

Web and Chat Text Although Project Gutenberg contains thousands of books, it represents established literature. Asian girl Polished leather and strawb FreqDist [w. Note Your Turn: Choose a different section of the Brown Corpus, and adapt the previous example to count a selection of wh words, such as what , when , where , who , and why. Reuters Corpus The Reuters Corpus contains 10, news documents totaling 1.

Inaugural Address Corpus In 1. Annotated Text Corpora Many text corpora contain linguistic annotations, representing POS tags, named entities, syntactic structures, semantic roles, and so forth. Corpora in Other Languages NLTK comes with corpora for many languages, though in some cases you will need to learn how to manipulate character encodings in Python before using these corpora see 3.

Note Your Turn: Pick a language of interest in udhr. Text Corpus Structure We have seen a variety of corpus structures so far; these are summarized in 2. Loading your own Corpus If you have a your own collection of text files that you would like to access using the above methods, you can easily load them with the help of NLTK's PlaintextCorpusReader.

Conditions and Events A frequency distribution counts observable events, such as the appearance of words in a text. Counting Words by Genre In 2. Plotting and Tabulating Distributions Apart from combining two or more frequency distributions, and being easy to initialize, a ConditionalFreqDist provides some useful methods for tabulation and plotting. Note Your Turn: Working with the news and romance genres from the Brown Corpus, find out which days of the week are most newsworthy, and which are most romantic.

Generating Random Text with Bigrams We can use a conditional frequency distribution to create a table of bigrams word pairs. Creating Programs with a Text Editor The Python interactive interpreter performs your instructions as soon as you type them. Try this now, and enter the following one-line program: print 'Monty Python' Save this program in a file called monty. Functions Suppose that you work on analyzing text that involves different forms of the same word, and that part of your program needs to work out the plural form of a given singular noun.

Modules Over time you will find that you create a variety of useful little text processing functions, and you end up copying them from old programs to new ones. A Pronouncing Dictionary A slightly richer kind of lexical resource is a table or spreadsheet , containing a word plus some properties in each row.

Note A subtlety of the above program is that our user-defined function stress is invoked inside the condition of a list comprehension. P-CH perch puche poche peach petsche poach pietsch putsch pautsch piche pet P-K pik peek pic pique paque polk perc poke perk pac pock poch purk pak pa P-L pil poehl pille pehl pol pall pohl pahl paul perl pale paille perle po P-N paine payne pon pain pin pawn pinn pun pine paign pen pyne pane penn p P-P pap paap pipp paup pape pup pep poop pop pipe paape popp pip peep pope P-R paar poor par poore pear pare pour peer pore parr por pair porr pier P-S pearse piece posts pasts peace perce pos pers pace puss pesce pass pur P-T pot puett pit pete putt pat purt pet peart pott pett pait pert pote pa P-Z pays p.

Comparative Wordlists Another example of a tabular lexicon is the comparative wordlist. Shoebox and Toolbox Lexicons Perhaps the single most popular tool used by linguists for managing data is Toolbox , previously known as Shoebox since it replaces the field linguist's traditional shoebox full of file cards. Senses and Synonyms Consider the sentence in 1a.

If we replace the word motorcar in 1a by automobile , to get 1b , the meaning of the sentence stays pretty much the same: 1 a.

Note Your Turn: Write down all the senses of the word dish that you can think of. The WordNet Hierarchy WordNet synsets correspond to abstract concepts, and they don't always have corresponding words in English. More Lexical Relations Hypernyms and hyponyms are called lexical relations because they relate one synset to another.

NOUN Semantic Similarity We have seen that synsets are linked by a complex network of lexical relations. Note Several other similarity measures are available; you can type help wn for more information. NLTK comes with many corpora, e. Some text corpora are categorized, e.

A conditional frequency distribution is a collection of frequency distributions, each one for a different condition. They can be used for counting word frequencies, given a context or a genre.

Python programs more than a few lines long should be entered using a text editor, saved to a file with a. Python functions permit you to associate a name with a particular block of code, and re-use that code as often as necessary.

Some functions, known as "methods", are associated with an object and we give the object name followed by a period followed by the function, like this: x. To find out about some variable v , type help v in the Python interactive interpreter to read the help entry for this kind of object. WordNet is a semantically-oriented dictionary of English, consisting of synonym sets — or synsets — and organized into a network.

Some functions are not available by default, but must be accessed using Python's import statement. Experiment with the operations described in this chapter, including addition, multiplication, indexing, slicing, and sorting.

How many word tokens does this book have? How many word types? Count occurrences of men , women , and people in each document. What has happened to the usage of these words over time? What problem might arise with this approach? Can you suggest a way to avoid this problem?

They give this example of correct usage: However you advise him, he will probably do as he thinks best. Can you find pairs of words which have quite different meanings across the two texts, such as monstrous in Moby Dick and in Sense and Sensibility?

The article gives the following statistic about teen language: "the top 20 words used, including yeah, no, but and like, account for around a third of all words. What do you conclude about this statistic? Try to explain them in terms of your own impressionistic understanding of the different genres. Can you find other closed classes of words that exhibit significant differences across different genres? How many distinct words does it contain? What fraction of words in this dictionary have more than one possible pronunciation?

You can get all noun synsets using wn. Include the full set of Brown Corpus genres nltk. Which genre has the lowest diversity greatest number of tokens per type?

Is this what you would have expected? Choose your own words and try to find words whose presence or absence is typical of a genre.

Discuss your findings. Suppose that all the words of a text are ranked according to their frequency, with the most frequent word first. Zipf's law states that the frequency of a word type is inversely proportional to its rank i. For example, the 50th most common word type should occur three times as frequently as the th most common word type.

Write a function to process a large text and plot word frequency against word rank using pylab. Do you confirm Zipf's law? Hint: it helps to use a logarithmic scale. What is going on at the extreme ends of the plotted line? Generate random text, e. You will need to import random first. Use the string concatenation operator to accumulate characters into a very long string. Then tokenize this string, and generate the Zipf plot as before, and compare the two plots.

What do you make of Zipf's Law in the light of this? Clicking in the preview panel, will cause the cursor in the editor to be positioned over the element you clicked. If you click a link pointing to another file in the book, that file will be opened in the edit and the preview panel, automatically. You can turn off the automatic syncing of position and live preview of changes — by buttons under the preview panel. The live update of the preview panel only happens when you are not actively typing in the editor, so as not to be distracting or slow you down, waiting for the preview to render.

The preview panel shows you how the text will look when viewed. However, the preview panel is not a substitute for actually testing your book an actual reader device. It is both more, and less capable than an actual reader. It will tolerate errors and sloppy markup much better than most reader devices. It will also not show you page margins, page breaks and embedded fonts that use font name aliasing. Use the preview panel while you are working on the book, but once you are done, review it in an actual reader device or software emulator.

The preview panel does not support embedded fonts if the name of the font inside the font file does not match the name in the CSS font-face rule. You can use the Check Book tool to quickly find and fix any such problem fonts. One, perhaps non-obvious, use of the preview panel is to split long HTML files.

While viewing the file you want to split, click the Split mode button under the preview panel. Then simply move your mouse to the place where you want to split the file and click. A thick green line will show you exactly where the split will happen as you move your mouse.

Once you have found the location you want, simply click and the split will be performed. Splitting the file will automatically update all links and references that pointed into the bottom half of the file and will open the newly split file in an editor.

You can also split a single HTML file at multiple locations automatically, by right clicking inside the file in the editor and choosing Split at multiple locations. This will allow you to easily split a large file at all heading tags or all tags having a certain class and so on. The Live CSS panel shows you all the style rules that apply to the tag you are currently editing. The name of tag, along with its line number in the editor are displayed, followed by a list of matching style rules.

It is a great way to quickly see which style rules apply to any tag. The view also has clickable links in blue , which take you directly to the location where the style was defined, in case you wish to make any changes to the style rules. Style rules that apply directly to the tag, as well as rules that are inherited from parent tags are shown. The panel also shows you what the finally calculated styles for the tag are.

Properties in the list that are superseded by higher priority rules are shown with a line through them. The Table of Contents view shows you the current table of contents in the book.

Double clicking on any entry opens the place that entry points to in an editor. Words are shown with the number of times they occur in the book and the language the word belongs to.

Language information is taken from the books metadata and from lang attributes in the HTML files. This allows the spell checker to work well even with books that contain text in multiple languages. You can double click a word to highlight the next occurrence of that word in the editor.

This is useful if you wish to manually edit the word, or see what context it is in. To change a word, simply double click one of the suggested alternative spellings on the right, or type in your own corrected spelling and click the Change selected word to button.

This will replace all occurrences of the word in the book. You can also right click on a word in the main word list to change the word conveniently from the right click menu. You can have the spelling checker ignore a word for the current session by clicking the Ignore button. You can also add a word to the user dictionary by clicking the Add to dictionary button. The spelling checker supports multiple user dictionaries, so you can select the dictionary you want the word added to.

You can also have the spelling checker display all the words in your book, not just the incorrectly spelled ones. This is useful to see what words are most common in your book and to run a simple search and replace on individual words. If you make any changes to the book by editing files while the spell check tool is open, you should click the Refresh button in the Spell check tool. If you do not do this and continue to use the Spell check tool, you could lose the changes you have made in the editor.

To exclude an individual file from being spell checked when running the spell check tool, you can use the Exclude files button or add the following comment just under the opening tag in the file:. The spelling checker comes with builtin dictionaries for the English and Spanish languages. The spell checker can use dictionaries from the LibreOffice program in the. You can download these dictionaries from The LibreOffice Extensions repository.

This shows you all Unicode characters, simply click on the character you want to type. If you hold Ctrl while clicking, the window will close itself after inserting the selected character. This tool can be used to insert special characters into the main text or into any other area of the user interface, such as the Search and replace tool.

Because there are a lot of characters, you can define your own Favorite characters, that will be shown first. Simply right click on a character to mark it as favorite. You can also right click on a character in favorites to remove it from favorites.

Finally, you can re-arrange the order of characters in favorites by clicking the Re-arrange favorites button and then drag and dropping the characters in favorites around. You can also directly type in special characters using the keyboard. Finally, you can type in special characters by using HTML named entities. The replacement happens only when typing the semi-colon.

You open it by right clicking a location in the preview panel and choosing Inspect. You can even dynamically edit the styles and see what effect your changes have instantly.

Note that editing the styles does not actually make changes to the book contents, it only allows for quick experimentation. The ability to live edit inside the Inspector is under development. You can use this tool to check all links in your book that point to external websites. The tool will try to visit every externally linked website, and if the visit fails, it will report all broken links in a convenient format for you to fix.

The tool will find all such resources and automatically download them, add them to the book and replace all references to them to use the downloaded files. Often when editing EPUB files that you get from somewhere, you will find that the files inside the EPUB are arranged haphazardly, in different sub-folders.

This tool allows you to automatically move all files into sub-folders based on their types. Note that this tool only changes how the files are arranged inside the EPUB, it does not change how they are displayed in the File browser.

The editor includes the ability to import files in some other e-book formats directly as a new EPUB, without going through a full conversion. Every line in the report is hot-linked. Double clicking a line jumps to the place in the book where that item is used or defined as appropriate. For example, in the Links view, you can double click entries the Source column to jump to where the link is defined and entries in the Target column to jump to where the link points.

The calibre HTML editor is very powerful. Spelling errors in the text inside HTML tags and attributes such as title are highlighted. The spell checking is language aware, based on the value of the lang attribute of the current tag and the overall book language.

Special characters that can be hard to distinguish such as non-breaking spaces, different types of hyphens, etc. If the filename they point to does not exist, the filename is marked with a red underline. You can also hold down the Ctrl key and click on any filename inside a link tag to open that file in the editor automatically. Similarly, Ctrl clicking a class name will take you to the first style rule that matches the tag and class.

Right clicking a class name in an HTML file will allow you to rename the class, changing all occurrences of the class throughout the book and all its stylesheets. When editing an e-book, one of the most tedious tasks is creating links to other files inside the book, or to CSS stylesheets, or images.

You have to figure out the correct filename and relative path to the file. The editor has auto-complete to make that easier.

As you type a filename, the editor automatically pops up suggestions. Simply use the Tab key to select the correct file name. The editor even offers suggestions for links pointing to an anchor inside another HTML file. After you type the character, the editor will show you a list of all anchors in the target file, with a small snippet of text to help you choose the right anchor.