arrow(1)

NAME

arrow - manual page for arrow

SYNOPSIS

arrow [OPTION...] [ARG...]

DESCRIPTION

Arrow is a document retrieval front-end to libbow, it uses TFIDF to retrieve relevant documents.

EXAMPLES

If you have a database of documents in foo you would just need to type arrow --index foo to create the database. You could then make queries by typing arrow --query then typing your query, and pressing Control-D.

If you want to make many queries, it will be more efficient to run arrow as a server, and query it multiple times without restarts by communicating through a socket. Type, for example, arrow --queryserver=9876 and access it through port number 9876. For example: telnet localhost 9876 In this mode there is no need to press Control-D to end a query. Simply type your query on one line, and press return.

OPTIONS

General options: For building data structures from text files:
-i, --index: tokenize training documents found under ARG..., build weight vectors, and save them to disk; For doing document retrieval using the data structures built with -i:
-c, --compare=FILE: Print the TFIDF cosine similarity metric of the query with this FILE.
-n, --num-hits-to-show=N: Show the N documents that are most similar to the query text (default N=1)
-q, --query[=FILE]: tokenize input from stdin [or FILE], then print document most like it
--query-forking-server=PORTNUM: Run arrow in socket server mode, forking a new process with every connection. Allows multiple simultaneous connections.
--query-server=PORTNUM Run arrow in socket server mode.: Diagnostics
--print-coo: Print word co-occurrence statistics.
--print-idf: Print, in unsorted order the IDF of all words in the model's vocabulary
--annotations=FILE: The sarray file containing annotations for the files in the index
-b, --no-backspaces: Don't use backspace when verbosifying progress (good for use in emacs)
-d, --data-dir=DIR: Set the directory in which to read/write word-vector data (default=~/.<program_name>).
--random-seed=NUM: The non-negative integer to use for seeding the random number generator
--score-precision=NUM: The number of decimal digits to print when displaying document scores
-v, --verbosity=LEVEL: Set amount of info printed while running; (0=silent, 1=quiet, 2=show-progess,...5=max); Lexing options
--append-stoplist-file=FILE: Add words in FILE to the stoplist.
--exclude-filename=FILENAME: When scanning directories for text files, skip files with name matching FILENAME.
-g, --gram-size=N: Create tokens for all 1-grams,... N-grams.
-h, --skip-header: Avoid lexing news/mail headers by scanning forward until two newlines.
--istext-avoid-uuencode: Check for uuencoded blocks before saying that the file is text, and say no if there are many lines of the same length.
--lex-pipe-command=SHELLCMD: Pipe files through this shell command before lexing them.
--max-num-words-per-document=N: Only tokenize the first N words in each document.
--no-stemming: Do not modify lexed words with a stemming function. (usually the default, depending on lexer)
--replace-stoplist-file=FILE: Empty the default stoplist, and add space-delimited words from FILE.
-s, --no-stoplist: Do not toss lexed words that appear in the stoplist.
--shortest-word=LENGTH Toss lexed words that are shorter than LENGTH.: Default is usually 2.
-S, --use-stemming: Modify lexed words with the `Porter' stemming function.
--use-stoplist: Toss lexed words that appear in the stoplist. (usually the default SMART stoplist, depending on lexer)
--use-unknown-word: When used in conjunction with -O or -D, captures all words with occurrence counts below threshold as the `<unknown>' token
--xxx-words-only: Only tokenize words with `xxx' in them; Mutually exclusive choice of lexers
--flex-mail: Use a mail-specific flex lexer
--flex-tagged: Use a tagged flex lexer
-H, --skip-html: Skip HTML tokens when lexing.
--lex-alphanum: Use a special lexer that includes digits in tokens, delimiting tokens only by non-alphanumeric characters.
--lex-infix-string=ARG Use only the characters after ARG in each word for: stoplisting and stemming. If a word does not contain ARG, the entire word is used.
--lex-suffixing: Use a special lexer that adds suffixes depending on Email-style headers.
--lex-white: Use a special lexer that delimits tokens by whitespace only, and does not change the contents of the token at all---no downcasing, no stemming, no stoplist, nothing. Ideal for use with an externally-written lexer interfaced to rainbow with --lex-pipe-cmd.; Feature-selection options
-D, --prune-vocab-by-doc-count=N: Remove words that occur in N or fewer documents.
-O, --prune-vocab-by-occur-count=N: Remove words that occur less than N times.
-T, --prune-vocab-by-infogain=N: Remove all but the top N words by selecting words with highest information gain.; Weight-vector setting/scoring method options
--binary-word-counts: Instead of using integer occurrence counts of words to set weights, use binary absence/presence.
--event-document-then-word-document-length=NUM: Set the normalized length of documents when --event-model=document-then-word
--event-model=EVENTNAME: Set what objects will be considered the `events' of the probabilistic model. EVENTNAME can be one of: word, document, document-then-word.; Default is `word'.
--infogain-event-model=EVENTNAME: Set what objects will be considered the `events' when information gain is calculated. EVENTNAME can be one of: word, document, document-then-word.; Default is `document'.
-m, --method=METHOD: Set the word weight-setting method; METHOD may be one of: tfidf_words, tfidf_log_words, tfidf_log_occur, tfidf, default=naivebayes.
--print-word-scores: During scoring, print the contribution of each word to each class.
--smoothing-dirichlet-filename=FILE: The file containing the alphas for the dirichlet smoothing.
--smoothing-dirichlet-weight=NUM: The weighting factor by which to muliply the alphas for dirichlet smoothing.
--smoothing-goodturing-k=NUM: Smooth word probabilities for words that occur NUM or less times. The default is 7.
--smoothing-method=METHOD: Set the method for smoothing word probabilities to avoid zeros;
METHOD may be one of: goodturing, laplace, mestimate, wittenbell
--uniform-class-priors When setting weights, calculating infogain and: scoring, use equal prior probabilities on classes.
-?, --help: Give this help list
--usage: Give a short usage message
-V, --version: Print program version
Mandatory or optional arguments to long options are also mandatory or optional for any corresponding short options.

REPORTING BUGS

Please report bugs related to this program to Andrew McCallum <mccallum@cs.cmu.edu>. If the bugs are related to the Debian package send bugs to submit@bugs.debian.org

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