arrow - manual page for arrow

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

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

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 --query-server=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.

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.

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

archer(1), crossbow(1), rainbow(1).

The full documentation for arrow will be provided as a Texinfo manual. If the info and arrow programs are properly installed at your site, the command

info arrow

should give you access to the complete manual.

You can also find documentation and updates for libbow at http://www.cs.cmu.edu/~mccallum/bow