January 27, 2014

Turkish Language Resources

This post contains links to various Turkish language resources that I have collected. Please send a comment if you find Turkish resources that you would like to see on this page.

Turkish Data Depository (TDD)

Our new website that we hope will act as a central location for all computational resources and models for Turkish. Most resources listed below and more will be found there.

ITU Turkish Natural Language Processing Pipeline

This webpage provides the Turkish NLP Tools and API developed in Istanbul Technical University by our Natural Language Processing group led by Asst. Prof. Dr. Gülşen Eryiğit including a Tokenizer, Normalization tools, Deasciifier, Vocalizer, Spelling Corrector, Turkish word detector, Morphological Analyzer, Morphological Disambiguator, Named Entity Recognizer, and Dependency Parser.

TS Corpus

Taner Sezer's TS Corpus is a 491M token general purpose Turkish corpus. See comments below for details.

BounWebCorpus

Hasim Sak's page contains some useful Turkish language resources and code in addition to a large web corpus. (2021-08-26: link broken)

Bibliography

Özgür Yılmazel's Bibliography on Turkish Information Retrieval and Natural Language Processing.

tr-disamb.tgz

Turkish morphological disambiguator code. Slow but 96% accurate. See Learning morphological disambiguation rules for Turkish for the theory.

correctparses_03.txt.gz, train.merge.gz

Turkish morphology training files. Semi-automatically tagged, has limited accuracy. Two files have the same data except the second file also includes the ambiguous parses (the first parse on each line is correct). A newer version can be found in TrMor2006.

test.1.2.dis.gz, test.merge.gz

Turkish morphology test files, second one includes ambiguous parses (the first parse on each line is correct). The data is hand tagged, it has good accuracy. A newer version can be found in TrMor2006.

tr-tagger.tgz

Turkish morphological tagger, includes Oflazer's finite state machines for Turkish. From Kemal Oflazer. Please use with permission. Requires the publically available Xerox Finite State software. For a better tagger use Morse.

turklex.tgz, pc_kimmo.tgz

Turkish morphology rules for PC-Kimmo by Kemal Oflazer. Older implementation. Originally from www.cs.cmu.edu

Milliyet.clean.bz2

Original Milliyet corpus, one token per line, 19,627,500 total tokens. Latin-5 encoded, originally was in three 11MB parts. From Kemal Oflazer. Please use with permission.

Turkish wordnet

From Kemal Oflazer. Please use with permission. (2021-08-26: link broken)

METU-Sabanci Turkish Treebank

Turkish treebank with dependency annotations. Please use with permission.

sozluk.txt.gz

English-Turkish dictionary (127157 entries, 826K) Originally from www.fen.bilkent.edu.tr/~aykutlu.

sozluk-boun.txt.gz
Turkish word list (25822 words, 73K) Originally from www.cmpe.boun.edu.tr/courses/cmpe230

Avrupa Birliği Temel Terimler Sözlüğü

(Originally from: www.abgs.gov.tr/ab_dosyalar, Oct 6, 2006)

BilisimSozlugu.zip

Bilişim Sözlüğü by Bülent Sankur (Originally from: www.bilisimsozlugu.com, Oct 9, 2006)

turkish.el

Emacs extension that automatically adds accents to Turkish words while typing on an English keyboard.

en-tr.zip, lm.tr.gz

Turkish English parallel text from Kemal Oflazer, Statistical Machine Translation into a Morphologically Complex Language, Invited Paper, In Proceedings of CICLING 2008 -- Conference on Intelligent Text Processing and Computational Linguistics, Haifa, Israel, February 2008 (lowercased and converted to utf8). The Turkish part of the dataset is "selectively split", i.e. some suffixes are separated from their stems, some are not. lm.tr.gz is the Turkish text used to develop the language model.

Zemberek (blog) (github)

Zemberek doğal dil işleme kütüphanesi.

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January 20, 2014

dlib: Deniz's C Library

(Download the latest version from Github)

Abstract: The main design goal of dlib is to make C programming as practical and concise as Perl, and yet to retain its fine grained control over memory use. For years I have been starting my research projects in Perl, because I can get results quickly. Then at some point I rewrite everything in C, because I run out of memory (I usually work with large statistical models). The tiny memory overheads for each string, array, hash, or object add up when you have billions of them. Yet, I cringe at the prospect of expressing the same ideas in C using many more lines of code than Perl. Why should that be the case?

Contents

Introduction

The main design goal of dlib is to make C programming as practical and concise as Perl, and yet to retain its fine grained control over memory use. For years I have been starting my research projects in Perl, because I can get results quickly. Then at some point I rewrite everything in C, because I run out of memory (I usually work with large statistical models). The tiny memory overheads for each string, array, hash, or object add up when you have billions of them. Yet, I cringe at the prospect of expressing the same ideas in C using many more lines of code than Perl. Why should that be the case?

As a motivational example, let's say we want to "count the number of times each word appears in a file". It takes 11 words to express this program in English (7 if you don't count the function words). Perl is just as concise (7 tokens excluding punctuation):

while(<>) {
  for (split) {
    $cnt{$_}++;
  }
}

Imagine what a nightmare this tiny program is in standard C (or especially Java). The main feature that distinguishes Perl among high level languages and enables a Perl programmer to write concise code is that it provides reasonable defaults for everything, so that things you express most often can be written concisely (human languages and compression algorithms use the same trick). And of course as with every modern language these days Perl has built in dynamic arrays, hashes, and regexps and makes them practical to use with its concise syntax. This is my feeble, incomplete, and evolving attempt to import some of these ideas into C. Among other things, dlib introduces some iteration constructs for file I/O and string tokenization and built-in hashes to make programs of this sort almost as concise:

forline (str, NULL) {
  fortok (tok, str) {
    cnt(tok)++;
  }
}

Compilation

To compile your C code with dlib, #include "dlib.h" at the beginning of your files and add dlib.c to the files to be compiled. My typical gcc options are: 

-O3 -D_GNU_SOURCE -std=c99 -pedantic -Wall

I tried to stick with the C99 standard but used some extensions that can be turned off. Use the -D_GNU_SOURCE compiler flag if you want to compile with these extensions without warnings. Define the following flags with -D compiler options if you don't have, or don't want these extensions:

_NO_POPEN   Do not use pipes in File I/O.
_NO_GETLINE Do not use GNU getline.
_NO_PROC    Do not use the proc filesystem for memory reporting.
_NO_MUSABLE Do not use GNU malloc_usable_size for memory reporting.
NDEBUG      Turn off debug output and assert checks (from assert.h).

File input

forline(s, f), is an iteration construct which executes the statements in its body with the undeclared string variable s set to each line in file f. All the file handle creation, opening and closing, allocating and freeing of string buffers etc. are taken care of behind the scenes. The following example prints the length of each line in "file.txt":

forline (str, "file.txt") {
  printf("%d\n", strlen(str));
}

There are some special f arguments:

  • If f==NULL, stdin is read.
  • If pipes are available, and f starts with <, as in f=="< cmd args", the cmd is run with args and its stdout is read.
  • If pipes are available, compressed files with .gz, .xz, and .bz2 extensions are automatically handled.
  • Otherwise a regular file with path f is read.

Tokenization

fortok(t, s) is an iteration construct which executes the statements in its body with the undeclared string variable t set to each whitespace separated token in string s. Empty tokens are skipped. It modifies and tokenizes s the same way strtok does, but unlike strtok it is reentry safe (i.e. multiple nested fortok loops are ok). fortok3(t, s, d) takes an additional character array d to specify delimiter characters any one of which will act as a delimiter. fortok(t, s) is equivalent to fortok3(t, s, " \f\n\r\t\v"). Examples:

char *str = strdup("  To be    or not");
// need strdup because fortok won't work with constant strings
fortok (tok, str) {
  printf("[%s]", tok); // prints "[To][be][or][not]"
}

char *pwd = strdup(":root::/root:/bin/bash:");
fortok3 (tok, pwd, ":/") {
  printf("[%s]", tok); // prints "[root][root][bin][bash]"
}

split(char *str, const char *delim, char **argv, size_t argv_len) returns the tokens of a string in an array. This is useful because one often needs to refer to the n'th token in a string rather than iterating over them. split splits the str into tokens delimited by single characters from delim (including empty tokens) and sets the pointers in the argv array to successive tokens. argv should have enough space to hold argv_len pointers. Split stops when argv_len tokens are reached or str runs out. It modifies str by replacing delimiter characters with '\0'. Returns the number of tokens placed in argv. Example:

char *pwd = strdup(":root::/root:/bin/bash:");
char **toks = malloc(10 * sizeof(char *));
int n = split(pwd, ":/", toks, 10);
for (int i = 0; i < n; i++) {
  printf("[%s]", toks[i]); // prints "[][root][][][root][][bin][bash][]"
}

Note the difference in delimiter handling between split and fortok. fortok and fortok3 follow strtok and see multiple delimiter characters in a row as a single delimiter, whereas split, following strsep, will perceive empty fields. fortok is intended for free text, split for structured data.

NOTES: Neither split nor fortok can handle a multi-character delimiter like "::". Not sure if using different delimiter handling strategies in the two will be confusing for the user. Probably need something like chop or trim eventually.

Dynamic arrays

darr_t (pronounced "dar-ty") is a general purpose dynamic array type in dlib.

  • darr_t darr(size_t n, type t) returns a new array that has an initial capacity of at least n and element type t. n==0 is ok. The elements are not initialized.
  • void darr_free(darr_t a) frees the space allocated for a.
  • size_t len(darr_t a) gives the number of elements in a. A new array has len(a) == 0.
  • size_t cap(darr_t a) gives the current capacity of a. It will grow as needed, up to a maximum of (1<<58) elements.

A regular C array reference, such as a[i], can be used as an l-value (a[i] = 10), an r-value (x = a[i]), or both (a[i]++). I think this type of access makes the code more readable and I wanted the same flexibility with darr_t. So instead of the usual set, get, add etc. functions we have a single macro val(darr_t a, size_t i, type t) which gives a reference to the i'th element of a that can be used as an l-value or an r-value. All of the following are valid expressions and do what they look like they are supposed to:

darr_t a = darr(0, int);  // initialize array
val(a, i, int) = 10;      // set an element
int x = val(a, i, int);   // get an element
val(a, i, int)++;         // increment an element
int *p = &val(a, i, int); // get a pointer to an element
val(a, len(a), int) = 5;  // add a new element, increments len(a)

The user can request or set any index of a darr_t from 0 to (1<<58-1). The darr_t will never complain and resize itself to permit the access (if memory allows). len(a) will be one plus the largest index accessed (read or write). Some may think this gives too much power to the user to shoot themselves in the foot. A read access to a never initialized element will return a random value. An accidental read or write to a very large index may blow up the memory. Oh well, don't do it.

Hash tables

Hash tables are implemented as dynamic arrays (darr_t) with some search logic. You can initialize and free a hash table using darr and darr_free exactly like you do with dynamic arrays. The macro D_HASH defines an inline function xget (the prefix x is user specified) that searches the array for an element matching a given key and returns a pointer to it:

etype *xget(darr_t htable, ktype key, bool insert);

The etype and ktype are user specified types for array elements and their keys respectively. The boolean argument insert determines what happens when a matching element cannot be found. If insert == true one will be created (in a user specified manner) and added to the array and xget will return its pointer. If insert == false, xget will return NULL.

forhash(etype, eptr, htable, isnull)

is an iteration construct for hash tables which executes the statements in its body with etype *eptr bound to each element of darr_t htable for which the macro or function isnull returns false.

In order to define the xget function, the macro D_HASH takes the following nine arguments (I am open to suggestions to reduce this number). The last six can be macros (preferrable) or functions.

  • prefix: The prefix added to get to allow multiple hash table types (e.g. x for xget).
  • etype: Type of array element.
  • ktype: Type of key.
  • keyof: ktype keyof(etype e) returns the key for element e.
  • kmatch: bool kmatch(ktype a, ktype b) returns true if two keys match.
  • khash: size_t khash(ktype k) is a hash function for keys.
  • einit: etype einit(ktype k) returns a new element with key matching k.
  • isnull: bool isnull(etype e) returns true if array element e is empty.
  • mknull: void mknull(etype e) modifies array element e to become empty.

NOTE: This horribly convoluted interface is necessary to have a general enough implementation that can support sets (where the key and the element are one and the same) and maps (where the key is a function of the element); element types that are primitive (uint32_t, char*) or compound (structs, bit-fields), keys that can be components of compound elements (e.key) or arbitrary functions of primitive ones (e & mask), arrays that store the elements themselves or their pointers etc. It is not intended for daily use. Think of it as your hash table code generator. Once you correctly generate the code for the few hash table types you use, you will hopefully never need D_HASH again.

Here is an example hash table for counting strings:

#include <stdio.h>
#include "dlib.h"

typedef struct strcnt_s { char *key; size_t cnt; } strcnt_t;
#define keyof(e) ((e).key)
extern size_t fnv1a(const char *k);  // string hash fn defined in dlib
#define strmatch(a,b) (!strcmp((a),(b)))
#define newcnt(k) ((strcnt_t) { strdup(k), 0 })
#define keyisnull(e) ((e).key == NULL)
#define keymknull(e) ((e).key = NULL)

D_HASH(s, strcnt_t, char *, keyof, strmatch, fnv1a, newcnt, keyisnull, keymknull)

Given the function sget defined by D_HASH above, we can now write our word counting example from the introduction.

#define cnt(k) sget(htable, (k), true)->cnt

int main() {
  darr_t htable = darr(0, strcnt_t);
  forline (str, NULL) {
    fortok (tok, str) {
      cnt(tok)++;
    }
  }

And print the counts:

  forhash (strcnt_t, e, htable, keyisnull) {
    printf("%s\t%lu\n", e->key, e->cnt);
  }
}
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January 14, 2014

Smoothing a Tera-word Language Model

Deniz Yuret. In The 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL-08: HLT) (Download PDF, code)
Update of Jan 14, 2014: The latest version gets rid of all glib dependencies (glib is broken, it blows up your code without warning if your arrays or hashes get too big). The glookup program reads ngram patterns with wildcards from stdin and prints their counts from the Web1T Google ngram data. The glookup.pl script quickly searches for a given pattern in uncompressed Google Web1T data. Use the C version for bulk processing, the perl version to get a few counts quickly. The model.pl script optimizes and tests various language models. See the README in the code repository for details. Typical usage:
$ model.pl -patterns < text > patterns
$ glookup -p web1t_path < patterns > counts
$ model.pl -counts counts < text



Abstract: Frequency counts from very large corpora, such as the Web 1T dataset, have recently become available for language modeling. Omission of low frequency n-gram counts is a practical necessity for datasets of this size. Naive implementations of standard smoothing methods do not realize the full potential of such large datasets with missing counts. In this paper I present a new smoothing algorithm that combines the Dirichlet prior form of (Mackay and Peto, 1995) with the modified back-off estimates of (Kneser and Ney, 1995) that leads to a 31% perplexity reduction on the Brown corpus compared to a baseline implementation of Kneser-Ney discounting.
2 comments:

January 09, 2014

FASTSUBS: An Efficient and Exact Procedure for Finding the Most Likely Lexical Substitutes Based on an N-gram Language Model

Deniz Yuret. Nov. 2012. Signal Processing Letters, IEEE. Volume: 19, Issue: 11, Pages: 725-728. DOI, URL. (Download the pdf, code, and data (1GB). Our EMNLP-2012 paper uses FASTSUBS to get the best published result in part of speech induction.).
Update of Jan 9, 2014: The latest version gets rid of all glib dependencies (glib is broken, it blows up your code without warning if your arrays or hashes get too big). It also fixes a bug: The previous versions of the code and the paper assumed that the log back-off weights were upper bounded by 0 which they are not. In my standard test of generating the top 100 substitutes for all the 1,222,974 positions in the PTB, this caused a total of 66 low probability substitutes to be missed and 31 to be listed out of order. Finally a multithreaded version, fastsubs-omp is implemented. The number of threads can be controlled using the environment variable OMP_NUM_THREADS. The links above have been updated to the latest version.

Abstract: Lexical substitutes have found use in areas such as paraphrasing, text simplification, machine translation, word sense disambiguation, and part of speech induction. However the computational complexity of accurately identifying the most likely substitutes for a word has made large scale experiments difficult. In this paper I introduce a new search algorithm, FASTSUBS , that is guaranteed to find the K most likely lexical substitutes for a given word in a sentence based on an n-gram language model. The computation is sub-linear in both K and the vocabulary size V . An implementation of the algorithm and a dataset with the top 100 substitutes of each token in the WSJ section of the Penn Treebank are available at http://goo.gl/jzKH0.
2 comments:

January 07, 2014

NLP Course Slides

Here are the slides I used in my NLP course last semester. More to come...
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