ranlp 2007 – september 27-29, 2007, borovets, bulgaria improved word alignments using the web as a...

RANLP 2007 – September 27-29, 2007, Borovets, Bulgaria

Improved Word Alignments Using the

Web as a Corpus

Preslav Nakov, University of California, BerkeleySvetlin Nakov, Sofia University "St. Kliment Ohridski"

Elena Paskaleva, Bulgarian Academy of Sciences

International Conference RANLP 2007(Recent Advances in Natural Language Processing)


Statistical Machine Translation (SMT) 1988 – IBM models 1, 2, 3, 4 and 5

Start with bilingual parallel sentence-aligned corpus

Learn translation probabilities of individual words

2004 – PHARAOH model Learn translation probabilities for phrases Alignment template approach – extracts

translation phrases from word alignments Improved word alignments in sentences

improve translation quality!


Word Alignments The word alignments problem

Given a bilingual parallel sentence-aligned corpus align the words in each sentence with corresponding words in its translation

Example English sentence

Example Bulgarian sentence

Try our same day delivery of fresh flowers, roses, and unique gift baskets.

Опитайте нашите свежи цветя, рози и уникални кошници с подаръци с доставка на същия ден.


Word Alignments – Exampletry

oursame

daydelivery

offresh

flowersroses

andunique

giftbaskets

опитайтенашитесвежицветярозииуникалникошницисподаръцисдоставканасъщияден


Our Method Use combination of

Orthographic similarity measure Semantic similarity measure Competitive linking

Orthographic similarity measure Modified weighted minimum-edit-distance

Semantic similarity measure Analyses the co-occurring words in the

local contexts of the target words using the Web as a corpus


Orthographic Similarity Minimum Edit Distance Ratio (MEDR)

MED(s1, s2) = the minimum number of INSERT / REPLACE / DELETE operations for transforming s1 to s2

Longest Common Subsequence Ratio

(LCSR) LCS(s1, s2) = the longest common

subsequence of s1 and s2


Orthographic Similarity Modified Minimum Edit Distance Ratio

(MMEDR) for Bulgarian / Russian1. Normalize the strings2. Assign weights for the edit operations

Normalizing the strings Hand-crafted rules

Strip the Russian letters "ь" and "ъ" Remove the Russian "й" at the endings Remove the definite article in Bulgarian

(e.g. "ът", "ят" at the endings)


Orthographic Similarity Assigning weights for the edit operations

0.5-0.9 for the vowel to vowel substitutions, e.g. 0.5 for е о

0.5-0.9 for some consonant-consonant replacements, e.g. с з

1.0 for all other edit operations Example: Bulgarian първият and the

Russian первый (first) Normalization produces първи and перви,

thus MMED = 0.5 (weight 0.5 for ъ о)


Semantic Similarity What is local context?

Few words before and after the target word

The words in the local context of given word are semantically related to it

Need to exclude the stop words: prepositions, pronouns, conjunctions, etc. Stop words appear in all contexts

Need of sufficiently big corpus

Same day delivery of fresh flowers, roses, and unique gift baskets from our online boutique. Flower delivery online by local florists for birthday flowers.


Semantic Similarity Web as a corpus

The Web can be used as a corpus to extract the local context for given word The Web is the largest possible corpus Contains big corpora in any language

Searching some word in Google can return up to 1 000 excerpts of texts The target word is given along with its local

context: few words before and after it Target language can be specified


Semantic Similarity Web as a corpus

Example: Google query for "flower"

Flowers, Plants, Gift Baskets - 1-800-FLOWERS.COM - Your Florist ...

Flowers, balloons, plants, gift baskets, gourmet food, and teddy bears presented by 1-800-FLOWERS.COM, Your Florist of Choice for over 30 years.

Margarita Flowers - Delivers in Bulgaria for you! - gifts, flowers, roses ...

Wide selection of BOUQUETS, FLORAL ARRANGEMENTS, CHRISTMAS ECORATIONS, PLANTS, CAKES and GIFTS appropriate for various occasions. CREDIT cards acceptable.Flowers, plants, roses, & gifts. Flowers delivery with fewer ...

Flowers, roses, plants and gift delivery. Order flowers from ProFlowers once, and you will never use flowers delivery from florists again.


Semantic Similarity Measuring semantic similarity

For given two words their local contexts are extracted from the Web A set of words and their frequencies Apply lemmatization

Semantic similarity is measured as similarity between these local contexts Local contexts are represented as

frequency vectors for given set of words Cosine between the frequency vectors in

the Euclidean space is calculated


Semantic Similarity Example of context words frequencies

word countfresh 217order 204rose 183delivery 165gift 124welcome 98red 87... ...

word: flowerword countInternet 291PC 286technology 252order 185new 174Web 159site 146... ...

word: computer


Semantic Similarity Example of frequency vectors

Similarity = cosine(v1, v2)

# word freq.0 alias 31 alligator 22 amateur 03 apple 5... ... ...

4999 zap 05000 zoo 6

v1: flower# word freq.0 alias 71 alligator 02 amateur 83 apple 133... ... ...

4999 zap 35000 zoo 0

v2: computer


Cross-Lingual Semantic Similarity We are given two words in different

languages L1 and L2

We have a bilingual glossary G of translation pairs {p ∈ L1, q ∈ L2}

Measuring cross-lingual similarity:

1. We extract the local contexts of the target words from the Web: C1 ∈ L1 and C2 ∈ L2

2. We translate the context

3. We measure similarity between C1* and C2

C1*C1G


Competitive Linking What is competitive linking?

One-to-one bi-directional word alignments algorithm

Greedy "best first" approach Links the most probable pair first, removes

it, and repeats the same for the rest


Applying Competitive Linking1. Make all words lowercase2. Remove punctuation3. Remove the stop words: prepositions,

pronouns, conjunctions, etc. We don't align them

4. Align the most similar pair of words Using the orthographic similarity

combined with the semantic similarity5. Remove the aligned words6. Align the rest of the sentences


Our Method – Example Bulgarian sentence

Russian sentence

Процесът на създаването на такива рефлекси е по-сложен, но същността им е еднаква.

Процесс создания таких рефлексов сложнее, но существо то же.


Out Method – Example1. Remove the stop words

Bulgarian: на, на, такива, е, но, им, е Russian: таких, но, то

2. Align рефлекси and рефлексов (semantic similarity = 0.989)

3. Align по-сложен and сложнее (orthographic similarity = 0.750)

4. Align процесът and процесс (orthographic similarity = 0.714)

5. Align създаването and создания (orthographic similarity = 0.544)

6. Align процесът and процесс (orthographic similarity = 0.536)

7. Not aligned: еднаква


Our Method – Exampleпроцесът

насъздаването

натакива

рефлексие

по-сложенно

същносттаим

ееднаква

процесс

создания

таких

рефлексов

сложнее

но

существо

то

же


Evaluation We evaluated the following algorithms

BASELINE: the traditional alignment algorithm (IBM model 4)

LCSR, MEDR, MMEDR: orthographic similarity algorithms

WEB-ONLY: semantic similarity algorithm WEB-AVG: average of WEB-ONLY and

MMEDR WEB-MAX: maximum of WEB-ONLY and

MMEDR WEB-CUT: 1 if MMEDR(s1, s2) >= α (0 < α <

1), or WEB-ONLY(s1, s2) otherwise


Testing Data and Experiments Testing data set

A corpus of 5 827 parallel sentences Training set: 4 827 sentences Tuning set: 500 sentences Testing set: 500 sentences

Experiments Manual evaluation of WEB-CUT AER for competitive linking Translation quality: BLEU / NIST


Manual Evaluation of WEB-CUT Aligned the texts of the testing data set

Used competitive linking and WEB-CUT for α=0.62

Obtained 14,246 distinct word pairs Manually evaluated the aligned pairs as:

Correct Rough (considered incorrect) Wrong (considered incorrect)

Calculated precision and recall For the case MMEDR < 0.62


Manual Evaluation of WEB-CUT Precision-recall curve


Evaluation of Alignment Error Rate Gold standard for alignment

For the first 100 sentences Created manually by a linguist Stop words and punctuation were removed

Evaluated the alignment error rate (AER) for competitive linking Evaluated for all the algorithms LCSR, MEDR, MMEDR, WEB-ONLY, WEB-

AVG, WEB-MAX and WEB-CUT


Evaluation of Alignment Error Rate AER for competitive linking


Evaluation of Translation Quality Built a Russian Bulgarian statistical

machine translation (SMT) system Extracted from the training set the distinct

word pairs aligned with competitive linking Added them twice as additional “sentence”

pairs to the training corpus Trained log-linear model for SMT with

standard feature functions Used minimum error rate training on the

tuning set Evaluated BLUE and NIST score on the

testing set


Evaluation of Translation Quality Translation quality: BLEU


Evaluation of Translation Quality Translation quality: NIST


Resources We used the following resources:

Bulgarian-Russian parallel corpus: 5 827 sentences

Bilingual Bulgarian / Russian glossary: 3 794 pairs of translation words

A list of 599 Bulgarian / 508 Russian stop words

Bulgarian lemma dictionary: 1 000 000 wordforms and 70 000 lemmata

Russian lemma dictionary: 1 500 000 wordforms and 100 000 lemmata


Conclusion and Future Work Conclusion

Semantic similarity extracted from the Web can improve statistical machine translation

For similar languages like Bulgarian and Russian orthographic similarity is useful

Future Work Improve MMED with automatic leaned rules Improve the semantic similarity algorithm

Filter parasite words like "site", "click", etc. Replace competitive linking with maximum

weight bipartite matching


Questions?

Improved Word Alignments Using the Web as a Corpus

ranlp 2007 – september 27-29, 2007, borovets, bulgaria improved word alignments using the web as a...

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