DSC 2008 – June 2008, Thessaloniki, Greece Automatic Acquisition of Synonyms Using the Web as a Corpus Svetlin Nakov, Sofia University "St. Kliment Ohridski" 3rd Annual South East European Doctoral Student Conference (DSC2008): Infusing Knowledge and Research in South East Europe

DSC 2008 – June 2008, Thessaloniki, Greece Introduction We want to automatically extract all pairs of synonyms inside given text Our goal is: Design an algorithm that can distinguish between synonyms and non-synonyms Our approach: Measure semantic similarity using the Web as a corpus Synonyms are expected to have higher semantic similarity than non-synonyms

DSC 2008 – June 2008, Thessaloniki, Greece The Paper in One Slide Measuring semantic similarity Analyze the words local contexts Use the Web as a corpus Similar contexts similar words TF.IDF weighting & reverse context lookup Evaluation 94 words (Russian fine arts terminology) 50 synonym pairs to be found 11pt average precision: 63.16%

DSC 2008 – June 2008, Thessaloniki, Greece Contextual Web 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.

DSC 2008 – June 2008, Thessaloniki, Greece Contextual Web 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 large corpora in any language Searching some word in Google can return up to snippets of texts The target word is given along with its local context: few words before and after it Target language can be specified

DSC 2008 – June 2008, Thessaloniki, Greece Contextual Web Similarity Web as a corpus Example: Google query for "flower" Flowers, Plants, Gift Baskets FLOWERS.COM - Your Florist... Flowers, balloons, plants, gift baskets, gourmet food, and teddy bears presented by 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.

DSC 2008 – June 2008, Thessaloniki, Greece Contextual Web Similarity Measuring semantic similarity For given two words their local contexts are extracted from the Web A set of words and their frequencies 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

DSC 2008 – June 2008, Thessaloniki, Greece Contextual Web Similarity Example of context words frequencies wordcount fresh217 order204 rose183 delivery165 gift124 welcome98 red87... word: flower wordcount Internet291 PC286 technology252 order185 new174 Web159 site word: computer

DSC 2008 – June 2008, Thessaloniki, Greece Contextual Web Similarity Example of frequency vectors Similarity = cosine(v 1, v 2 ) #wordfreq. 0alias3 1alligator2 2amateur0 3apple zap0 5000zoo6 v 1 : flower #wordfreq. 0alias7 1alligator0 2amateur8 3apple zap3 5000zoo0 v 2 : computer

DSC 2008 – June 2008, Thessaloniki, Greece TF.IDF Weighting TF.IDF (term frequency times inverted document frequency) Statistical measure in information retrieval Shows how important is a certain word for a given document in a set of documents Increases proportionally to the number of word's occurrences in the document Decreases proportionally to the total number of documents containing the word

DSC 2008 – June 2008, Thessaloniki, Greece Reverse Context Lookup Local context extracted from the Web can contain arbitrary parasite words like "online", "home", "search", "click", etc. Internet terms appear in any Web page Such words are not likely to be associated with the target word Example (for the word flowers) "send flowers online", "flowers here", "order flowers here" Will the word "flowers" appear in the local context of "send", "online" and "here"?

DSC 2008 – June 2008, Thessaloniki, Greece Reverse Context Lookup If two words are semantically related, then Both of them should appear in the local contexts of each other Let #{x,y} = number of occurrences of x in the local context of y For any word w and a word from its local context w c, we define their strength of semantic association p(w,w c ) as follows: p(w, w c ) = min{ #(w, w c ), #(w c,w) } We use p(w, w c ) as vector coordinates We introduce a minimal occurrence threshold (e.g. 5) to filter words appearing just by chance

DSC 2008 – June 2008, Thessaloniki, Greece Data Set We use a list of 94 Russian words: Terms extracted from texts in the subject of fine arts Limited to nouns only The data set: There are 50 synonym pairs in these words We expect to find them by our algorithms абрис, адгезия, алмаз, алтарь, амулет, асфальт, беломорит, битум, бородки, ваятель, вермильон,..., шлифовка, штихель, экспрессивность, экспрессия, эстетизм, эстетство

DSC 2008 – June 2008, Thessaloniki, Greece Experiments We tested few modifications of our contextual Web similarity algorithm Basic algorithm (without modifications) TF.IDF weighting Reverse context lookup with different frequency threshold

DSC 2008 – June 2008, Thessaloniki, Greece Experiments RAND – random ordering of all the pairs SIM – the basic algorithm for extraction of semantic similarity from the Web Context size of 3 words Without analyzing the reverse context With lemmatization SIM+TFIDF – modification of the SIM algorithm with TF.IDF weighting REV2, REV3, REV4, REV5, REV6, REV7 – the SIM algorithm + reverse context lookup with frequency thresholds of: 2, 3, 4, 5, 6 and 7

DSC 2008 – June 2008, Thessaloniki, Greece Resources Used We used the following resources: Google Web search engine: extracted the first results for Russian words Russian lemma dictionary: wordforms and lemmata A list of 507 Russian stop words

DSC 2008 – June 2008, Thessaloniki, Greece Evaluation Our algorithms arrange all pairs of words according to their semantic similarity We expect the 50 synonyms pairs to be at the top of the result list We count how many synonyms are found in the top N results (e.g. top 5, top 10, etc.) We measure precision and recall We measure 11pt average precision to evaluate the results

DSC 2008 – June 2008, Thessaloniki, Greece SIM Algorithm – Results nWord 1Words 2 Semantic Similarity Syno- nyms n n 1выжиганиепирография yes100.00%2% 2тонированиетонировка yes100.00%4% 3гематиткровавик yes100.00%6% 4подрамокподрамник yes100.00%8% 5оливинперидот yes100.00%10% 6полированиешлифование no83.33%10% 7полировкашлифовка no71.43%10% 8амулетталисман yes75.00%12% 9пластификаторымягчители yes77.78%14%... Precision and recall obtained by the SIM algorithm

DSC 2008 – June 2008, Thessaloniki, Greece Comparison of the Algorithms Comparison of the algorithms (number of synonyms in the top results) Algorithm Max RAND SIM SIM+TFIDF REV REV REV REV REV REV

DSC 2008 – June 2008, Thessaloniki, Greece Comparison of the Algorithms (11pt Average Precision) Comparing RAND, SIM, SIM+TDIDF and REV2 … REV7 11pt Average Precision 1,15% 58,98% 63,16% n/a 0,00% 10,00% 20,00% 30,00% 40,00% 50,00% 60,00% 70,00% RANDSIMSIM+TFIDFREV2REV3REV4REV5REV6REV7

DSC 2008 – June 2008, Thessaloniki, Greece Results (Precision-Recall Graph) Comparing the recall-precision graphs of evaluated algorithms

DSC 2008 – June 2008, Thessaloniki, Greece Discussion Our approach is original because: Measures automatically semantic similarity Uses the Web as a corpus Does not rely on any preexisting corpora Does not requires semantic resources like WordNet and EuroWordNet Works for any language Tested for Bulgarian and Russian Uses reverse-context lookup and TF.IDF Significant improvement in quality

DSC 2008 – June 2008, Thessaloniki, Greece Discussion Good accuracy, but far away from 100% Known problems of the proposed algorithms: Semantically related words are not always synonyms red – blue wood – pine apple – computer Similar contexts does not always mean similar words (distributional hypothesis) The Web as a corpus introduces noise Google returns the first results only

DSC 2008 – June 2008, Thessaloniki, Greece Discussion Known problems of the proposed algorithms: Google ranks higher news portals, travel agencies and retail sites than books, articles and forum messages Local context always contain noise Working with words, not capturing phrases

DSC 2008 – June 2008, Thessaloniki, Greece Conclusion and Future Work Conclusion Our algorithms can distinguish between synonyms and non-synonyms Accuracy should be improved Future Work Additional techniques to distinguish between synonyms and semantically related words Improve the semantic similarity measure algorithm

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DSC 2008 – June 2008, Thessaloniki, Greece Questions? Automatic Acquisition of Synonyms Using the Web as a Corpus