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| "title": "Distributional Similarity of Multi-Word Expressions", |
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| "first": "Laura", |
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| "institution": "The University of Sydney NSW 2006", |
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| "first": "James", |
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| "last": "Curran", |
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| "abstract": "Most existing systems for automatically extracting lexical-semantic resources neglect multi-word expressions (MWEs), even though approximately 30% of gold-standard thesauri entries are MWEs. We present a distributional similarity system that identifies synonyms for MWEs. We extend Grefenstette's SEXTANT shallow parser to first identify bigram MWEs using collocation statistics from the Google WEB1T corpus. We extract contexts from WEB1T to increase coverage on the sparser bigrams.", |
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| "text": "Most existing systems for automatically extracting lexical-semantic resources neglect multi-word expressions (MWEs), even though approximately 30% of gold-standard thesauri entries are MWEs. We present a distributional similarity system that identifies synonyms for MWEs. We extend Grefenstette's SEXTANT shallow parser to first identify bigram MWEs using collocation statistics from the Google WEB1T corpus. We extract contexts from WEB1T to increase coverage on the sparser bigrams.", |
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| "text": "Lexical-semantic resources, such as WordNet (Fellbaum, 1998) , are used in many applications in Natural Language Processing (NLP). Unfortunately, they are expensive and time-consuming to produce and are prone to bias and limited coverage. Automatically extracting these resources is crucial to overcoming the knowledge bottleneck in NLP.", |
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| "text": "(Fellbaum, 1998)", |
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| "section": "Introduction", |
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| "text": "Existing distributional approaches to semantic similarity focus on unigrams, with very little work on extracting synonyms for multi-word expressions (MWEs). In this work, we extend an existing system to support MWEs by identifying bigram MWEs using collocation statistics (Manning and Sch\u00fctze, 1999) . These are calculated using n-gram counts from the Google WEB1T corpus (Brants and Franz, 2006) .", |
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| "section": "Introduction", |
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| "text": "We evaluate against several gold-standard thesauri and observe a slight decrease in overall performance when the bigram MWEs were included. This is unsurprising since the larger vocabulary and sparser contextual information for bigrams makes the task significantly harder. We also experimented with contexts extracted from WEB1T in an attempt to overcome the data sparseness problem. Inspection of the results for individual headwords revealed many cases where the synonyms returned were significantly better when bigram data was included.", |
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| "section": "Introduction", |
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| "text": "Distributional similarity relies on the distributional hypothesis that similar terms appear in similar contexts (Harris, 1954) . Here we extend the SEXTANT parser (Grefenstette, 1994) to include multi-word terms and syntactic contexts. Curran (2004) experiments with different parsers for extracting contextual information, including SEXTANT, MINIPAR (Lin, 1994) , RASP (Briscoe and Carroll, 2002) , and CASS (Abney, 1996) . Lin (1998) used MINIPAR and Weeds (2003) used RASP for distributional similarity calculations. MINIPAR is the only parser to identify a range of MWEs that has been used for distributional similarity. Weeds (2003) and Curran (2004) evaluate measures for calculating distributional similarity. We follow (Curran, 2004) in using the weighted Jaccard measure with truncated t-test relation weighting for our experiments.", |
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| "text": "MINIPAR and Weeds (2003)", |
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| "section": "Background", |
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| "text": "The initial step in creating a thesaurus for MWEs is to identify potential MWE headwords using collocation statistics. We used various statistical tests, e.g. the t-test and the log-likelihood test (Manning and Sch\u00fctze, 1999) , calculated over the Google WEB1T unigram and bigram counts. These counts, calculated over 1 trillion words of web text, gave the most reliable counts. However, highly ranked terms, e.g.", |
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| "section": "Detecting MWEs", |
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| "text": "Contact Us and Site Map, demonstrate bias towards web-related terminology. This list of selected bigrams is used to detect bigrams within the BNC using a modified version of the Viterbi algorithm.", |
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| "section": "Detecting MWEs", |
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| "text": "Grefenstette's (2004) (SEXTANT) parser was extended to extract contextual information for the list of selected bigrams extracted above. Adding these bigrams does not result in a substantial increase in the number of relations which implies that there is very little contextual information available about the bigram data. This has a significant impact on the difficulty of the task.", |
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| "section": "Context Extraction", |
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| "text": "Experiments were also conducted whereby the contextual information was extracted from the WEB1T 3, 4 and 5-gram data for a list of known bigrams from the gold-standard thesauri. This data lacks the syntactic information provided by SEX-TANT but the counts are estimated over 10,000 times as much data. This should reduce the sparseness problem for the bigram headwords.", |
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| "section": "Context Extraction", |
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| "text": "Following Curran (2004) , the extracted synonyms are compared directly against multiple goldstandard thesauri. We extend this evaluation to include multi-word headwords and synonyms. We randomly selected 300 unigram and 300 bigram headwords from the MAQCUARIE (Bernard, 1990), MOBY (Ward, 1996) , and ROGET'S (1911) thesauri, and WORDNET (Fellbaum, 1998) .", |
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| "text": "(Ward, 1996)", |
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| "start": 301, |
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| "text": "ROGET'S (1911) thesauri, and", |
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| { |
| "start": 330, |
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| "text": "WORDNET (Fellbaum, 1998)", |
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| "section": "Synonym Extraction", |
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| "text": "We calculated the number of direct matches against the gold standard (DIRECT) and the inverse rank (INVR), the sum of the reciprocal ranks of matches. The results for the unigram headword experiments are summarised in Table 1. Both INVR and DIRECT demonstrate that performance decreases when MWEs are included. However, performance did increase significantly for some terms when MWEs were added. For example, tool improved from 0.270 to 0.568 INVR. The results for rate, shown in Table 2 , also improved.", |
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| "start": 218, |
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| "text": "Table 1.", |
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| "text": "Table 2", |
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| "section": "Synonym Extraction", |
| "sec_num": "5" |
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| "text": "The next set of experiments extracted synonyms for 300 bigram headwords drawn from the MAC-QUARIE thesaurus. The best results for bigram headwords was achieved when unigram and bigram data (Baldwin and Villavicencio, 2002) was included. Table 3 shows the top 5 synonyms (as ranked by the Jaccard measure) for atomic bomb and dining table.", |
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| "text": "(Baldwin and Villavicencio, 2002)", |
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| "text": "We have integrated the identification of simple multi-word expressions (MWEs) with a state-of-theart distributional similarity system. We evaluated extracted synonyms for both unigram and bigram headwords against a gold standard consisting of the union of multiple thesauri. The main difficulties are the sparsity of distributional evidence for MWEs and their low coverage in the gold standard. These preliminary experiments show the potential of distributional similarity for extracting lexical-semantic resources for both unigrams and MWEs.", |
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| "section": "Conclusion", |
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| "text": "", |
| "html": null, |
| "num": null, |
| "content": "<table><tr><td colspan=\"3\">: Results for unigram headwords</td></tr><tr><td>UNI</td><td>UNI+ BI</td><td>UNI+ BI+ VPC</td></tr><tr><td>level</td><td>level</td><td>level</td></tr><tr><td>price</td><td>price</td><td>price</td></tr><tr><td>cost</td><td colspan=\"2\">amount cost</td></tr><tr><td colspan=\"2\">income cost</td><td>amount</td></tr><tr><td>growth</td><td>speed</td><td>average</td></tr></table>" |
| }, |
| "TABREF2": { |
| "type_str": "table", |
| "text": "Sample synonyms for rate", |
| "html": null, |
| "num": null, |
| "content": "<table><tr><td>ATOMIC BOMB</td><td>DINING TABLE</td></tr><tr><td>nuclear bomb</td><td>coffee table</td></tr><tr><td>atom bomb</td><td>dining room</td></tr><tr><td colspan=\"2\">nuclear explosion cocktail table</td></tr><tr><td>atomic explosion</td><td>dining chair</td></tr><tr><td>nuclear weapon</td><td>bedroom furniture</td></tr></table>" |
| }, |
| "TABREF3": { |
| "type_str": "table", |
| "text": "Sample bigram synonyms was extracted from WEB1T and the VPC resource", |
| "html": null, |
| "num": null, |
| "content": "<table/>" |
| } |
| } |
| } |
| } |
