| { | |
| "paper_id": "O14-1019", | |
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| "date_generated": "2023-01-19T08:04:13.815583Z" | |
| }, | |
| "title": "Testing Distributional Hypothesis in Patent Translation", | |
| "authors": [ | |
| { | |
| "first": "Hsin-Hung", | |
| "middle": [], | |
| "last": "Lin", | |
| "suffix": "", | |
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| "email": "" | |
| }, | |
| { | |
| "first": "Yves", | |
| "middle": [], | |
| "last": "Lepage", | |
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| "abstract": "This paper presents a wordlist-based lexical richness approach to testing distributional hypothesis for genre analysis in translation studies. In recent years, there has been continuing interest in patent translation. However, there are only a few lay their interests on comparison between native and non-native writing. The proposed approach to terms distrubution of technical words contained in United States Patent and Trademark Office (USPTO) and Japan Patent Office (JPO) in terms of lexical variation, lexical density and lexical sophistication, in brief, highlights distributional similarity of technical genre, and in particular, distibutional difference of academic and general genres.", | |
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| "abstract": [ | |
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| "text": "This paper presents a wordlist-based lexical richness approach to testing distributional hypothesis for genre analysis in translation studies. In recent years, there has been continuing interest in patent translation. However, there are only a few lay their interests on comparison between native and non-native writing. The proposed approach to terms distrubution of technical words contained in United States Patent and Trademark Office (USPTO) and Japan Patent Office (JPO) in terms of lexical variation, lexical density and lexical sophistication, in brief, highlights distributional similarity of technical genre, and in particular, distibutional difference of academic and general genres.", | |
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| "section": "Abstract", | |
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| "body_text": [ | |
| { | |
| "text": "As globalization has resulted in rapid greater economic growth, the challenges of interdisciplinary interaction in pursuit of precise patent writing have incredibly increased.", | |
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| "section": "Introduction", | |
| "sec_num": "1." | |
| }, | |
| { | |
| "text": "In Lin and Hsieh (2010a) , English patent documents were statistically extracted and computationally examined from LexisNexis Academic, a database for legal professionals. They compiled a reference corpus of independent claim texts and lay the focus on their collocation features. Mutual information is attainable with the help of selectional collocation features underlining specific clausal types represented in natural language processing of patent specification.", | |
| "cite_spans": [ | |
| { | |
| "start": 3, | |
| "end": 24, | |
| "text": "Lin and Hsieh (2010a)", | |
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| "section": "Introduction", | |
| "sec_num": "1." | |
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| "text": "While their work appears to fill a niche in the ESP (English for Specific Purposes) field (and particularly in the English for Occupational Legal Purposes), Lin and Hsieh (2010b) further compiled a modern patent language technical term list with statistical-retrieval methodologies as a mandatory \uf02a Graduate School of Information, Production, and Systems, Waseda University, Japan. E-mail: nobuhiro602@toki.waseda.jp; yves.lepage@waseda.jp approach. The research content and statistical investigations assist patent attorneys expand the vocabulary size for the advancement of patent writing at an international level. Lin and Hsieh (2011) proposed a mixed-method approach to detecting scholarly discourse in patent technical documents. The Patent Technical Word Corpus (hereafter PTWC), containing 16 million word tokens, was compiled to elucidate the underpinning principles in identifying discourse elements, text-structure components, and the location of references. Whereas most existing IPR (intellectual property rights) databases accessible for information retrieval, the creation of PTWC, based on corpus-statistics and text-processing technology, refines more decisive characteristics of terminological knowledge as potential contribution for evaluation of technical documents.", | |
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| { | |
| "start": 157, | |
| "end": 178, | |
| "text": "Lin and Hsieh (2010b)", | |
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| { | |
| "start": 618, | |
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| "text": "Lin and Hsieh (2011)", | |
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| "section": "Introduction", | |
| "sec_num": "1." | |
| }, | |
| { | |
| "text": "To characterize technical genre in translation studies, we use lexical richness based on technical wordlist to test distributional hypothesis.", | |
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| "section": "Introduction", | |
| "sec_num": "1." | |
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| "text": "We firstly conduct a quantitative survey based on USPTO Glossary to rank the distribution of technical terms used in United States Patent and Trademark Office (USPTO) and Japan Patent Office (JPO) within the time period from year 2010 to 2013. Table 1 below presents the statistical results. 'Comprising', a term of art used in claim language which means that the named elements are essential in describing the invention, ranked the first in USPTO. According to USPTO Glossary, it is a transitional phrase that is synonymous with \"including,\" \"containing\" or \"characterized by;\" is inclusive or open-ended and does not exclude additional, unrecited elements or method steps. On the contrary, 'consisting of', a transitional phrase that is closed and excludes any element, step, or ingredient not specified in the claim, ranked the 6 th .", | |
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| "section": "Technical Terms Distribution", | |
| "sec_num": "2." | |
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| { | |
| "text": "To characterize transitional phrases of technical genre in translation studies, we retrieved co-occurring information of 'comprising' and 'consisting of' to compare it with academic and general genres. We give the survey of terms used in JPO in Table 2 . It is noted that \"comprising\" ranked the first in distribution of USPTO and JPO, whereas \"consisting of\" ranked the 6 th . Sahlgren (2008:33) maintains that distributional approaches to meaning acquisition utilize distributional properties of linguistic entities as the building blocks of semantics. This hypothesis is often stated in terms like words which are similar in meaning occur in similar contexts (Rubenstein & Goodenough, 1965) . In other words, words that occur in the same contexts tend to have similar meanings (Pantel, 2005) .", | |
| "cite_spans": [ | |
| { | |
| "start": 378, | |
| "end": 396, | |
| "text": "Sahlgren (2008:33)", | |
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| }, | |
| { | |
| "start": 662, | |
| "end": 693, | |
| "text": "(Rubenstein & Goodenough, 1965)", | |
| "ref_id": "BIBREF8" | |
| }, | |
| { | |
| "start": 780, | |
| "end": 794, | |
| "text": "(Pantel, 2005)", | |
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| "start": 245, | |
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| "text": "Table 2", | |
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| "section": "Technical Terms Distribution", | |
| "sec_num": "2." | |
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| { | |
| "text": "Transitional phrases in patent application were used to specify whether the claim is limited to only the elements listed, or whether the claim may cover items or processes that have additional elements. The most common transitional phrase used is the open-ended phrase \"comprising\". However, many claims use closed-ended language such as \"consisting of\".", | |
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| "section": "Corpus Preparation", | |
| "sec_num": "3.2" | |
| }, | |
| { | |
| "text": "In this regards, we retrieve co-occurring information containing \"comprising\" and \"consisting of\" from LexisNexis Academic for corpus preparation. Table 3 shows the structure for the corpus creation.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 147, | |
| "end": 154, | |
| "text": "Table 3", | |
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| "section": "Corpus Preparation", | |
| "sec_num": "3.2" | |
| }, | |
| { | |
| "text": "Genres Native Writing Non-Native Writing Technical (Patent) USPTO JPO Academic (Law Journal) Canadian Legal Journals HK Law Journal General (Newspapers) US Newspapers Non-US Newspapers", | |
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| "ref_spans": [], | |
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| "section": "Table 3. Genre-based co-occurrence corpus of transitional phrases", | |
| "sec_num": null | |
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| { | |
| "text": "Lexical richness is a concept about one's lexical uses, which can be measured by lexical density, sophistication, and variation (Kao and Wang, 2014:54) . Kojima and Yamashita (2014:23) suggest that lexical richness measures primarily assess learners' vocabulary use. Lexical variation, the proportion between different words (types) and the total number (tokens) of words used in the text, is known as the type-token ratio (TTR).", | |
| "cite_spans": [ | |
| { | |
| "start": 128, | |
| "end": 151, | |
| "text": "(Kao and Wang, 2014:54)", | |
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| "start": 154, | |
| "end": 184, | |
| "text": "Kojima and Yamashita (2014:23)", | |
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| "section": "Lexical Richness", | |
| "sec_num": "3.3" | |
| }, | |
| { | |
| "text": "Lexical density is defined as the percentage of lexical words in the text, for example, nouns, verbs, adjectives, and adverbs (Laufer and Nation, 1995:309) . Since only content words carry semantic meanings, a greater lexical density indicates more semantic information conveyed in a text. Read (2000: 200) distinguishes dimensions of lexical richness, and one of these is lexical sophistication, which he defines as 'the use of technical terms and jargon as well as the kind of uncommon words that allow writers to express their meanings in a precise and sophisticated manner'. The proportion of words used at different frequency levels, in terms of K1, K2, AWL (Academic Word List), and off-list words, in the text. K1 and K2 words are the most commonly used first 1000 and 1001 to 2000 words, respectively, in English. Words beyond these K1, K2, and AWL are placed into the off-list level, where proper nouns, rare words, special terms, acronyms, abbreviations, incompletions, and even misspellings may be found.", | |
| "cite_spans": [ | |
| { | |
| "start": 126, | |
| "end": 155, | |
| "text": "(Laufer and Nation, 1995:309)", | |
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| "start": 290, | |
| "end": 306, | |
| "text": "Read (2000: 200)", | |
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| "section": "Lexical Richness", | |
| "sec_num": "3.3" | |
| }, | |
| { | |
| "text": "In terms of lexical density, non-natives employed more semantic information than the natives, among all genres. In terms of lexical variation, non-natives employed more lexical diversity than the natives in technical and academic genres.", | |
| "cite_spans": [], | |
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| "section": "Results and Discussion", | |
| "sec_num": "4." | |
| }, | |
| { | |
| "text": "Academic Genre, in particular, HK Law Journal, containing most semantic information (83%), among the all, whilst general genre, Non-Us Newspapers, containing least lexical diversity, as we excluded technical genre for analysis.", | |
| "cite_spans": [], | |
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| "section": "Results and Discussion", | |
| "sec_num": "4." | |
| }, | |
| { | |
| "text": "In technical genre, in particular, JPO (Patent Abstract of Japan), containing least advanced words (15.03%) in the texts, among all. Less vocabulary knowledge in K2, AWL, and Off-list words were employed in \"consisting of\", compared with that of \"comprising\". The natives employed more academic words in Table 4 , more off-list words in Table 5 . As shown in Table 6 and Table 7 , non-natives employed more off-list words in academic legal genre, whereas the natives employed more K1 and K2 words in academic legal genre. ", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 304, | |
| "end": 311, | |
| "text": "Table 4", | |
| "ref_id": "TABREF2" | |
| }, | |
| { | |
| "start": 337, | |
| "end": 344, | |
| "text": "Table 5", | |
| "ref_id": "TABREF3" | |
| }, | |
| { | |
| "start": 359, | |
| "end": 366, | |
| "text": "Table 6", | |
| "ref_id": "TABREF4" | |
| }, | |
| { | |
| "start": 371, | |
| "end": 378, | |
| "text": "Table 7", | |
| "ref_id": "TABREF5" | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Technical Genre", | |
| "sec_num": "4.1" | |
| }, | |
| { | |
| "text": "As can be seen in Table 8 and Table 9 , the non-natives employed more K2, AWL, and Off-list words but less K1 words in general genre. In short, K1 words were employed more by the natives in academic and general genres, whilst less used in technical genres.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 18, | |
| "end": 37, | |
| "text": "Table 8 and Table 9", | |
| "ref_id": "TABREF6" | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "General Genre", | |
| "sec_num": "4.3" | |
| }, | |
| { | |
| "text": "There is a correlation between distributional similarity and meaning similarity, which allows us to utilize the former in order to estimate the latter (Sahlgren, 2008:33) . In terms of distribution statistics, the technical genre reveals more distributional and meaning similarity.", | |
| "cite_spans": [ | |
| { | |
| "start": 151, | |
| "end": 170, | |
| "text": "(Sahlgren, 2008:33)", | |
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| "section": "Conclusion and Future Work", | |
| "sec_num": "5." | |
| }, | |
| { | |
| "text": "In summary, lexical richness is a valid and reliable measure to characterize genres. For future research, we seek to investigate the origin differences between syntagmatic and paradigmatic relations to further refine the preliminaries of the present study.", | |
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| "section": "Conclusion and Future Work", | |
| "sec_num": "5." | |
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| "pages": "33--53", | |
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| "raw_text": "Sahlgren, M. (2008). The distributional hypothesis. Italian Journal of Linguistics, 20(1), 33-53.", | |
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| "content": "<table><tr><td>Rank</td><td>Term</td><td colspan=\"2\">Frequency Rank</td><td>Term</td><td>Frequency</td></tr><tr><td>1</td><td>comprising</td><td>3785213</td><td>11</td><td>specification</td><td>854667</td></tr><tr><td>2</td><td>scope</td><td>2459656</td><td>12</td><td>continuation</td><td>738785</td></tr><tr><td>3</td><td>patent</td><td>1603882</td><td>13</td><td>dependent claim</td><td>625886</td></tr><tr><td>4</td><td>Group</td><td>1306808</td><td>14</td><td>composed of</td><td>617353</td></tr><tr><td>5</td><td>element</td><td>1245265</td><td>15</td><td>independent claim</td><td>587926</td></tr><tr><td>6</td><td>consisting of</td><td>1165427</td><td>16</td><td>representative</td><td>518762</td></tr><tr><td>7</td><td>drawing</td><td>1015261</td><td>17</td><td>benefit claim</td><td>437599</td></tr><tr><td>8</td><td>disclosure</td><td>919881</td><td>18</td><td>person</td><td>383784</td></tr><tr><td>9</td><td>application (patent)</td><td>884470</td><td>19</td><td>priority claim</td><td>381352</td></tr><tr><td>10</td><td>patent application</td><td>884470</td><td>20</td><td>interference</td><td>341173</td></tr></table>", | |
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| "content": "<table><tr><td>Rank</td><td>Term</td><td colspan=\"2\">Frequency Rank</td><td>Term</td><td>Frequency</td></tr><tr><td>1</td><td>comprising</td><td>629750</td><td>11</td><td>applicant</td><td>60293</td></tr><tr><td>2</td><td>composed of</td><td>371852</td><td>12</td><td>drawing</td><td>53469</td></tr><tr><td>3</td><td>element</td><td>272496</td><td>13</td><td>person</td><td>48893</td></tr><tr><td>4</td><td>POWER</td><td>272088</td><td>14</td><td>IDS</td><td>24946</td></tr><tr><td>5</td><td>Group</td><td>176103</td><td>15</td><td>Control No.</td><td>22905</td></tr><tr><td>6</td><td>consisting of</td><td>136992</td><td>16</td><td>interference</td><td>22445</td></tr><tr><td>7</td><td>PAIR</td><td>122746</td><td>17</td><td>RE</td><td>19777</td></tr><tr><td>8</td><td>representative</td><td>72519</td><td>18</td><td>specification</td><td>18102</td></tr><tr><td>9</td><td>Request (PCT)</td><td>70606</td><td>19</td><td>classification</td><td>16977</td></tr><tr><td>10</td><td>application (patent)</td><td>62027</td><td>20</td><td>independent claim</td><td>15513</td></tr></table>", | |
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| "TABREF2": { | |
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| "content": "<table><tr><td>Word Level (%)</td><td>USPTO</td><td>JPO</td></tr><tr><td>K1 Words</td><td>50.35</td><td>50.37</td></tr><tr><td>K2 Words</td><td>2.61</td><td>3.61</td></tr><tr><td>AWL Words</td><td>23.74</td><td>21.78</td></tr><tr><td>Off-List Words</td><td>23.31</td><td>24.24</td></tr></table>", | |
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| "TABREF3": { | |
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| "content": "<table><tr><td>Word Level (%)</td><td>USPTO</td><td>JPO</td></tr><tr><td>K1 Words</td><td>62.37</td><td>64.89</td></tr><tr><td>K2 Words</td><td>0.29</td><td>0.34</td></tr><tr><td>AWL Words</td><td>19.43</td><td>19.74</td></tr><tr><td>Off-List Words</td><td>17.92</td><td>15.03</td></tr><tr><td>4.2 Academic Genre</td><td/><td/></tr><tr><td colspan=\"3\">In academic genre, HK Law Journal, containing most advanced words (33.28%)</td></tr><tr><td>in the texts, among all.</td><td/><td/></tr></table>", | |
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| "content": "<table><tr><td colspan=\"3\">. Lexical sophistication of \"comprising\" in academic genre</td></tr><tr><td>Word Level (%)</td><td>Canadian Legal</td><td>HK Law Journal</td></tr><tr><td/><td>Journal</td><td/></tr><tr><td>K1 Words</td><td>59.02</td><td>51.02</td></tr><tr><td>K2 Words</td><td>5.88</td><td>3.07</td></tr><tr><td>AWL Words</td><td>13.47</td><td>12.63</td></tr><tr><td>Off-List Words</td><td>21.63</td><td>33.28</td></tr></table>", | |
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| "TABREF5": { | |
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| "content": "<table><tr><td>Word Level (%)</td><td>Canadian Legal</td><td>HK Law Journal</td></tr><tr><td/><td>Journal</td><td/></tr><tr><td>K1 Words</td><td>59.74</td><td>50.74</td></tr><tr><td>K2 Words</td><td>6.12</td><td>2.97</td></tr><tr><td>AWL Words</td><td>13.07</td><td>14.24</td></tr><tr><td>Off-List Words</td><td>21.07</td><td>32.05</td></tr></table>", | |
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| "content": "<table><tr><td colspan=\"3\">. Lexical sophistication of \"comprising\" in general genre</td></tr><tr><td>Word Level (%)</td><td>US Newspapers</td><td>Non-US Newspapers</td></tr><tr><td>K1 Words</td><td>63.34</td><td>51.97</td></tr><tr><td>K2 Words</td><td>3.69</td><td>5.47</td></tr><tr><td>AWL Words</td><td>11.66</td><td>16.73</td></tr><tr><td>Off-List Words</td><td>21.30</td><td>25.84</td></tr><tr><td colspan=\"3\">Table 9. Lexical sophistication of \"consisting of\" in general genre</td></tr><tr><td>Word Level (%)</td><td>US Newspapers</td><td>Non-US Newspapers</td></tr><tr><td>K1 Words</td><td>63.37</td><td>53.48</td></tr><tr><td>K2 Words</td><td>4.03</td><td>7.71</td></tr><tr><td>AWL Words</td><td>12.61</td><td>16.42</td></tr><tr><td>Off-List Words</td><td>19.99</td><td>22.09</td></tr></table>", | |
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