Abstract ",
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"text": "We further notice that it is difficult to align sentences 0002. The sentence in the Chinese page is much longer than its counterpart in the English page because some additional information (font) is added. The length-based method thus tends to take sentence 0002, 0003, and 0004 in the English page as the translation of sentence 0002 in the Chinese page (Fig. 2) , which is wrong. This in turn provocated the three following incorrect alignments. As we can see in Fig. 3 , the cognate method did not make the same mistake because of the noise in sentence 0002. Despite their large length difference, the two 0002 sentences are still aligned as a 1-1 pair, because the sentences in the following 4 alignments (0003 -0003; 0004 -0004, 0005; 0005 -0006; 0006 -0007) have rather similar HTML markups and are taken by the program to be the most likely alignments.",
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"text": "Beside HTML markups, other criteria may also be incorporated. For example, it would be helpful to consider strong correspondence between certain English and Chinese words, as in (Wu, 1994) . We hope to implement such correspondences in our future research.",
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"text": "To evaluate the precision of the English-Chinese translation model trained on the Web corpus, we examined two sample lexicons of 200 words, one in each direction. The 200 words for each lexicon were randomly selected from the training source. We examined the most probable translation for each word. The Chinese-English lexicon was found to have a precision of 77%. The English-Chinese lexicon has a higher precision of 81.5%. Part of the lexicons are shown in Fig. 4 , where t/f indicates whether a translation is true or false.",
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"text": "These precisions seem to be reasonably high. They are quite comparable to that obtained by Wu (1994) using a manual Chinese-English parallel corpus.",
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"text": "We also found that stop-lists have significant effect on the translation model. Stop-list is a set of the most frequent words that we remove from the train- ing source. Because these words exist in most alignments, the statistical model cannot derive correct translations for them. More importantly, their existence greatly affects the accuracy of other translations. They can be taken as translations for many words. A priori, it would seem that both the English and Chinese stop-lists should be applied to eliminate the noise caused by them. Interestingly, from our observation and analysis we concluded that for better precision, only the stop-list of the target language should be applied in the model training.",
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"text": "We first explain why the stop-list of the target language has to be applied. On the left side of Fig. 5 , if the Chinese word C exists in the same alignments with the English word E more than any other Chinese words, C will be the most probable translation for E. Because of their frequent appearance, some Chinese stopwords may have more chances to be in the same alignments with E. The probability of the translation E --+ C is then reduced (maybe even less than those of the incorrect ones). This is the reason why many English words are translated to \"~' (of) by the translation model trained without using the Chinese stop-list.",
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"start": 97,
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"text": "Fig. 5",
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"text": "We also found that it is not necessary to remove the stopwords of the source language. In fact, as illustrated on the right side of Fig. 5 , the existence of the English stopwords has two effects on the probability of the translation E -~ C:",
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"start": 132,
"end": 138,
"text": "Fig. 5",
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"text": "1 They may often be found together with the Chinese word C. Owing to the Expectation Maximization algorithm, the probability of E -~ C may therefore be reduced.",
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"section": "Effect of Stopwords",
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"text": "2 On the other hand, there is a greater likelihood that English stopwords will be found together with the most frequent Chinese words. Here, we use the term \"Chinese frequent words\" instead of \"Chinese stopwords\" because even if a stop-list is applied, there may still remain some common words that have the same effect as the stopwords. The coexistence of English and Chinese frequent words reduces the probability that the Chinese frequent words are the translations of E, and thus raise the probability of E -+ C.",
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"text": "The second effect was found to be more significant than the first, since the model trained without the English stopwords has better precision than the model trained with the English stopwords. For the correct translations given by both models, the model English-Chinese CLIR Results",
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"section": "Effect of Stopwords",
"sec_num": null
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"text": "Our final goal was to test the performance of the translation models trained on the Web parallel corpora in CLIR. We conducted CLIR experiments using the Smart IR system.",
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"text": "The English test corpus (for C-E CLIR) was the AP corpus used in TREC6 and TREC7. The short English queries were translated manually into Chinese and then translated back to English by the translation model. The Chinese test corpus was the one used in the TREC5 and TREC6 Chinese track. It contains both Chinese queries and their English translations.",
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"section": "Results",
"sec_num": "4.1"
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"text": "Our experiments on these two corpora produced the results shown in Tab. 1. The precision of monolingual IR is given as benchmark. In both E-C and C-E CLIR, the translation model achieved around 40% of monolingual precision. To compare with the dictionary-based approach, we employed a Chinese-English dictionary, CEDICT (Denisowski, 1999) , and an English-Chinese online dictionary (Anonymous, 1999a) to translate queries. For each word of the source query, all the possible translations given by the dictionary are included in the translated query. The Chinese-English dictionary has about the same performace as the translation model, while the English-Chinese dictionary has lower precision than that of the translation model.",
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"start": 320,
"end": 338,
"text": "(Denisowski, 1999)",
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"section": "Results",
"sec_num": "4.1"
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"text": "We also tried to combine the translations given by the translation model and the dictionary. In both C-E and E-C CLIR, significant improvements were achieved (as shown in Tab. 1). The improvements show that the translations given by the translation model and the dictionary complement each other well for IR purposes. The translation model may give either exact translations or incorrect but related words. Even though these words are not correct in the sense of translation, they are very possibly related to the subject of the query and thus helpful for IR purposes. The dictionary-based approach expands a query along another dimension. It gives all the possible translations for each word including those that are missed by the translation model.",
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"section": "Results",
"sec_num": "4.1"
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{
"text": "One advantage of a parallel text-based translation model is that it is easier to build than an MT system. Now that we have examined the CLIR performance of the translation model, we will compare it with two existing MT systems. Both systems were tested in E-C CLIR.",
"cite_spans": [],
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"section": "Comparison With MT Systems",
"sec_num": "4.2"
},
{
"text": "Using the Sunshine WebTran server (Anonymous, 1999b) , an online Engiish-Chinese MT system, to translate the 54 English queries, we obtained an average precision of 0.2001, which is 50.3% of the mono-lingual precision. The precision is higher than that obtained using the translation model (0.1804) or the dictionary (0.1427) alone, but lower than the precison obtained using them together (0.2232). Kwok (1999) investigated the CLIR performance of an English-Chinese MT software called Transperfect, using the same TREC Chinese collection as we used in this study. Using the MT software alone, Kwok achieved 56% of monolingual precision. The precision is improved to 62% by refining the translation with a dictionary. Kwok also adopted pretranslation query expansion, which further improved the precison to 70% of the monolingual results.",
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"start": 34,
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"start": 400,
"end": 411,
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"section": "Sunshine WebTran Server",
"sec_num": "4.2.1"
},
{
"text": "In our case, the best E-C CLIR precison using the translation model (and dictionary) is 56.1%. It is lower than what Kwok achieved using Transperfect, however, the difference is not large.",
"cite_spans": [],
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"section": "Transperfect",
"sec_num": "4.2.2"
},
{
"text": "The Chinese-English translation model has a fax lower CLIR performance than that of the English-French model established using the same method (Nie et al., 1999) . The principal reason for this is the fact that English and Chinese are much more different than English and French. This problem surfaced in many phases of this work, from text alignment to query translation. Below, we list some further factors affecting CLIR precision.",
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{
"start": 143,
"end": 161,
"text": "(Nie et al., 1999)",
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}
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"section": "Further Problems",
"sec_num": "4.3"
},
{
"text": "\u2022 The Web-collected corpus is noisy and it is difficult to align English-Chinese texts. The alignment method we employed has performed more poorly than on English-French alignment. This in turn leads to poorer performance of the translation model. In general, we observe a higher variability in Chinese-English translations than in English-French translations.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Further Problems",
"sec_num": "4.3"
},
{
"text": "\u2022 For E-C CLIR, although queries in both languages were provided, the English queries were not strictly translated from the original Chinese ones. For example, AJg,~ (human right situation) was translated into human right issue. We cannot expect the translation model to translate issue back to ~ (situation). ",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Further Problems",
"sec_num": "4.3"
},
{
"text": "The goal of this work was to investigate the feasibility of using a statistical translation model trained on a Web-collected corpus to do English-Chinese CLIR.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Summary",
"sec_num": "5"
},
{
"text": "In this paper, we have described the algorithm and implementation we used for parallel text mining, translation model training, and some results we obtained in CLIR experiments. Although further work remains to be done, we can conclude that it is possible to automatically construct a Chinese-English parallel corpus from the Web. The current system can be easily adapted to other language pairs. Despite the noisy nature of the corpus and the great difference in the languages, the evaluation lexicons generated by the translation model produced acceptable precision. While the current CLIR results are not as encouraging as those of English-French CLIR, they could be improved in various ways, such as improving the alignment method by adapting cognate definitions to HTML markup, incorporating a lexicon and/or removing some common function words in translated queries. We hope to be able to demonstrate in the near future that a fine-tuned English-Chinese translation model can provide query translations for CLIR with the same quality produced by MT systems.",
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"section": "Summary",
"sec_num": "5"
},
{
"text": "1An anchor text is a piece of text on a Web page which, when clicked on, will take you to another linked page. To be helpful, it usually contains the key information about the linked page.",
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"FIGREF0": {
"type_str": "figure",
"text": "The workflow of the mining process.",
"uris": null,
"num": null
},
"FIGREF1": {
"type_str": "figure",
"text": "An alignment example using pure length-based method.",
"uris": null,
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"FIGREF2": {
"type_str": "figure",
"text": "An alignment example considering cognates.",
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"FIGREF3": {
"type_str": "figure",
"text": "Effect of stop lists in C-E translation.",
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"FIGREF4": {
"type_str": "figure",
"text": "The training source and the CLIR collections were from different domains. The Web corpus are retrieved from the parallel sites in Hong Kong while the Chinese collection is from People's Daily and Xinhua News Agency, which are published in mainland China. As the result, some important terms such as ~$ $ (mostfavored-nation) and ---I!! ~ ~ (one-nation-twosystems) in the collection are not known by the model.",
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"num": null
}
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}
}
Volume 6, No l&2, pp. 19-27 (May, 1996) divided into sentences. The sentences are marked by and . Note that we determine sentences not only by periods, but also by means of HTML markup.",
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{
"start": 133,
"end": 141,
"text": "(Fig. 2)",
"ref_id": "FIGREF1"
},
{
"start": 176,
"end": 184,
"text": "(Fig. 3)",
"ref_id": "FIGREF2"
}
],
"eq_spans": [],
"section": "Parallel Text Alignment",
"sec_num": "3.3"
},
{
"text": "