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Table 2: Characteristics of Hansards corpus. S semaine. Combining the source-target and target- parvenir. The eval- decision NULL uation of different possible sets are presented in table 3. Both reference corpora con-. Only the upper one is acceptable. For Verbmobil, the reference corpus contains only S links. The recall plays an important The databases have been selected to contain only record- role and the union is the best combination.

The reference ings in US-English and to focus on the appointment corpus for the Hansards task contains few S links and many scheduling domain. Then their counterparts in Catalan and P links. The intersection is the best combination because it Spanish have been generated by means of human trans- keeps fewer, more precise links. Dates and times were cat- Results with weighted links, as described in section 3. The test are presented in a research report Lambert and Castell, corpus consists of four hundred sentence pairs manually In most cases the effect of the weighting of the aligned by a single annotator.

See the characteristics of the links is simply to move up the scores. However for the data in table 1. Hansards corpus it produces a qualitative change: the inter- section gets a score worse than the union. Table 4 presents the evaluation of the symmetrisation Table 1: Characteristics of Verbmobil corpus process in these two cases. The symmetrisation increases the recall but introduces also some noise, so the precision 4.

Hansards Corpus is lower. However the outcome is a decrease of the error The corpus consists of the debates in the 36th Canadian rate from We used a version of the Hansards aligned by 9. The larger effect in the Ullrich Germann at the level of sentences or smaller frag- case of the Hansards could be due to the much greater size ments Germann, From the over 1. This allows a higher cov- allel text chunks, we selected those of 40 words or less. The erage but also permits to increase the threshold number of size of this corpus is much larger than that of Verbmobil and occurrences of an asymmetry, which implies a gain in pre- cision.

Conclusions Germann, Ullrich, Evalua- alignments can in turn improve those applications where tion and symmetrisation of alignments obtained aligned corpora are a valuable resource. Dan, Manual annotation of translational In this paper we also pointed out some critical issues equivalence. All of them stress the Mihalcea, Rada and Ted Pedersen, An evaluation ex- care with which evaluation results must be compared. In Rada Mihalcea and Ted Pedersen eds. Och, Franz Josef, References ing of statistical translation models.

Improved sta- translation. Borovets, Bulgary. Hongkong, China. Baum, L. A system- imization technique in statistical estimation for proba- atic comparison of various statistical alignment models. Inequalities, — Computational Linguistics, 29 1 — Della Pietra, Vincent J. Mercer, The mathematics lation. Copenhagen, Denmark. Computational Linguistics, 19 2 — The usability of bilingual corpus is evaluated by the lexical alignment score calculated for the bi-lexicon pair distributed in the aligned bilingual sentence pairs.

We compare and contrast a number of bilingual corpora, ranging from parallel, to comparable, and to non- parallel corpora. We compare different methods of mining parallel sentences and bilingual lexicon from bilingual corpora. These methods make several sentence-level assumptions on the bilingual corpora. We have found that some of them are applicable to bilingual parallel documents but non-applicable to non-parallel, comparable documents.

None of the sentence-level assumptions can be made about non- parallel and quasi-comparable corpora. The latter contain bilingual documents that may or may not be on the same topic. By postulating additional assumptions on comparable documents, we propose a completely unsupervised method to extract useful material, such as parallel sentences and bilexicons, from quasi-comparable corpora.

The lexical alignment score for the comparable sentences extracted with our unsupervised method is found to be very close to that of the parallel corpus. This shows that our extraction method is effective. Finally, by postulating additional Introduction assumptions on comparable documents, we propose a There is an explosively increasing amount of new content completely unsupervised method to extract useful being loaded to the Internet every day.

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The Hong Kong Laws Corpus is a parallel corpus with This requires the comparison of documents in different sentence level alignment; it is used as parallel sentence languages that are not translations of each other. There What is a comparable document? There is as yet no agreement on the Previous works have extracted bilingual word senses, nature of the similarity, because there are very lexicon and parallel sentence pairs from noisy parallel few examples of comparable corpora. This type of corpora is often called comparable corpora.

Corpora like the Hong Kong News Corpus, and The degree of comparability of different documents the Xinhua News Corpus are in fact rough translations of varies, but we believe that the more comparable the each other, focused on the same thematic topics, with corpora are, it is more useful for various NLP research some insertions and deletions of paragraphs. Sentence and task. It contains transcriptions of In this paper, we describe a method for quantifying the various news stories from radio broadcasting or TV news comparability of a bilingual corpus.

Then we compare report from in English and Chinese. In this different methods for mining parallel sentences and corpus, there are about 7, Chinese and 12, English bilingual lexicon, from bilingual corpora with different documents, covering 60 different topics. These methods are based on and 4, English documents are manually labeled as different assumptions about the characteristics of relevant to a topic and are in-topic.

The remaining bilingual corpora. We have found that some assumptions documents are labeled as off-topic since they are only for bilingual parallel documents are non-applicable to weakly relevant to a topic or irrelevant to all topics. A few 1. There are no missing translations in the target of the Chinese and English document are almost parallel document; document and contain some parallel sentences. Sentence lengths: a bilingual sentence pair are Nevertheless, the existence of considerable amount of off- similarly long in the two languages; topic document makes the whole corpus quasi- 3.

Sentence position: Sentences are assumed to comparable. The TDT 3 corpus also contains , correspond to those roughly at the same position Chinese, , English sentences, giving more than 30 in the other language. A very small portion of the 4. Bi-lexical context: A pair of bilingual sentences sentence pairs will turn out to be parallel, but many are which contain more words that are translations sentence pairs describing comparable content, with some of each other tend to be translations themselves.

The objective of our proposed method is to automatically For noisy parallel corpora without sentence delimiters, identify documents that are on the same topic, and then assumptions for bilingual word pairs are made as follows: extract parallel sentence pairs from these documents. Occurrence frequencies of bilingual word pairs Comparing Bilingual Corpora are similar 6. The positions of bilingual word pairs are similar We argue that the usability of bilingual corpus is 7.

Words have one sense per corpus determined by how well the sentences are aligned. Following 7, words have a single translation per postulate that if the sentence pairs in the corpus are indeed corpus translations of each other, then bilingual word pairs 9. Following 4, the contexts in two languages of a identified in the dictionary will co-occur frequently in this bilingual word pair are similar. Similarly, for quasi-comparable f We is the occurrence frequency of Chinese word Wc and corpora, we cannot rely on any other sentence level or English word We, in the respective language sentences set.

Both work use a translation- Kong News , and a non-parallel, quasi-comparable corpus model based alignment model trained from parallel TDT 3. The lexical alignment scores are computed from corpus and adaptively extract more parallel sentences and the extracted sentence pairs and shown in the following bilingual lexicon in the comparable corpus. There are table.

We can see that the scores are in direct proportion several differences between the two methods. Zhao and to the parallel-ness or comparability of the corpus. Vogel used a generative statistical machine translation alignment model, while Munteanu and Marcu used suffix trees. Table 1. They differ in the training and computation of document Comparing Alignment Methods similarity scores and sentence similarity scores. Examples All previous work on sentence alignment from parallel of document similarity computation include counting corpus makes use of one or multiple of the following word overlap and cosine similarity.

Sentence pairs above a set trained from parallel corpora, generative alignment threshold are considered parallel and extracted from the classifier. We propose a method to find parallel sentences and new Step 3: Update the Bilingual Lexicon word translations from unequal number of sentences in The occurrence of unknown words can adversely affect news stories in Chinese and English.

In our work, we use parallel sentence extraction by introducing erroneous simple cosine similarity measures and we dispense with word segmentations. Hence, we need to refine the bi- using parallel corpora to train an alignment classifier. In An Alignment Method for Quasi- this work, we focus on learning translations for name entities since these are the words most likely missing in comparable Corpora our baseline lexicon.

The Chinese name entities are In addition to the bi-lexical context assumption described extracted first Zhai et al. The algorithm then iterates to refine document passages that are found to contain at least one extraction and parallel sentence extraction. An alignment pair of parallel sentences are likely to contain score is computed in each iteration, which counts, on more parallel sentences.

The Based on these assumptions, we propose a first method alignment score is high when these sentence pairs are in extracting useful material from quasi-comparable really translations of each other. Similar to the iterative process in statistical word Evaluation alignment methods, we propose that while better document matching leads to better parallel sentence We have evaluated our algorithm on a comparable corpus extraction, better sentence matching leads to improved of TDT3 data. We use our method and a baseline method bilingual lexical extraction, the latter in turn improves the to extract parallel sentences from this corpus and document and sentence matches.

We propose a multi- manually examine the precision of these parallel level bootstrapping algorithm that iteratively improves the sentences. The baseline method shares the same preprocessing, document matching and sentence matching with our proposed method. However, it does not iterate to update Multi-level Bootstrapping the comparable document set, the parallel sentence set, or Step 1: Extract Comparable Documents the bilingual lexicon. For our document pairs that are similar in term distributions. In addition, we also found that the precision of parallel Then the Chinese documents are glossed with the same sentence pair extraction increases steadily over each dictionary.

When a Chinese word has multiple possible iteration in our method, until convergence. Both the glossed bootstrapping is in steps 3 and 4 and in the iterative Chinese document and English are represented in vector process. By using the correct alignment term weight. For quasi-comparable corpora, this document alignment step also serves as topic alignment.

Conclusion Step 2: Extract Parallel Sentences In this step, we extract parallel sentences from the We explore the usability of different bilingual corpora matched English and Chinese documents in the previous for the purpose of multilingual natural language section. Each sentence is again represented as word processing. We compare and contrast a number of vectors. A lexical Masao Utiyama and Hitoshi Isahara. Reliable measures alignment score calculated for the bi-lexicon pair for aligning Japanese-English news articles and sentences.

In distributed in the aligned bilingual sentence pairs then Proceedings of the 41st Annual Meeting of the Association evaluates the usability of each type of corpus.

Asian Cup, Qatar - Iraq: Bùng nổ cú đá phạt thần sầu định đoạt-Bóng đá 24h

Sapporo, Japan. We compared different alignment assumptions for Jean Veronis editor. Parallel Text Processing: mining parallel sentences from these different types of Alignment and Use of Translation Corpora. Dordrecht: bilingual corpora and proposed new assumptions for Kluwer.

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ISBN Aug Dekai Wu. In Robert Dale, Hermann Moisl, By postulating additional assumptions on seed parallel and Harold Somers editors , Handbook of Natural Language sentences of comparable documents, we propose a multi- Processing. New York: Marcel Dekker. ISBN 0- level bootstrapping algorithm to extract useful material, Jul Processing Comparable comparable corpora.

This shows that the proposed assumptions and and Dekai Wu. Using N-best list for Named Entity algorithm are promising for our objective. The lexical Recognition from Chinese Speec. In the Proceedings of the alignment score for the comparable sentences extracted NAACL , to appear with our unsupervised method is found to be very close to that of the parallel corpus.

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References Regina Barzilay and Noemie Elhadad, Pascale Fung and Kathleen Mckeown. Finding terminology translations from non-parallel corpora. Pages , Hong Kong, Aug. Gregory Grefenstette, editor. Kluwer Academic Publishers, Hiroyuki Kaji. Genichiro Kikui. Resolving translation ambiguity using non-parallel bilingual corpora.

Foundations of Statistical Natural Language Processing. The MIT Press. Kenji Matsumoto and Hideki Tanaka. Reinhard Rapp. Identifying word translations in non- parallel texts. Proceedings of the 33rd Meeting of the Association for Computational Linguistics. Cambridge, MA, This level of annotation, called a Parallel Proposition Bank, abstracts away from divergences in word order and syntactic categories to facilitate a mapping from a clausal structure in one language to the corresponding clausal structure in the other language.

It collects together split arguments, making it easier to find their foreign language counterparts. It also provides for a level of coarse-grained word sense disambiguation based primarily on differences in subcategorization frames that could simplify the task of lexical choice. Although there are still many language specific characteristics of the semantic annotation, it moves us one step closer to a general semantic representation that is language independent.

Introduction parts. The first is an annotated corpus wherein every verb and its arguments are explicitly marked. The corpus in Concurrent with the completion of the PropBank project question for English is the Wall Street Journal portions of at Penn Palmer et al submitted , Kingsbury and Palmer the Penn TreeBank II Marcus et al, , while for Chi- , the decision was made to extend the annotation nese the corpus is the Chinese TreeBank Xue et al, methodology both to independent corpora in other lan- Of more interest is the second part of the Prop- guages and to multilingual parallel corpora.

Arguments are as- PropBanking efforts have begun for Chinese Xue and signed a relatively theory-neutral numbered label and Palmer , Korean and one is planned for Arabic. A are assigned a verb-specific mnemonic label. Just as there is evidence that syntac- taining independent definitions of arguments. Senses are tic parses improve the accuracy of MT systems Yamada defined on both semantic and syntactic grounds. For ex- and Knight , Charniak etal. PropBanking further includes a degree of coarse-grained sense-tagging which could also facilitate 1.

These days Nissan can afford that strategy, even accurate translations. Last year the public was afforded a preview of Ms. More important is the pre-existence of argument- arg0: entity sustaining cost structure lexicons for each of the languages in question. A third component arg0: provider of the parallel propbanking endeavor is to explore the arg1: thing provided transferability between the languages at the level of arg2: recipient frameset.

It is hoped that this transferability can be ex- ploited in future Machine Translation systems. Framesets are also distinguished when the meanings of the usages are sufficiently different, even if the number of The Propbank roles is the same. Travelers Corp. The frameset in 6 roughly means "pass by voting" while the frameset illustrated by stem. Congress recently passed the inter-state arg1: thing no longer flowing banking law. The English TreeBank contains 7. One issue is basis for the English PropBank annotation also exist in the disambiguation of preverbal prepositional phrases.

Such a simple solution the same role in a and b , even though it occurs in dif- does not exist for Chinese. Instead, this is handled as part ferent syntactic positions. This regularity is captured by of the PropBanking effort by marking verb-dependent assigning the same argument label ARG1 to both in- PPs, such as that of 8a , as a semantic argument of the stances. Like the Eng- label relative to the verb. The tag set for Chinese 8.

Đầm cho người mập, Thời trang cho người mập, for fatty, fashion for fatty

Here the possessor and possessee are abstract notions and Second, the PropBank annotation also abstracts away do not necessarily indicate a strict possession relation. Split arguments may occur in different places and with different predicates in the two languages, but the 9. In many cases the framesets of a verb in ping can take place. If these mappings can be recovered one language map to different lexical items in another.

Although the extent to which such different set of arguments. They are mapped to different mapping can be performed in a straightforward manner is lexical items in Chinese: yet to be determined, a preliminary examination shows that the PropBank annotations would facilitate such a leave.

First, the PropBank repre- Arg0: entity leaving sentation abstracts away from divergences in the word or- Arg1: place left der and the syntactic category of the two languages and Arg2: attribute of Arg1 allows for a straightforward mapping at the predicate-ar- gument structure level. This is illustrated in 10 and This flight leaves Shanghai at midnight.

John left Mary a big fortune. New Orleans. Conclusion Kingsbury, P. It abstracts away from surface idiosyncrasies The Penn Treebank: Annotating predicate argu- such as word order, syntactic category and split constitu- ment structure. The expectation is that this level of annotation, in ogy Workshop, pp , Plainsboro NJ. Exploiting sophisticated monolingual information processing tools, Parallel Texts for Word Sense Disambiguation: An will also prove useful to various kinds of machine transla- Empirical Study. In the Proceedings of the 41st Annual tion systems.

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Transfer-based machine translation Meeting of the Association for Computational Linguis- approaches could benefit from corpus-based transfer lexi- tics ACL Statis- Palmer, M. Xue, N. Yamada, K.

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A Syntax-based Statisti- cal Translation Model. To enhance their exploitation by human users, specially designed interfaces need to be developed. We will describe the main functions of the interface, which provides two distinct browsing modalities: a bi-text-oriented modality and a word-oriented modality, which amounts to a bilingual semantic concordancer.

Both the word alignment and the annotation transfer are 1 Introduction carried out automatically. In the last years, the importance of parallel and The main hypothesis underlying this methodology is comparable corpora has become more and more evident that, given a text and its translation into another language, within the human language technology field, where these the translation preserves to a large extent the meaning of resources are used for the extraction of multilingual the source language text. The automatic projection of annotations knowledge structuring.

More resources such as on-line dictionaries. It has been texts aligned at sentence level with their corresponding obtained starting from SemCor, an English corpus Italian translations. The total amount of running semantically tagged with WordNet senses. The The rest of the paper is organized as follows. In word alignment and transfer methodology has been Section 2 we summarise the methodology developed for applied to 29 texts out of the texts available. These 29 the creation of the MultiSemCor corpus and its texts are aligned at word level and annotated with PoS, composition up to now.

In Section 3 we describe in detail lemma, and word sense. As regards English, we have the MultiSemCor Web interface, its main browsing 55, running words and 29, words semantically functionalities and novel characteristics. In Section 4 we annotated from SemCor. As for Italian, the corpus is outline some existing related work before concluding in composed of 59, running words among which 23, Section 5. As a matter of fact, out of the 23, subset of the English Brown corpus containing almost Italian words automatically sense-tagged, 5, are not , running words.

The strategy for creating MultiSemCor teaching and learning, translation studies, lexicography, consists in having SemCor texts translated into Italian by multilingual information browsing. MultiSemCor, a Web-based browser has been realized. In Alignments could be shown for instance by marking its design we faced a number of interesting issues, such as aligned words with various colours, a colour for each making available to the users information about corpus alignment, or by putting the two sentences in a two annotation, bilingual text alignment, bilingual semantic column table, where each row contains a word alignment.

To meet all these requirements, two distinct visually awkward, and for long sentences it makes the browsing modalities have been implemented. The first is correspondence between words hard to trace. The latter text-oriented and the second is word-oriented. Each of solution makes the correspondence between words easier these two modalities is embodied in a dynamic Web page. To solve the user can access the following information: problem we choose to show only one word alignment per time, by highlighting the aligned words in the source and A.

Note that along with the word alignment, B. In fact there are two such web-page organized in three sections corresponding to the lists, one for English-to-Italian, and one for Italian-to- three kinds of information above, see Figure 1. Section A English correspondences. Each token is hyperlinked with contains the whole bi-text and shows the alignment at the sentence in which the token occurs. This has been realized through a simple In the example in Figure 1, the user is browsing the two column table, where each column contains the text in text br-c02 in Section A of the interface.

By clicking on one of the two languages, and each row shows the the word character contained in sentence nr. This gets two results. Section B highlights the alignment solution shows the alignment between sentences, while between the word character in the English sentence, and keeping the possibility for the user to read the entire two carattere in the Italian translation. On the other hand, the texts in a natural way. Note that the user can now sentence and shows the available alignments at word level ask for the interface to show the passages in which the for that sentence.

Showing word level alignments through other translations of character are to be found. The second modality for browsing the corpus is word- oriented, and amounts to a bilingual semantic 3. More interface is that it allows for the integration between the precisely, in the MultiWordNet concordancer the user can semantically annotated corpus and its reference lexicon, alternatively search for all the occurrences of a word form, i.

The This integration has a twofold effect. On the one side, user can also constrain the search to a certain PoS. Free while browsing the MultiSemCor word senses the user combinations between all these constraints language, can consult MultiWordNet for a better understanding of word form, lemma, word sense, PoS are allowed. For the semantic annotation. On the other side, while instance the user can search for all the occurrences of: the browsing MultiWordNet the user can get examples of word form characters; or the word form character as usage of a certain word sense from MultiSemCor.

To our knowledge, MultiSemCor is the first interface to a verb; or the lemma character in all of its senses; or the multilingual corpus integrated with an on-line lexical lemma character in its third sense according to resource. The same form used in Figure 2 to ask for a semantic The system will return a KWIC-like concordance of all concordance, can be exploited to access the the tokens in the corpus that match the request, within the MultiWordNet lexical information related to a word form sentence in which they occur; each sentence is presented or lemma.

Figure 3 and the WordNet sense are also reported, as shown in shows the result of searching lexical information about the Figure 2. An hyperlink connects each semantic lemma character in the standard MultWordNet interface. MultiSemCor semantic concordancer on the specific sense In Figure 2, the user has asked for the semantic which is in the focus of the interface. Three From an implementation point of view, the aligned sentence in which the lemma occurs can be seen MultiSemCor browser has been developed in PHP.

The in the picture. The most important institutions, such corpus, annotated at lexical level. Also some two main modalities, addressing the needs of users with parallel concordancers have been made available to the different background. Moreover, it allows for the community. Opportunistic Barcelona. This is Semantic Tagging. In Proceedings of the Third the only available interface giving access to word-level International Conference on Language Resources and alignment. Other on-line interfaces allow for the browsing Evaluation pp.

Las Palmas, Canary of sentence-level alignment, and for a token-based search Islands — Spain, May , Wordnet: An Electronic Lexical Database. Pisa, Italy, Compara, by the Linguateca group: September Other projects made available only an on-line sample. In our approach, the examples used for translation are annotated under the representation schema of Translation Corresponding Tree TCT. Each Translation Corresponding Tree describes a translation example a pair of bilingual sentences.

It represents the syntactic structure of source language sentence i. Portuguese in our system , as well as denotes the translation correspondences i. Chinese translation for each node in the representation tree. In addition, syntax transformation rules are also encapsulated at each node in the TCT representation that captures the differentiation of grammatical structure between the source and target languages.

With this annotation schema, translation examples are effectively represented and organized in the bilingual knowledge database. In the translation process, the source sentence is parsed. By referring to the translation information coded in the TCTs, target language translation is synthesized. Translation Corresponding Tree TCT as the basic Introduction structure to annotate the examples in our bilingual The construction of bilingual knowledge base, in the knowledge base for the Portuguese to Chinese example- development of example-based machine translation based machine translation system.

In the translation process, the application of bilingual examples Translation Corresponding Tree concerns with how examples are used to facilitate Representation translation, which involves the factorization of an input Translation Corresponding Tree structure, as an extension sentence into the format of stored examples and the of structure string-tree correspondence representation conversion of source texts into target texts in terms of the Boitet and Zaharin, , is a general structure that can existing translations by referencing to the bilingual flexibly associate not only the string of a sentence to its knowledge base.

Theoretically speaking, examples can be syntactic structure in source language, but also allow the achieved from bilingual corpus where the texts are aligned language annotator to explicitly associate the string from in sentential level, and technically, we need an example its translation in target language for the purpose to base for convenient storage and retrieval of examples. The describe the correspondences between different languages. In TCT structure, the Matsumoto et al. All of these approaches substring of source sentence encoded by the interval annotate examples by mean of a pair of analyzed SNODE n , which denotes the interval containing the structures, one for each language sentence, where the substring corresponding to the node, 2 one between the correspondences between inter levels of source and target subtree and the substring of source sentence represented structures are explicitly linked.

For example, in Wu, , the translation examples containing the substring in target sentence corresponding used for building the translation alignments are strictly to the subtree of source sentence. The associated selected based on constraints. As a result, these substrings may be discontinuous in all cases. In this paper, we overcome the problem by phenomena for a language Boitet and Zaharin, ; Al- designing a flexible representation schema, called Adhaileh et al.

This is can be flexibly extended to keep various kinds of actually the idea behind the formalism of Translation linguistic information, if they are considered useful for Corresponding Tree. In many phrasal matching approaches, such as Figure 1: An TCT representation for annotating the constituency-oriented Kaji et al.

Such annotated in a TCT structure. While the line is used to represent the inversion of translation translation correspondence between the subtree of source fragments of its immediate subtrees. Here, to structure for representing translation examples in the facilitate such examples representation, we use the bilingual knowledge base, the translation units between Translation Corresponding Tree as the basic annotation the Portuguese sentence and its target translation in structure.

For instance, from the translation terms of an TCT structure. For automatically process and generate a preliminary TCT phrasal translation, we may visit the higher level representation structure for it. The resultant annotation constituents in the representing structure of TCT and tree is then further edited by human through the use of an apply the similar coding information to retrieve the TCT editing program if any amendment to the corresponding translation for the unit that representing a representation structure is necessary.

Portuguese sentence is then used for establishing the correspondences between the surface substrings and the Example-Based Translation Based on TCT inter levels of its structure, which includes the In example-based machine translation systems, a corpus correspondences between nodes and its substrings, as well of translation examples used to facilitate the translation as the correspondences between subtrees and substrings in rather than linguistic rules is the significant component the sentence.

Next, in order to identify and establish the Sato and Nagao, In our approach, translation translation correspondences for structural constituents of examples are annotated under the representation structure Portuguese sentence, it relies on the grammatical of TCT. Each TCT structure consists of a sentence in information of the analyzed structure of Portuguese and a source language, e.

Portuguese in our case, an associated given bilingual dictionary to search the corresponding constituency structure that describing the source sentence, translation substrings from the Chinese sentence. Finally, the mapping between the inter levels of abstracted the consequent TCT structure will be verified and edited structure and its surface string of the sentence, as well as manually to obtain the final representation, which is the the corresponding relations against its translation in target basic element of the knowledge base.

The overall process language, e. The overall picture of the translation replacing the subtrees of source sentence with the chosen processes is depicted in Figure 5. In the case Portuguese Portuguese if more than one example is found, the system will Syntactic Tree evaluate the distance between the chosen examples and the source sentence based on the edit distance function. TCT representation examples as the bilingual knowledge base example base. References Al-Adhaileh, M. Grishman, R. Structures for a Bilingual Corpus.

Kaji, H. In Os1 requerentes2 devem3 ser4 comunicados 5 ao6 tribunal7 Casos1 de2 irresponsabilidade 3 do4 empreiteiro5 The petitioners must be communicated to the court Cases of irresponsibility of the contractor Proceedings of CoLING, Nantes, pp.