Construction of a Bilingual Dictionary Intermediated by a Third Language C o n , t r u c t l o n o f a ]3ili ngua, l )" ? 1 tci, onary I n t e r m e d i a t e d b y a T h i r d L a n g u a , g e K u m[.]
A problematic case of context vector projection is illustrated in Figure 2. For calculating contex- tual similarity, such as a cosine, the context vec- tors 3 must be projected onto associated-word di- mensions in the same language. In this approach, associated words are duplicated by translation perplexity. In this example, each word associated with the Japanese word “石油” sekiyu ‘petrole- um’ has several possible English translations. It yields unnecessary Chinese associated words such as “力” li ‘power’ and “细胞” xibao ‘cell (in the biological sense),’ and then falsely de- creases the cosine value because the norm of the projected vector increases. 4
During word segmentation undefined words can be problematic. A lexicon cannot contain all the place names, institution names and personal names that can occur, such as Kista, Adecco, Jason, Peter, etc., but word segmentation for Chinese needs automatically to identify all of those words. For language processing of Chinese, lexical analysis is therefore of vital importance.
In order to evaluate our approach, we conduct ex- periments on two real data sets, which are from collection of brand reviews including digital cam- eras and car domains. For the target language of English, the product dataset contains 9542 reviews which are collected from www.buzzilions.com and www.carreview.com. For the source language of Chinese, the product dataset contains 8432 re- views which are collected from www.Amazon.cn and www.xche.com.cn. For our experiment,we use a Oxford English-Chinese bilingualdictionary to match similarity semantic reviewer sentence, any two of them are used as comparable corpus,the copora are non-parallel, but loosely compara in term of its content. Though the scale of Chinese corpora is large, most of the reviews are short texts and there are a lot noise in the content. For Chi- nese, we use the ICTLAS 3.0 (Zhang et al., 2003) toolkit to conduct word segmentation over sen- tences.
It is no longer useful to dwell on the costly and lengthy nature of the construction of a computational lexicon for natural language processing and word sense disambiguation. For nearly twenty years now, researchers have tried to tap the contents of machine-readable dictionaries with a view to extracting, formalizing and representing the linguistic information they contain and turning it into formats usable in machine translation, information retrieval, automatic dictionary look-up, question answering, etc. More recently, especially as a result of advances in dictionary making in the Anglo- Saxon world, corpora have become one of the main sources of information for populating the large computational lexica required by any NLP system. Although some researchers claim that pure dictionary research has run its course and that the time has come to envisage applications only, it is far from clear whether all the information contained in MRDs has really been tapped and whether the electronic versions of large commercial dictionaries have yielded all their secrets, making them intellectually less interesting and scientifically less worthy of attention. This is probably a moot point since the new generation of dictionaries are the result of scores of person-years of close scrutiny of corpus-based evidence which had to be dissected, digested, interpreted, condensed and regurgitated by teams of highly skilled lexicographers. Neglecting this data would boil down to reinventing the wheel with imperfect tools, which, in this author's view, pleads for a combination of linguistic resources, viz. existing dictionaries and textual corpora, rather than the exclusion of one resource in favour of the other.
Bilingual dictionaries are vital in many areas of natural language processing, but such resources are rarely available for lower-density language pairs, especially for those that are closely related. Pivot-based induction consists of using a thirdlanguage to bridge a language pair. As an approach to create new dictionaries, it can generate wrong translations due to polysemy and ambiguous words. In this paper we propose a constraint approach to pivot-based dictionary induction for the case of two closely related languages. In order to take into account the word senses, we use an approach based on semantic distances, in which possibly missing translations are considered, and instance of induction is encoded as an optimization problem to generate new dictionary. Evalua- tions show that the proposal achieves 83.7% accuracy and approximately 70.5% recall, thus outperforming the baseline pivot-based method. Keywords: BilingualDictionary Induction, Weighted Partial Max-SAT, Constraint Satisfaction
The third part of the dictionary is the video manager section. Experts in both sign language and in edu- cational curricula have selected the words that have to be included in the dictionary and “translated” into videos. The operation of “translating” words into videos is crucial for the dictionary. This part of the dictionary is shared between the two main sections described above and consists of a list of videos, tagged using the four sign parameters, which display Spanish words expressed in sign language. Each video is linked to the Spanish form and contains the sign parameters used to express that specific form. The tagging procedure is very important in the process of defining the bilingualdictionary. Videos are tagged
Nobody doubts the usefulness and multiple applications of bilingual dictionaries: as the final product in lexicography, translation, language learning, etc. or as a basic resource in several fields such as Natural Language Processing (NLP) or Information Retrieval (IR), too. Unfortunately, only major languages have many bilingual dictionaries. Furthermore, construction by hand is a very tedious job. Therefore, less resourced languages (as well as less-common language pairs) could benefit from a method to reduce the costs of constructing bilingual dictionaries. With the growth of the web, resources like Wikipedia seem to be a good option to extract new bilingual lexicon (Erdmann et al., 2008), but the reality is that a dictionary is quite different from
There are many hardcopy or paper Assamese Dictionary developed by many Authors. Miles Bronson, an American missionary was the first to compile a Dictionary of the Assamese language. His Dictionary published in 1867 at American Baptist Mission Press Sibsagar is out of print now. The first Anglo-Assamese Dictionary was compiled by a student of Cotton College in the year 1910, Makhan Lal Chaliha of Chiring Chapori, which was found in the British Library by a researcher of the Jatiiya Sikha Samanay Parishad. The third Assamese Dictionary Chandrakanta Abhidhan, a comprehensive BilingualDictionary with words and their meanings in Assamese and English, originally compiled and published by Assam Sahitya Sabha in 1933, 32 years after the publication of the Hem Kosh.
We implemented the EM-algorithm using the programming language C on a Unix work station. Sparse matrix technology [Pissanetzky, 1984] was used to implement a datastructure that uses minimal memory to hold two matrix copies with the same zero values. One copy is used for the probability estimates, the other is necessary to collect the frequency counts. The datastructure used is called a sparse row-wise ordered matrix format. Each row consists of a list of three words (a word consists of two bytes), the first contains the column index, the second and the third contain the values of both matrix copies. Two values with the same column indexes are allowed indicating that the value has to be stored in four bytes. A pointer list is needed to find the start of each row. Each matrix copy needs little more than 33% memory overhead, that is memory needed to find the right matrix cell. The program uses four stages to carry out the EM algorithm.
The automatic construction of bilingual lexicons has been one of the most studied areas in the ﬁeld of Nat- ural Language Processing in recent years, especially with the hope of harnessing the vast sum of data avail- able on the web. A variety of approaches have been proposed, most of which focus on the extraction of generic lexicons, however in this paper we focus on
For practical needs in Korea, this paper considers the bilingual pronunciation dictionary of both Korean and English. These languages also provide academic insights, because their cross-linguistic variation ranges significantly. Moreover, the two languages are very different in both grammatical and social aspects. In the following sections, we will explore in detail how the difference affects the design of the lexical entry for the dictionary in question.
The idea behind modeling POS tags is that words should have the same part of speech tag in dif- ferent languages. For example, if we are trans- lating the noun Katze from German to English, in English we expect the singular noun cat and not the plural cats. While this information may be monolithically represented in word vectors gen- erated by embedding methods such as Skip-gram and CBOW, here we seek to explicitly model POS tags. Since each word can have multiple POS tags, we model a word’s part of speech information as a distribution over all the possible POS tags that it can take on. We learn POS tag statistics by first tagging a large corpus of each language, we then use tag counts to generate distributions. For exam- ple, if the English word, bark appears tagged as a verb 30 times in our corpus, and tagged as a noun 10 times, we generate a vector which puts 2/3 in the verb direction, and 1/3 in the noun direction, and 0 in the directions of all other POS tags. While these statistics can be noisy, we hope they can still provide useful signals. We use the universal POS tags, there are 12 tags in the universal POS tags (Petrov et al., 2011).
s.t. U ΣV > = SVD(Y X > ) (1) This step can be used iteratively by using the new matrix W to create new seed translation pairs. It requires frequent words to serve as reliable anchors for learning a translation matrix. In the experiments in Conneau et al. (2018), as well as in ours, the iter- ative Procrustes refinement improves performance across the board. 4) Cross-domain similarity lo- cal scaling (CSLS) is used to expand high-density areas and condense low-density ones, for more ac- curate nearest neighbor calculation, CSLS reduces the hubness problem in high-dimensional spaces (Radovanovi´c et al., 2010; Dinu et al., 2015). It relies on the mean similarity of a source language embedding x to its K target language nearest neigh- bours (K = 10 suggested) nn 1 , . . . , nn K :
Offline linear map induction methods The ear- liest approach to induce a linear mapping from the monolingual embedding spaces into a shared space was introduced in (Mikolov et al., 2013). They propose to learn the mapping by optimising the least squares objective on the monolingual em- bedding matrices corresponding to translational equivalent pairs. Subsequent research aimed to improve the mapping quality by optimising dif- ferent objectives such as max-margin (Lazaridou et al., 2015) and by introducing an orthogonal- ity constraint to the bilingual map to enforce self- consistency (Xing et al., 2015; Smith et al., 2017). (Artetxe et al., 2016) provide a theoretical analy- sis to existing approaches and in a follow-up re- search (Artetxe et al., 2018) they propose to learn principled bilingual mappings via a series of linear transformations.
One issue with the pervasive use of dictionary translations is the problem of compound phrases in the test sentence that are made up of component phrases in the dictionary. For instance, when de- coding the sentence, “Here was developed a phase shift magnetic sensor system composed of two sets of coils , amplifiers , and phase shifts for sensing and output .”, we fetch the following entries from the dictionary to translate the underlined multi- word term:
The Kamusi solution is to provide fields for “bridges”. Though not implemented as of this writing, the monolingual entry for a term will also include the option for a contributor to “add a bridge” for a part of speech. The English adjec- tive “careful” can be augmented with the verb bridge “be careful”, and the French noun “atten- tion” can have the verb bridge “faire attention”. The English and French items can then be linked to German and become connected transitively along the horizontal beam, or they can be linked directly without the German intermediary. In either case, we do not crowd the monolingual side of a dictionary with unnecessary entries for differently-structured concepts from other lan- guages, but we include the necessary information and make it discoverable.
MAN ASSISTED MACHINE CONSTRUCTION OF A SEMANTIC DICTIONARY FOR NATURAL LANGUAGE PROCESSING COLING 82, J Horeck~ (ed ) North Holland Publishing Company Academia, 1982 MAN ASSISTED MACHINE CONSTRUCTION[.]
R ÉSUMÉ ____________________________________________________________________________________________________________ Cet article présente la structure du dictionnaire kanouri-français de 6 000 entrées élaboré lors du projet SOUTÉBA puis informatisé lors du projet DiLAF. Il présente également la langue kanouri, ses locuteurs ainsi que la place de la langue dans les diférentes classifcations génétiques. Viennent ensuite une description de sa typologie et de son système verbal. L'article se termine par une description de l'orthographe kanouri. A BSTRACT __________________________________________________________________________________________________________ Construction of the Kanuri-French bilingualdictionary
Second, neither the lexicon nor the bilingual dic- tionary provides information on the sense of the in- dividual entries, and therefore the translation has to rely on the most probable sense in the target lan- guage. Fortunately, the bilingualdictionary lists the translations in reverse order of their usage frequen- cies. Nonetheless, the ambiguity of the words and the translations still seems to represent an impor- tant source of error. Moreover, the lexicon some- times includes identical entries expressed through different parts of speech, e.g., grudge has two sepa- rate entries, for its noun and verb roles, respectively. On the other hand, the bilingualdictionary does not make this distinction, and therefore we have again to rely on the “most frequent” heuristic captured by the translation order in the bilingualdictionary.