Top PDF Implementation of an Assamese English Bilingual E-Dictionary

A. Natural Language Processing: Natural Languages are the languages which have naturally evolved and used by human beings for communication purposes. The some examples of Natural Languages are Assamese, Bengali, English and Hindi. Natural Language Processing (NLP) is the scientific study of languages from computational perspective. NLP is a field of Computer science, artificial intelligence and linguistics concerned with the interactions between Computers and Natural languages. NLP is a very attractive method of human-computer interaction. Natural language processing is the ability of a Computer program to understand human speech as it is spoken. The goal of the Natural Language Processing group is to design and build software that will analyze, understand, and generate languages that humans use naturally. Application areas within NLP include automatic (Machine) translation between languages, Natural language generation, Natural language understanding, Optical Character Recognition(OCR) , Part-of-Speech Tagging(POST), Parsing, Speech Recognition(SR), Speech Processing(SP), Information Retrieval (IR), and Speech Segmentation(SS). .

The great majority of technical terms in Japanese are transliterations of English words. It is therefore an in- teresting option to consider designing a system specif- ically for transliteration extraction, as this allows us to improve the accuracy greatly by making use of a transliteration model, while sacrificing only mini- mal coverage. Although we concentrate on Japanese- English in this paper, our model could easily be ex- tended to generate dictionaries for other languages that contain many transliterations, such as Arabic, Ko- rean and Russian.

Most pronunciation dictionaries from Merriam-Webster, Cambridge, Longman, etc. are “lexicon-based,” in that all lexical entries of different meanings are listed for audio registry. One selected speaker typically records them. The proposed dictionary is “sound-based,” in that all sound entries are listed for the different allophonic environments, and all recording materials are read by various speakers of balanced distribution. Hence, when one seeks an answer to the question “how many representative variations are there for the phoneme [k] in English and Korean?”, then only a sound-based dictionary as opposed to a lexicon-based dictionary can answer the variations from, for instance,

Frame semantics is a linguistic theory which is currently gaining ground. The creation of lexical entries for a large number of words presupposes the development of complex lexical acquisition techniques in order to identify the vocabulary for describing the elements of a 'frame'. In this paper, we show how a lexical-semantic database compiled on the basis of a bilingual (English-French) dictionary can be used to identify some general frame elements which are relevant in a frame-semantic approach such as the one adopted in the FrameNet project (Fillmore & Atkins 1998, Gahl 1998). The database has been systematically enriched with explicit lexical-semantic relations holding between some elements of the microstructure of the dictionary entries. The manifold relationships have been labelled in terms of lexical functions, based on Mel'cuk's notion of co-occurrence and lexical-semantic relations in Meaning-Text Theory (Mel'cuk et al. 1984). We show how these lexical functions can be used and refined to extract potential realizations of frame elements such as typical instruments or typical locatives, which are believed to be recurrent elements in a large number of frames. We also show how the database organization of the computational lexicon makes it possible to readily access implicit and translationally-relevant combinatorial information.

Another well-known approach, pivot-based induction, uses a widespread language as a bridge between less-resourced language pairs. Its naive implementation proceeds as fol- lows. For each word in A language we take its translations in pivot language B from dictionary A-B, then for each such pivot translation, we take its translations in C lan- guage using B -C. This implementation yields highly noisy dictionaries containing incorrect translation pairs, because lexicons are generally intransitive. This intransitivity stems from polysemy and ambiguous words in the pivot language. To cope with the issue of divergence, previous studies at- tempted to select correct translation pairs by using semantic distances extracted from the inner structure of input dictio- naries (Tanaka and Umemura, 1994) or by using additional external resources such as part of speech (Bond and Ogura, 2008), WordNet (Istv´an and Shoichi, 2009), comparable corpora (Kaji et al., 2008; Shezaf and Rappoport, 2010) and

training dictionaries might not be available for all languages. However, for a given language with only a small seed dictionary, there could be a highly related language with a much larger seed dictionary. For example, we might have a small seed dictionary for translating Portuguese to English (pt → en), but a large seed dictio- nary for translating Spanish to English language (es → en). At training time, we can train the (pt → en) mapping function not only using the small seed dictionary, but also make use of the trilingual path going through Spanish, (pt → es → en). Since pt and es are highly re- lated, a small amount of data may be sufficient to learn the projection (pt → es). This is the idea of using a bridge or pivot language in machine trans- lation (Utiyama and Isahara, 2007). Our contri- bution is a knowledge distillation training objec- tive function that encourages the mapping func- tion ( pt → en) to predict the true English target words as well as to match the predictions of the trilingual path ( pt → es → en) within a margin. This is approach allows seamless Example trilin- gual paths are shown in Figure 1.

Word embeddings are particularly good at cap- turing relations between nouns, but even if we con- sider the top k most frequent English nouns and their translations, the graphs are not isomorphic; see Figure 1c-d. We take this as evidence that word embeddings are not approximately isomor- phic across languages. We also ran graph isomor- phism checks on 10 random samples of frequent English nouns and their translations into Spanish, and only in 1/10 of the samples were the corre- sponding nearest neighbor graphs isomorphic. Eigenvector similarity Since the nearest neigh- bor graphs are not isomorphic, even for frequent translation pairs in neighboring languages, we want to quantify the potential for unsupervised BDI us- ing a metric that captures varying degrees of graph similarity. Eigenvalues are compact representations of global properties of graphs, and we introduce a spectral metric based on Laplacian eigenvalues (Shigehalli and Shettar, 2011) that quantifies the extent to which the nearest neighbor graphs are isospectral. Note that (approximately) isospectral graphs need not be (approximately) isomorphic, but (approximately) isomorphic graphs are always (approximately) isospectral (Gordon et al., 1992). Let A 1 and A 2 be the adjacency matrices of the

There is a general consensus on the fact that the major problem facing translators involved in translating from English into Arabic is finding term equivalents. The problem centers round critical, literary, social, political, or scientific terms. Some conceptual terms have actually been Arabicized and popularized such as democracy, dictatorship, imperialism, classicism, romanticism. But even these established concepts do not have equivalents that parallel their other syntactical forms- imperialize, romanticize, classicize, for instance. Sometimes there is more than one term in Arabic for an established concept/term in English- ‘discourse’ with ‘ باطخ’ as equivalent in Arabic. Is this so because Arabic is a less ‘developed’ language? Cluver (1989) points out that since the terminographer working on a developing language actually participates in the elaboration/ development of the terminology, he/she needs a deeper understanding of the word-formation processes than his/her counterpart who works on a so-called ‘developed language’ (Cluver, 1989: 254).

Among the resources available for making lexical data bases, we have typesetting tapes of Webster's Seventh, Longman's Dictionary of Contemporary English LDOCE, several Collins bilingual[r]

In this paper we describe the system that we develop as part of our participation in the shared task of WAT 2016. We have submitted models for English-Hindi language pair. We have developed various models based on phrase-based as well as hierarchical MT models. Empirical analysis shows that we achieve the best performance with a hierarchical SMT based approach. We also show that hierarchical SMT model, when augmented with bilingual dictionary along with syntactic reordering of English sentences produces better translation score.

Table 2 presents the BLEU scores of the Japanese to English (JA-EN) translation outputs from the phrase-based SMT system on the WAT test set. The leftmost columns indicate the number of times a dictionary is appended to the parallel training data (Baseline = 0 times, Passive x1 = 1 time). The rightmost columns present the results from both the passive and pervasive use of dictionary trans- lations, with exception to the top-right cell which shows the baseline result of the pervasive dictio- nary usage without appending any dictionary.

WordNet comprises of contents that are linked to both English and Hindi WordNet. A combina- tion of dictionary and thesaurus, Assamese Word- Net comprises of four major components. They are ID which act as a primary key for identify- ing any synset in WordNet, CAT indicates the Parts Of Speech category, SYNSET lists the syn- onymous words in a most used frequency order and GLOSS describes the concept of any synset. GLOSS consist of Text-Definition and Example- Sentence. Text Definition contains concepts de- noted by synset and Example shows the use of any synset entry. There are various semantic re- lation that occur between synsets in WordNet. They are Hypernymy-Hyponymy(IS-A/Kind of), Entailment-Troponymy (Manner-of for verbs), Meronymy-Holonymy (HAS-A/ PART-WHOLE). Synset, the basic building block of WordNet can explore the semantically related terms. For in- stance these words খা (kharu: Bangles), কংকণ (kankan: Bangles), ক ণ (kangkan: Bangles) de- scribes the same concept হাতত িপ া এিবধ গহনা (haatat pindhaa ebidh gahanaa:A hand wearing or- nament).This structure of WordNet helps in au- tomatic text analysis and various artificial intel- ligence applications as a combination of dictio- nary and thesaurus. Assamese WordNet has been used for a number of different purposes in text analysis such as Automatic document classifica- tion (Sarmah et al., 2012), Automatic text sum- marization (Kalita et al., 2012) etc. Here, we tried to use the Assamese WordNet basically the synsets for fine tuning the translated output by re- placing words with their most appropriate synony- mous word for that particular sentence.

This paper describes the machine translation sys- tems developed by the Computer Science labo- ratory at the University of Le Mans (LIUM) for the 2009 WMT shared task evaluation. This work was performed in cooperation with the company SYSTRAN. We only consider the translation be- tween French and English (in both directions). The main differences to the previous year’s system (Schwenk et al., 2008) are as follows: better us- age of SYSTRAN’s bilingual dictionary in the sta- tistical system, less bilingual training data, addi- tional language model training data (news-train08 as distributed by the organizers), usage of com- parable corpora to improve the translation model, and development of a statistical post-editing sys- tem (SPE). These different components are de- scribed in the following.

If a translation is unseen, the system will perform bad with particular queries, as the proper translation cannot be found if it has zero probability. So the answer to question one is yes. If the approximation of the channel probability P(D|E) allows unseen events to happen with very low probability then the approximation of the prior probability P(E) has to make sure that, if necessary, the unseen translation is chosen. Consider for example a native speaker of Dutch wants to know something about statistische automatische vertaling (that is, statistical machine translation) and the approximation of the channel probability gives high probabilities to (statistische | statistical), (automatische | automatic) and (vertaling | translation) and a very low probability to (automatische | machine) because it was unseen in the training data. Of course the English word automatic is not the right translation in this context. If the approximation of the prior probability P(E) is a bigram approximation, then it will probably assign very low probability to both (statistical, automatic) and (automatic, translation). A bigram approximation will probably assign relatively high probability to both (statistical, machine) and (machine, translation), choosing statistical machine translation to be the proper translation.

Nihon Keizai Shimbun, Inc, or NIKKEI publishes four daily newspapers in Japanese, i.e., The Nihon Keizai Shimbun, The Nikkei Industrial Daily, The Nikkei Financial Daily and The Nikkei Marketing Journal. Some of their articles are translated into English for distribution via various Internet services. Currently about 30,000 English articles are accumulated every year. At NIKKEI, these Japanese and English articles are stored in separate databases and have no explicit correspondence information. However, we can expect to make a voluminous bilingual corpus by aligning the English and Japanese articles with each other. Table 1 shows the actual number of Japanese and English articles between 1995 and 2001.

We have generated several ad hoc translations by simply translating each word in the segmentations to English. Most are not grammatically correct. We use a method, presented in Algorithm 1, to reduce the number of ad hoc translations. We consider words in each entry in the English n-gram data as a bag of words NB (lines 1-3), i.e., the words in each entry is simply considered a set of words instead of a sequence. For example, the 3-gram “computer sci- ence department” is considered as the set {computer, science, department}. Each ad hoc translation T , created in Section 4.3, is also considered a bag of words T B (lines 4-6). For every bag of words T B, we find each bag of words NB 0 , belonging to the set

Based on the findings of this study, L2 teachers in Iran can allow learners, particularly the less advanced ones, to make use of bilingual dictionaries to demonstrate their writing skill in L2 writing classrooms. As East (2005) pointed out, using dictionaries in assessment has been the subject of debate. If, however, focus is placed on helping L2 teachers to use assessment, as part of teaching and learning in classrooms, in ways that will raise their learners' achievement, dictionaries may have a valid role to play. Therefore, it is suggested that Iranian EFL learners, in particular, make use of Persian-to-English or English-to-Persian dictionaries as a supportive tool in essay writing classes. Another implication of this study is that product approaches to writing should not be totally abandoned. Rather, they should be complementary to the process approaches; bilingual dictionaries are useful in the process as well as the product of L2 writing. EFL learners should be taught not only heuristic devices to focus on meaning, but also heuristic devices to focus on linguistic features. However, the findings obtained in this study imply that high frequency of dictionary use, though important, does not compensate for the lack of L2 writing knowledge.

Abstract — Language is the most important aspect in the life of all human beings. A language is one of the most important and effective modes of communication between the people belonging to different communities and cultures. The language acts as a bridge among us and helps in creating a bond among our cultures. Therefore, to learn mother language as well as other new languages is very important for us. The dictionary is one of the important tools that can be used for learning new languages. A word is basically an association of linguistic sound and meaning. The spelling does not always easily correlate with the sound of a word. A dictionary helps us both with the spelling and pronunciation of such words. Electronic dictionaries are very popular nowadays and many users can be accessed simultaneously on Online. This paper describes the Development of Multilingual Assamese Electronic Dictionary (MAED). The MAED contains four languages, namely Assamese, Bengali, English and Hindi. We have developed Assamese-Bengali, English and Hindi (A-BEH) Dictionary in MAED. The A-BEH Dictionary is a user friendly dictionary and user can easily look up the meanings of words and other related information of the words like word Id, POS, synonyms and examples from Assamese language to Bengali, English and Hindi languages on Online. This dictionary will be beneficial for Assamese people as well as other people living in India.

In our resulting dictionary, we found several English words appearing in several inflected forms. Singular forms and plural forms of nouns in English, such as ‘service’ and ‘services’ respectively, both translate as the Chinese word ‘ 务’. Therefore, lemmatising English words before using Uplug may increase accuracy (see Piao 2002). We also noticed that there are several English synonyms translated as the same Chinese word, but there are no Chinese synonyms in the result list. We do not know the reason for this yet. One way to discover it would be to switch alignment order and let Chinese be the source language and English the target language.

We propose to obtain the average of the nearest k-english-word-vectors for the given french word and use it as the embedding for the French word. For k=1, this reduces to a bilingual lexical dictionary using bilingual em- beddings (Vulic and Moens, 2015; Madhyastha and España-Bonet, 2017). Since the bilingual embeddings are not perfectly aligned, Smith et al. (2017) show 4 that precision@k increases as k increases (e.g. for Hindi P@1 is 0.39, P@3 is 0.58 and P@10 is 0.63), when we ob- tain French (or any other language) transla- tions for an English word. Thus, we conduct experiments with varying values of k and re- port the best results for the optimal k. Our experiments confirm the efficacy of KNBET. Further, we believe this KNBET can be used to improve the performance of any multilin- gual system that uses bilingual embeddings.