... Wepropose a new statistical formalism: Bilingual TopicAdMixture model, or BiTAM, to facilitate topic- based word alignment in SMT.Variants of admixturemodels have appeared inpopulation genetics ... assumed to constitute a mixture ofhidden topics; each word-pair follows a topic- specific bilingual translation model.Three BiTAMmodels are proposed to cap-ture topic sharing at different levels of ... higher-order alignment models can be embedded similarly within the pro-posed framework.3 BilingualTopicAdMixture ModelNow we describe the BiTAM formalism thatcaptures the latent topical structure...
... the probability under topic 1, topic 2,etc., or F2: What is the probability under the mostprobable topic, second most, etc.A model using F1learns whether a specific topic is useful for translation, ... machinetranslation systems toward relevant transla-tions based on topic- specific contexts, wheretopics are induced in an unsupervised wayusing topic models; this can be thought ofas inducing subcorpora ... probabilistic member-ship. This topic model infers the topic distributionof a test set and biases sentence translations to ap-propriate topics. We accomplish this by introduc-ing topic dependent lexical...
... details) before build-ing topicmodels for CR(q), where some low-frequency items are removed. Determine the number of topics: Most topic models require the number of topics to be known beforehand1. ... be related to multiple topics in some topicmodels (e.g., pLSI and LDA). Topic modeling Semantic class construction word item (word or phrase) document RASC topic semantic class Table ... top-ic models here. To employ topic models, we treat RASCs as “documents”, items as “words”, and the final semantic classes as “topics”. There are, however, several challenges in ap-plying topic...
... WorkWe have presented two novel probabilistic models for unsupervised word sense disambiguation usingparallel corpora and have shown that both models outperform existing unsupervised approaches. ... significant difference inmodel performance.5 Experimental EvaluationBoth the models are generative probabilistic models learned from parallel corpora and are expected tofit the training and subsequent ... English and Spanishsenses to build the concepts.Unsupervised Sense Disambiguation Using Bilingual Probabilistic Models Indrajit BhattacharyaDept. of Computer ScienceUniversity of MarylandCollege...
... we can recover several previously used models formonolingual segmentation and bilingual joint seg-mentation and alignment. We discuss the relation-ship ... model configuration but asimpler uniform transition distortion distribution.Note that the bilingualmodels perform worse thanthe monolingual ones in segmentation F1. Thisfinding is in line with ... subset of variablesto use in each of three component sub -models. Thismight in part explain their advantage over previous-state-of-the-art models, which might use fewer (e.g.(Poon et al., 2009)...
... the 60 topics. Each storywas labelled according to whether the story dis-cussed the topic or not. Not all the topics werepresent in the Japanese corpora. We therefore col-lected 1 topic from ... evaluationdata, TDT1, 2, or 3. ‘ID’ denotes topic number de-fined by the TDT. ‘OnT.’(On -Topic) refers to thenumber of stories discussing the topic. Bold fontstands for the topic which happened in Japan. ... calcu-1mrefers to the difference of dates between English and232Table 2: Topic NameTDT ID Topic name OnT. TDT ID Topic name OnT.1 15 Kobe Japan quake 9,9122 31015 Japan Apology to Korea...
... account. We can envisage more complex models, in- cluding distortion parameters, multiword no- tions, or information on part-of-speech, infor- mation derived from bilingual dictionaries or from ... candidate terms are extracted in one language, 449 Flow Network Models for Word Alignment and Terminology Extraction from Bilingual Corpora l~ric Gaussier Xerox Research Centre Europe 6, ... solved alignment 4. 3.2 Experiment In order to test the previous model, we se- lected a small bilingual corpus consisting of 1000 aligned sentences, from a corpus on satel- lite telecommunications....
... in DADT author topics are disjoint fromdocument topics, with different priors for each topic set. Thus, the number of author topics can be differ-ent from the number of document topics, enablingus ... χ.To fairly compare the topic- based methods, weused the same overall number of topics for all the topic models. We present only the results obtainedwith the best topic settings: 100 for PAN’11 ... number of document topics. It is worth not-ing that AT can be seen as an extreme version ofDADT, where all the topics are author topics. A fu-ture extension is to learn the topic balance automat-ically,...
... Whichwould you choose to buy? Give specific reasons to explain your choice.Writing Topics (continued)27WRITING TOPICSTopics in the following list may appear in your actualtest. You should become ... TOEFL test. Rememberthat when you take the test you will not have a choiceof topics. You must write only on the topic that isassigned to you.People attend college or university for many different ... support or oppose this plan? Why? Usespecific reasons and details in your answer.Writing Topics29WRITING TOPICSShould a city try to preserve its old, historic buildings or destroy them andreplace...
... future is unlikely to come up, this section is arranged with the topics which are most common in the Speaking Part One “future plans” topic nearest the top. Talk about one thing you are going ... jump. Luckily, the 6 to 8 most popular topics in IELTS Speaking Part One (e.g. Friends and Families) can also come up in Part Two (although the same topic is never used twice in the same test). ... presentations. It is not possible to say which topics are most likely to come up in the IELTS Speaking Test Part Two, but the most typical questions for each of the topics that are the same as IELTS Speaking...