From an acquisition perspective, the observed topic distribution represents the child’s knowledge of the context of the interaction: she can distinguish bathtime from dinnertime, and is able to recognize that some topics appear in certain contexts (e. g. animals on walks, vegetables at dinnertime) and not in others (few vegetables appear at bath-time).

Conversely, potential minimal pairs that occur in situations with similar topic distributions are more likely to belong to the same topic and thus the same lexeme.

Although we assume that children infer topic distributions from the nonlinguistic environment, we will use transcripts from CHILDES to create the word/phone learning input for our model.

However, in our simulations we approximate the environmental information by running a topic model (Blei et al., 2003) over a corpus of child-directed speech to infer a topic distribution for each situation.

These topic distributions are then used as input to our model to represent situational contexts.

There are a fixed number of lower level topic-lexicons; these are matched to the number of topics in the LDA model used to infer the topic distributions (see Section 6.4).

where a, ,6, *7 are symmetric Dirichlet priors on state-wise topic distribution 675’s, topic-wise word distribution qbt’s and state transition multinomials, respectively.

Therefore, the topic distribution is often stable throughout the entire dialogue, and does not vary from turn to turn.

To express this, words in the TM—HMMS model are generated either from a dialogue-specific topic distribution , or from a state-specific language model.4 A distribution over sources is sampled once at the beginning of each dialogue and selects the expected fraction of words generated from different sources.

Polylingual topic models (Mimno et al., 2009) assume that the aligned documents in different languages share the same topic distribution and each language has a unique topic distribution over its word types.

If each topic defines a SMT domain, the document’s topic distribution is a soft domain assignment for that document.

The topics come from source documents only and create topic-specific lexical weights from the per-document topic distribution p(l<: | d).

For a test document d, the document topic distribution p(l<: | d) is inferred based on the topics learned from training data.

The PLDA toolkit (Liu et al., 2011) is used to infer topic distributions , which takes 34.5 hours to finish.

In contrast, with our neural network based approach, the learned topic distributions of “deliver X” or “distribute X” are more similar with the input sentence than “send X”, which is shown in Figure 4.

(2007) used bilingual LSA to learn latent topic distributions across different languages and enforce one-to-one topic correspondence during model training.

Since different sentences may have very similar topic distributions , we select negative instances that are dissimilar with the positive instances based on the following criteria:

We present Code-Switched LDA (csLDA), which infers language specific topic distributions based on code-switched documents to facilitate multilingual corpus analysis.

Our solution is to automatically identify aligned polylingual topics after learning by examining a topic’s distribution across code-switched documents.

0 Draw a topic distribution 6d ~ Dir(oz) 0 Draw a language distribution deDiT (7) o For each token i E d: 0 Draw a topic 21- ~ 6d 0 Draw a language I, ~ wd 0 Draw a word 21),- N of, For monolingual documents, we fix I, to the LID tag for all tokens.

Since csLDA duplicates topic distributions (2' x L) we used twice as many topics for LDA.

Thus, we employ a LDA variation (Mukherjee and Liu, 2012), an extension of (Zhao et al., 2010), to discover topic distribution on words, which sampled all words into two separated observations: opinion targets and opinion words.

It’s because that we are only interested in topic distribution of opinion targets/words, regardless of other useless words, including conjunctions, prepositions etc.

p(z|vt) and p(z|v0), and topic distribution Then, a symmetric Kullback—Leibler divergence as same as Eq.5 is used to calculate the semantical associations between any two homoge-nous candidates.

The kth column of A will be the topic distribution over all words for topic 11:, and A111,], is the probability of observing type w given topic 11:.

We use Bayes rule to recover the topic distribution p(w = = k) E

Held-out likelihood cannot be computed with existing anchor algorithms, so we use the topic distributions learned from anchor as input to a reference variational inference implementation (Blei et al., 2003) to compute HL.

In particular, a word-based graph model is proposed to represent the explicit relatedness among words in a discourse from the lexical perspective, while a topic-driven word-based model is proposed to enrich the implicit relatedness between words, by adding one more layer to the word-based graph model in representing the global topic distribution of the whole dataset.

In order to reduce the gap, we propose a topic-driven word-based model by adding one more layer to refine the word-based graph model over the global topic distribution , as shown in Figure 2.

In the topic-driven word-based graph model, the first layer denotes the relatedness among content words as captured in the above word-based graph model, and the second layer denotes the topic distribution , with the dashed lines between these two layers indicating the word-topic model return by LDA.