Index of papers in Proc. ACL that mention

**probability distribution**

Definitions | Intuitively, propemess ensures that where a pair of nonterminals in two synchronous strings can be rewritten, there is a probability distribution over the applicable rules. |

Definitions | We say a PSCFG is consistent if pg defines a probability distribution over the translation, or formally: |

Discussion | Prefix probabilities and right prefix probabilities for PSCFGs can be exploited to compute probability distributions for the next word or part-of-speech in left-to-right incremental translation of speech, or alternatively as a predictive tool in applications of interactive machine translation, of the kind described by Foster et al. |

Effective PSCFG parsing | The translation and the associated probability distribution in the resulting grammar will be the same as those in the source grammar. |

Effective PSCFG parsing | Again, in the resulting grammar the translation and the associated probability distribution will be the same as those in the source grammar. |

Introduction | Prefix probabilities can be used to compute probability distributions for the next word or part-of-speech. |

Introduction | Prefix probabilities and right prefix probabilities for PSCFGs can be exploited to compute probability distributions for the next word or part-of-speech in left-to-right incremental translation, essentially in the same way as described by Jelinek and Lafferty (1991) for probabilistic context-free grammars, as discussed later in this paper. |

Prefix probabilities | The next step will be to transform Qprefix into a third grammar gl’mfix by eliminating epsilon rules and unit rules from the underlying SCFG, and preserving the probability distribution over pairs |

probability distribution is mentioned in 8 sentences in this paper.

Topics mentioned in this paper:

Generation & Propagation | Both parallel and monolingual corpora are used to obtain these probability distributions over target phrases. |

Generation & Propagation | If a source phrase is found in the baseline phrase table it is called a labeled phrase: its conditional empirical probability distribution over target phrases (estimated from the parallel data) is used as the label, and is sub- |

Generation & Propagation | We then propagate by deriving a probability distribution over these target phrases using graph propagation techniques. |

Introduction | We then limit the set of translation options for each unlabeled source phrase (§2.3), and using a structured graph propagation algorithm, where translation information is propagated from labeled to unlabeled phrases proportional to both source and target phrase similarities, we estimate probability distributions over translations for |

probability distribution is mentioned in 7 sentences in this paper.

Topics mentioned in this paper:

- language model (18)
- BLEU (15)
- bigram (13)

Experiment | Furthermore, the hyper-parameters for topic probability distribution and word probability distribution in LDA are a=0.5 and [3:05, respectively. |

Experiment | Here, in the case of clustering the documents based on the topic probabilistic distribution by LDA, the topic distribution over documents 6 is changed in every estimation. |

Experiment | To measure the latent similarity among documents, we construct topic vectors with the topic probabilistic distribution , and then adopt the Jensen-Shannon divergence to measures it, on the other hand, in the case of using document vectors we adopt cosine similarity. |

Techniques for text classification | After obtaining a collection of refined documents for classification, we adopt LDA to estimate the latent topic probabilistic distributions over the target documents and use them for clustering. |

Techniques for text classification | In this study, we use the topic probability distribution over documents to make a topic vector for each document, and then calculate the similarity among documents. |

Techniques for text classification | Here, N is the number of all words in the target documents, wmn is the nth word in the m-th document; 6 is the topic probabilistic distribution for the documents, and gb is the word probabilistic distribution for every topic. |

probability distribution is mentioned in 7 sentences in this paper.

Topics mentioned in this paper:

- PageRank (31)
- text classification (12)
- co-occurrence (8)

Language Identification | For each language, we collect the n-gram counts (for n = l to n = 7 also using the word beginning and ending spaces) from the vocabulary of the training corpus, and then generate a probability distribution from these counts. |

Language Identification | From these counts, we obtained a probability distribution for all the words in our vocabulary. |

Language Identification | In Table 3, we present the top 10 results of the probability distributions obtained from the vocabulary of English, Finnish, and German corpora. |

Related Work | (Sibun and Reynar, 1996) used Relative Entropy by first generating n-gram probability distributions for both training and test data, and then measuring the distance between the two probability distributions by using the Kullback-Liebler Distance. |

probability distribution is mentioned in 6 sentences in this paper.

Topics mentioned in this paper:

- n-gram (11)
- confidence score (6)
- probability distribution (6)

Experiments | While the SENSESPOTTING task has MT utility in suggesting which new domain words demand a new translation, the MOSTFRE-QSENSECHANGE task has utility in suggesting which words demand a new translation probability distribution when shifting to a new domain. |

New Sense Indicators | Second, given a source word 3, we use this classifier to compute the probability distribution of target translations (p(t|s)). |

New Sense Indicators | Subsequently, we use this probability distribution to define new features for the SENSESPOTTING task. |

New Sense Indicators | Entropy is the entropy of the probability distribution : — 2t p(t|s) log p(t|s). |

probability distribution is mentioned in 6 sentences in this paper.

Topics mentioned in this paper:

- parallel data (10)
- probability distribution (6)
- n-gram (5)

Probabilistic generative model | If a generative model is fully parameterised it can be reversed to find the underlying word decomposition by forming the conditional probability distribution Pr(Y |X |

Probabilistic generative model | The first component of the equation above is the probability distribution over non-/boundaries Pr(bji). |

Probabilistic generative model | We assume that a boundary in i is inserted independently from other boundaries (zero-order) and the graphemic representation of the word, however, is conditioned on the length of the word m j which means that the probability distribution is in fact Pr(bji|mj). |

probability distribution is mentioned in 6 sentences in this paper.

Topics mentioned in this paper:

Background 3.1 LDA | Draw a word: wdm N Multinomial(zd,n) Where, T is the number of topics, 9b,; is the word probabilities for topic 75, 6d is the topic probability distribution , 2d,“, is topic assignment and wdm is word assignment for nth word position in document d respectively. |

Experimental Evaluation | (a) Infer a probability distribution 0d over class labels using M D using Equation 3. |

Introduction | We use the labeled topics to find probability distribution of each training document over the class labels. |

Topic Sprinkling in LDA | We use this new model to infer the probability distribution of each unlabeled training document over the class labels. |

Topic Sprinkling in LDA | While classifying a test document, its probability distribution over class labels is inferred using TS-LDA model and it is classified to its most probable class label. |

probability distribution is mentioned in 5 sentences in this paper.

Topics mentioned in this paper:

- text classification (19)
- LDA (17)
- Gibbs sampling (10)

Generative state tracking | (1996)) models the conditional probability distribution of the label 3/ given features X, p(y|x) via an exponential model of the form: |

Introduction | The task is to assign a probability distribution over the G dialog state hypotheses, plus a meta-hypothesis which indicates that none of the G hypotheses is correct. |

Introduction | Also note that the dialog state tracker is not predicting the contents of the dialog state hypotheses; the dialog state hypotheses contents are given by some external process, and the task is to predict a probability distribution over them, where the probability assigned to a hypothesis indicates the probability that it is correct. |

Introduction | Dialog state tracking can be seen an analogous to assigning a probability distribution over items on an ASR N-best list given speech input and the recognition output, including the contents of the N-best list. |

probability distribution is mentioned in 5 sentences in this paper.

Topics mentioned in this paper:

- confidence score (9)
- feature set (7)
- maximum entropy (7)

A Generative PCFG Model | (1996) who consider the kind of probabilities a generative parser should get from a PoS tagger, and concludes that these should be P(w|t) “and nothing fancier”.3 In our setting, therefore, the Lattice is not used to induce a probability distribution on a linear context, but rather, it is used as a common-denominator of state-indexation of all segmentations possibilities of a surface form. |

A Generative PCFG Model | We smooth Prf (p —> (s, 19)) for rare and 00V segments (3 E [,1 E L, s unseen) using a “per-tag” probability distribution over rare segments which we estimate using relative frequency estimates for once-occurring segments. |

Discussion and Conclusion | The overall performance of our joint framework demonstrates that a probability distribution obtained over mere syntactic contexts using a Treebank grammar and a data-driven lexicon outperforms upper bounds proposed by previous joint disambiguation systems and achieves segmentation and parsing results on a par with state-of-the-art standalone applications results. |

Model Preliminaries | Given that weights on all outgoing arcs sum up to one, weights induce a probability distribution on the lattice paths. |

probability distribution is mentioned in 4 sentences in this paper.

Topics mentioned in this paper:

- PoS tags (17)
- morphological analyzer (13)
- treebank (12)

Learning | Given the expected counts, we now need to normalize them to ensure that the transducer represents a conditional probability distribution (Eisner, 2002; Oncina and Sebban, 2006). |

Message Approximation | An alternative approach might be to simply treat messages as unnormalized probability distributions , and to minimize the KL divergence be- |

Message Approximation | tween some approximating message mm) and the true message However, messages are not always probability distributions and — because the number of possible strings is in principle infinite —they need not sum to a finite number.5 Instead, we propose to minimize the KL divergence between the “expected” marginal distribution and the approximated “expected” marginal distribution: |

Message Approximation | The procedure for calculating these statistics is described in Li and Eisner (2009), which amounts to using an expectation semiring (Eisner, 2001) to compute expected transitions in 7' o 71* under the probability distribution 7' o ,u. |

probability distribution is mentioned in 4 sentences in this paper.

Topics mentioned in this paper:

- unigram (21)
- edit distances (14)
- bigram (9)

Attribute-based Classification | For each image iw E Iw of concept w, we output an F -dimensional vector containing prediction scores scorea(iw) for attributes a = 1, ...,F. We transform these attribute vectors into a single vector pw 6 [0,1]1XF, by computing the centroid of all vectors for concept w. The vector is normalized to obtain a probability distribution over attributes given w: |

Attribute-based Semantic Models | Let P E [0, 1]N XF denote a visual matrix, representing a probability distribution over visual attributes for each word. |

Experimental Setup | We can thus compute the probability distribution over associates for each cue. |

probability distribution is mentioned in 3 sentences in this paper.

Topics mentioned in this paper:

Count distributions | In the E step of EM, we compute a probability distribution (according to the current model) over all possible completions of the observed data, and the expected counts of all types, which may be fractional. |

Word Alignment | The IBM models and related models define probability distributions p(a, f | e, 6), which model how likely a French sentence f is to be generated from an English sentence e with word alignment a. |

Word Alignment | Different models parameterize this probability distribution in different ways. |

probability distribution is mentioned in 3 sentences in this paper.

Topics mentioned in this paper:

- language model (17)
- n-gram (7)
- unigram (7)

Introduction | In the second phase, a conditional probability distribution is estimated that describes the probability that a word was uttered given such event representations. |

Linguistic Mapping | We model this relationship, much like traditional language models, using conditional probability distributions . |

Linguistic Mapping | The model assumes that every document is made up of a mixture of topics, and that each word in a document is generated from a probability distribution associated with one of those topics. |

probability distribution is mentioned in 3 sentences in this paper.

Topics mentioned in this paper:

- language model (46)
- error rate (13)
- bigram (5)

Pairwise Markov Random Fields and Loopy Belief Propagation | and x to observed ones X (variables with known labels, if any), our objective function is associated with the following joint probability distribution |

Pairwise Markov Random Fields and Loopy Belief Propagation | A message mizj is sent from node i to node j and captures the belief of 2' about j, which is the probability distribution over the labels of j; i.e. |

Pairwise Markov Random Fields and Loopy Belief Propagation | what i “thinks” j’s label is, given the current label of i and the type of the edge that connects i and j. Beliefs refer to marginal probability distributions of nodes over labels; for example denotes the belief of node 2' having label 3),. |

probability distribution is mentioned in 3 sentences in this paper.

Topics mentioned in this paper:

- synsets (24)
- word-level (15)
- WordNet (11)

Introduction | important reason for the success of these models is the fact that they are lexicalized: the probability distributions are also conditioned on the actual words occuring in the utterance, and not only on their parts of speech. |

Language Model 2.1 The General Approach | P was modeled by means of a dedicated probability distribution for each conditioning tag. |

Language Model 2.1 The General Approach | The resulting probability distributions were trained on the German TIGER treebank which consists of about 50000 sentences of newspaper text. |

probability distribution is mentioned in 3 sentences in this paper.

Topics mentioned in this paper:

- language model (23)
- error rate (15)
- parse tree (10)

Background | MLNs define a probability distribution over possible worlds, where a world’s probability increases exponentially with the total weight of the logical clauses that it satisfies. |

Background | Given a set of weighted logical formulas, PSL builds a graphical model defining a probability distribution over the continuous space of values of the random variables in the model. |

Background | Using distance to satisfaction, PSL defines a probability distribution over all possible interpretations I of all ground atoms. |

probability distribution is mentioned in 3 sentences in this paper.

Topics mentioned in this paper:

- logical forms (6)
- semantic similarity (6)
- similarity score (6)

Distributional semantic models | The word2vec toolkit implements two efficient alternatives to the standard computation of the output word probability distributions by a softmax classifier. |

Distributional semantic models | Hierarchical softmax is a computationally efficient way to estimate the overall probability distribution using an output layer that is proportional to log(unigram.perplexity(W)) instead of W (for W the vocabulary size). |

Introduction | Allocation (LDA) models (Blei et al., 2003; Griffiths et al., 2007), where parameters are set to optimize the joint probability distribution of words and documents. |

probability distribution is mentioned in 3 sentences in this paper.

Topics mentioned in this paper:

Bilingual LDA Model | denotes the vocabulary probability distribution in the topic k; M denotes the document number; 6m |

Bilingual LDA Model | denotes the topic probability distribution in the document m; Nm denotes the length of m; me |

Introduction | Preiss (2012) transformed the source language topical model to the target language and classified probability distribution of topics in the same language, whose shortcoming is that the effect of model translation seriously hampers the comparable corpora quality. |

probability distribution is mentioned in 3 sentences in this paper.

Topics mentioned in this paper:

- LDA (9)
- topic model (5)
- Conditional Probability (3)

Simultaneous Optimization of All-words WSD | Shadow thickness and surface height represents the composite probability distribution of all the twelve kernels. |

Simultaneous Optimization of All-words WSD | The cluster centers are located at the means of hypotheses including miscellaneous alternatives not intended, thus the estimated probability distribution is, roughly speaking, offset toward the center of WordNet, which is not what we want. |

Smoothing Model | Figure 1: Proposed probability distribution model for context-to-sense mapping space. |

probability distribution is mentioned in 3 sentences in this paper.

Topics mentioned in this paper:

Background | The C&C supertagger is similar to the Ratnaparkhi (1996) tagger, using features based on words and POS tags in a five-word window surrounding the target word, and defining a local probability distribution over supertags for each word in the sentence, given the previous two supertags. |

Background | Alternatively the Forward-Backward algorithm can be used to efficiently sum over all sequences, giving a probability distribution over supertags for each word which is conditional only on the input sentence. |

Results | Note that these are all alternative methods for estimating the local log-linear probability distributions used by the Ratnaparkhi-style tagger. |

probability distribution is mentioned in 3 sentences in this paper.

Topics mentioned in this paper:

- CCG (13)
- F-score (10)
- baseline system (6)

Novel Estimator of Vocabulary size | sequence drawn according to a probability distribution P from a large, but finite, vocabulary 9. |

Novel Estimator of Vocabulary size | Our main interest is in probability distributions 1? |

Novel Estimator of Vocabulary size | In particular, the authors consider a sequence of vocabulary sets and probability distributions , indexed by the observation size n. Specifically, the observation (X 1, . |

probability distribution is mentioned in 3 sentences in this paper.

Topics mentioned in this paper:

Introduction | We investigate the use of distributional representations, which model the probability distribution of a word’s context, as techniques for finding smoothed representations of word sequences. |

Smoothing Natural Language Sequences | If V is the vocabulary, or the set of word types, and X is a sequence of random variables over V, the left and right context of Xi = 2) may each be represented as a probability distribution over V: P(XZ-_1|XZ- = v) and P(Xi+1|X = 2)) respectively. |

Smoothing Natural Language Sequences | We then normalize each vector to form a probability distribution . |

probability distribution is mentioned in 3 sentences in this paper.

Topics mentioned in this paper:

- distributional representations (18)
- POS tagging (16)
- CRF (11)

Vector space model adaptation | Thus, we get the probability distribution of a phrase pair or the phrase pairs in the dev data across all subcorpora: |

Vector space model adaptation | To further improve the similarity score, we apply absolute discounting smoothing when calculating the probability distributions p,( f, e). |

Vector space model adaptation | We carry out the same smoothing for the probability distributions pi(dev). |

probability distribution is mentioned in 3 sentences in this paper.

Topics mentioned in this paper:

- phrase pair (20)
- vector space (11)
- NIST (6)

Abstract | The log linear model is defined as a conditional probability distribution of a corrected word and a rule set for the correction conditioned on the misspelled word. |

Introduction | The log linear model is defined as a conditional probability distribution of a corrected word and a rule set for the correction given the misspelled word. |

Model for Candidate Generation | We define the conditional probability distribution of we and R(wm, we) given mm as the following log linear model: |

probability distribution is mentioned in 3 sentences in this paper.

Topics mentioned in this paper:

- word pair (13)
- conditional probability (6)
- edit distance (5)

Experimental Setup | Table 2: Key to probability distributions |

Experimental Setup | Table 2 is a key to the probability distributions we use. |

Introduction | Language models, probability distributions over strings of words, are fundamental to many applications in natural language processing. |

probability distribution is mentioned in 3 sentences in this paper.

Topics mentioned in this paper:

- bigram (25)
- unigram (23)
- language models (17)

Background | Estimating a conditional probability distribution gbk; = p( as a context profile for each 212,- falls into this case. |

Background | When the context profiles are probability distributions, we usually utilize the measures on probability distributions such as the Jensen-Shannon (J S) divergence to calculate similarities (Dagan et al., 1994; Dagan et al., 1997). |

Background | The BC is also a similarity measure on probability distributions and is suitable for our purposes as we describe in the next section. |

probability distribution is mentioned in 3 sentences in this paper.

Topics mentioned in this paper:

- similarity measure (25)
- hyperparameters (7)
- semantic similarity (7)