Another is a web-scale N-gram corpus, which is a N-gram corpus with N-grams of length 1-5 (Brants and Franz, 2006), we call it Google V1 in this paper.

one is web, N-gram counts are approximated by Google hits.

This N-gram corpus records how often each unique sequence of words occurs.

times or more (1 in 25 billion) are kept, and appear in the n-gram tables.

Each n-gram is a sequence of words, POS tags or a combination of words and POS tags

To illustrate, consider the following feature set, a bigram and a trigram (each term in the n-gram either has the form word or Atag):

The first n-gram of the set, please AVB, would match pleaseARB bringAVB from the text.

The n-gram language model is essentially a word predictor that given its entire document history it predicts next word wk+1 based on the last n-l words with probability p(wk+1|w,’:_n+2) where w’g_n+2 = wk—n+27'°' 710k:-

The SLM (Chelba and J elinek, 1998; Chelba and Jelinek, 2000) uses syntactic information beyond the regular n-gram models to capture sentence level long range dependencies.

When combining n-gram , m order SLM and

The m-SLM performs competitively with its counterpart n-gram (n=m+1) on large scale corpus.

The Markov chain ( n-gram ) source models, which predict each word on the basis of previous n-l words, have been the workhorses of state-of—the-art speech recognizers and machine translators that help to resolve acoustic or foreign language ambiguities by placing higher probability on more likely original underlying word strings.

are efficient at encoding local word interactions, the n-gram model clearly ignores the rich syntactic and semantic structures that constrain natural languages.

(2006) integrated n-gram , structured language model (SLM) (Chelba and Jelinek, 2000) and probabilistic latent semantic analysis (PLSA) (Hofmann, 2001) under the directed MRF framework (Wang et al., 2005) and studied the stochastic properties for the composite language model.

Similar to SLM (Chelba and Jelinek, 2000), we adopt an N -best list approximate EM re-estimation with modular modifications to seamlessly incorporate the effect of n-gram and PLSA components.

This implies that when computing the language model probability of a sentence in a client, all servers need to be contacted for each n-gram request.

(2007) follows a standard MapReduce paradigm (Dean and Ghemawat, 2004): the corpus is first divided and loaded into a number of clients, and n-gram counts are collected at each client, then the n-gram counts mapped and stored in a number of servers, resulting in exactly one server being contacted per n-gram when computing the language model probability of a sentence.

Drawing on earlier successes in speech recognition, research in statistical machine translation has effectively used n-gram word sequence models as language models.

Modern phrase-based translation using large scale n-gram language models generally performs well in terms of lexical choice, but still often produces ungrammatical output.

(2007) use supertag n-gram LMs.

An n-gram language model history is also maintained at each node in the translation lattice.

The search space is further trimmed with hypothesis recombination, which collapses lattice nodes that share a common coverage vector and n-gram state.

We compare our error rates to the state-of-the-art and to a strong Google n-gram count baseline.

We attain a maximum error reduction of 69.8% and average error reduction across all test sets of 59.1% compared to the state-of-the-art and a maximum error reduction of 68.4% and average error reduction across all test sets of 41.8% compared to our Google n-gram count baseline.

We keep a table mapping each unique n-gram to the number of times it has been seen in the training data.

4.2 Google n-gram Baseline

The Google n-gram corpus is a collection of n-gram counts drawn from public webpages with a total of one trillion tokens — around 1 billion each of unique 3-grams, 4—grams, and 5-grams, and around 300,000 unique bigrams.

MAXENT also outperforms the GOOGLE N-GRAM baseline for almost all test corpora and sequence lengths.

For the Switchboard test corpus token and type accuracies, the GOOGLE N-GRAM baseline is more accurate than MAXENT for sequences of length 2 and overall, but the accuracy of MAXENT is competitive with that of GOOGLE N-GRAM .

MAXENT also attains a maximum error reduction of 68.4% for the WSJ test corpus and an average error reduction of 41.8% when compared to GOOGLE N-GRAM .

Our most compact representation can store all 4 billion n-grams and associated counts for the Google n-gram corpus in 23 bits per n-gram , the most compact lossless representation to date, and even more compact than recent lossy compression techniques.

and Raj, 2001; Hsu and Glass, 2008), our methods are conceptually based on tabular trie encodings wherein each n-gram key is stored as the concatenation of one word (here, the last) and an offset encoding the remaining words (here, the context).

For an n-gram language model, we can apply this implementation with a slight modification: we need n sorted arrays, one for each n-gram order.

Because our keys are sorted according to their context-encoded representation, we cannot straightforwardly answer queries about an n-gram w without first determining its context encoding.

Our goal in this paper is to provide data structures that map n-gram keys to values, i.e.

However, because of the sheer number of keys and values needed for n-gram language modeling, generic implementations do not work efficiently “out of the box.” In this section, we will review existing techniques for encoding the keys and values of an n-gram language model, taking care to account for every bit of memory required by each implementation.

In the WeblT corpus, the most frequent n-gram occurs about 95 billion times.

Unlike previous approaches, our method combines information from letter n-gram language models and word dictionaries and provides a robust decipherment model.

Combining letter n-gram language models with word dictionaries: Many existing probabilistic approaches use statistical letter n-gram language models of English to assign P (p) probabilities to plaintext hypotheses during decipherment.

3We set the interpolation weights for the word and n-gram LM as (0.9, 0.1).

We train the letter n-gram LM on 50 million words of English text available from the Linguistic Data Consortium.

EM Method using letter n-gram LMs following the approach of Knight et al.

0 Letter n-gram versus W0rd+n-gram LMs—Figure 2 shows that using a word+3-gram LM instead of a 3-gram LM results in +75% improvement in decipherment accuracy.

(2006) use the Expectation Maximization (EM) algorithm (Dempster et al., 1977) to search for the best probabilistic key using letter n-gram models.

Ravi and Knight (2008) formulate decipherment as an integer programming problem and provide an exact method to solve simple substitution ciphers by using letter n-gram models along with deterministic key constraints.

0 Our new method combines information from word dictionaries along with letter n-gram models, providing a robust decipherment model which offsets the disadvantages faced by previous approaches.

The mid-words m which rank highly are those where the occurrence of hma as an n-gram is a good indicator that a attaches to h (m of course does not have to actually occur in the sentence).

However, we additionally find other cues, most notably that if the N IN sequence occurs following a capitalized determiner, it tends to indicate a nominal attachment (in the n-gram , the preposition cannot attach leftward to anything else because of the beginning of the sentence).

These features essentially say that if two heads ml and 2122 occur in the direct coordination n-gram ml and wg, then they are good heads to coordinate (coordination unfortunately looks the same as complementation or modification to a basic dependency model).

(2010), which use Web-scale n-gram counts for multi-way noun bracketing decisions, though that work considers only sequences of nouns and uses only affinity-based web features.

Lapata and Keller (2004) uses the number of page hits as the web-count of the queried n-gram (which is problematic according to Kilgarriff (2007)).

Rather than working through a search API (or scraper), we use an offline web corpus — the Google n-gram corpus (Brants and Franz, 2006) — which contains English n-grams (n = l to 5) and their observed frequency counts, generated from nearly 1 trillion word tokens and 95 billion sentences.

Our system requires the counts from a large collection of these n-gram queries (around 4.5 million).

For P (e), we use a word n-gram LM trained on monolingual English data.

Whole-segment Language Models: When using word n-gram models of English for decipherment, we find that some of the foreign sentences are decoded into sequences (such as “THANK YOU TALKING ABOUT ‘2”) that are not good English.

This stems from the fact that n-gram LMs have no global information about what constitutes a valid English segment.

We model P(e) using a statistical word n-gram English language model (LM).

Our method holds several other advantages over the EM approach—(l) inference using smart sampling strategies permits efficient training, allowing us to scale to large data/vocabulary sizes, (2) incremental scoring of derivations during sampling allows efficient inference even when we use higher-order n-gram LMs, (3) there are no memory bottlenecks since the full channel model and derivation lattice are never instantiated during training, and (4) prior specification allows us to learn skewed distributions that are useful here—word substitution ciphers exhibit l-to-l correspondence between plaintext and cipher types.

build an English word n-gram LM, which is used in the decipherment process.

In contrast to the other strategies just discussed, our text categorization approach to deception detection allows us to model both content and context with n-gram features.

Specifically, we consider the following three n-gram feature sets, with the corresponding features lowercased and unstemmed: UNIGRAMS, BIGRAMS+, TRIGRAMS+, where the superscript + indicates that the feature set subsumes the preceding feature set.

We consider all three n-gram feature sets, namely UNIGRAMS, BIGRAMS+, and TRIGRAMS+, with corresponding language models smoothed using the interpolated Kneser-Ney method (Chen and Goodman, 1996).

Notably, a combined classifier with both n-gram and psychological deception features achieves nearly 90% cross-validated accuracy on this task.

Surprisingly, models trained only on UNIGRAMS—the simplest n-gram feature set—outperform all non—text-categorization approaches, and models trained on BIGRAMSJr perform even better (one-tailed sign test p = 0.07).

For our character n-gram experiments, we obtained LOWBOW representations for character 3-grams (only n-grams of size n = 3 were used) considering the 2, 500 most common n-grams.

Also, n-gram information is more dense in documents than word-level information.

(2003) propose the use of language models at the n-gram character-level for AA, whereas Keselj et al.

on characters at the n-gram level (Plakias and Stamatatos, 2008a).

Acceptable performance in AA has been reported with character n-gram representations.

The highly parallel nature of this data allows us to use simple n-gram comparisons to measure both the semantic adequacy and lexical dissimilarity of paraphrase candidates.

Thus, no n-gram overlaps are required to determine the semantic adequacy of the paraphrase candidates.

In essence, it is the inverse of BLEU since we want to minimize the number of n-gram overlaps between the two sentences.

where N is the maximum n-gram considered and n-

As machine translation systems improve in lexical choice and fluency, the shortcomings of widespread n-gram based, fluency-oriented MT evaluation metrics such as BLEU, which fail to properly evaluate adequacy, become more apparent.

N-gram based metrics assume that “good” translations tend to share the same leXical choices as the reference translations.

As MT systems improve, the shortcomings of the n-gram based evaluation metrics are becoming more apparent.

SEG-I method requires an access to a large web n-gram corpus (Brants and Franz, 2006).

where SQ is the set of all possible query segmenta-tions, 8 is a possible segmentation, s is a segment in S, and count(s) is the frequency of s in the web n-gram corpus.

(2009), and include, among others, n-gram frequencies in a sample of a query log, web corpus and Wikipedia titles.

(2010), an estimate of p(Cz-|7“) is a smoothed estimator that combines the information from the retrieved sentence 7“ with the information about unigrams (for capitalization and POS tagging) and bigrams (for segmentation) from a large n-gram corpus (Brants and Franz, 2006).

In our approach, n-gram sub-sequences of transitions per term in the discourse role matrix then constitute the more fine-grained evidence used in our model to distinguish coherence from incoherence.

tion of the n-gram discourse relation transition sequences in gold standard coherent text, and a similar one for incoherent text.

In our pilot work where we implemented such a basic model with n-gram features for relation transitions, the performance was very poor.

By definition (Lin, 2004), ROUGE-N is the n-gram recall between a candidate summary and a set of reference summaries.

Precisely, let S be the candidate summary (a set of sentences extracted from the ground set V), Ce : 2V —> Z+ be the number of times n-gram 6 occurs in summary 8, and R,- be the set of n-grams contained in the reference summary i (suppose we have K reference summaries, i.e., i = 1, - - - ,K).

where T6,, is the number of times n-gram 6 occurs in reference summary 2'.

The second step is to integrate an n-gram language model with this hypergraph.

The decoding problem for a broad range of these systems (e.g., (Chiang, 2005; Marcu et al., 2006; Shen et al., 2008)) corresponds to the intersection of a (weighted) hypergraph with an n-gram language model.1 The hypergraph represents a large set of possible translations, and is created by applying a synchronous grammar to the source language string.

The language model is then uwdwmmmemMMmhmmMnmmwmmmmr Decoding with these models is challenging, largely because of the cost of integrating an n-gram language model into the search process.

The N-gram approximation of the joint probability can be defined in terms of multigrams qi as:

N-gram models of order > 1 did not work well because these models tended to learn noise (information from non-transliteration pairs) in the training data.

(2010) submitted another system based on a standard n-gram kernel which ranked first for the English/Hindi and English/Tamil tasks.6 For the English/Arabic task, the transliteration mining system of Noeman and Madkour (2010) was best.

Standard n-gram features are also used as features.4 The feature model is built as follows: for every lemma in the f-structure, we extract a set of morphological properties (definiteness, person, pronominal status etc.

use several standard measures: a) exact match: how often does the model select the original corpus sentence, b) BLEU: n-gram overlap between top-ranked and original sentence, c) NIST: modification of BLEU giving more weight to less frequent n-grams.

The first widely known data-driven approach to surface realisation, or tactical generation, (Langk—ilde and Knight, 1998) used language-model n-gram statistics on a word lattice of candidate realisations to guide a ranker.