In this way, we incorporate rich context information of PAS for disambiguation.

The two methods successfully incorporate the rich context information into the translation process.

Specifically, after integrating the inside context information of PAS into transformation, we can see that system IC-PASTR significantly outperforms system PASTR by 0.71 BLEU points.

Conversely, by considering the context information , the PASTR+MEPD system chooses a correct rule for translation:

In this paper, we propose two novel methods to incorporate rich context information to handle PAS ambiguities.

In order to handle the role ambiguities, in this section, we concentrate on utilizing a maximum entropy model to incorporate the context information for PAS disambiguation.

The target-side-like PAS is selected only according to the language model and translation probabilities, without considering any context information of PAS.

They combine rich context information to do disambiguation for words or phrases, and achieve improved translation performance.

By incorporating the rich context information as features, they chose better rules for translation and yielded stable improvements on translation quality.

They also combine the context information in the model.

The feature we used includes characteristics of relation instance, phrase properties and context information (See Section 3 for details).

3.3 Context Information Feature

The context information of a phrase node is critical for identifying the role and the importance of a subtree in the whole relation instance.

Table 6: Impact of the contextual information .

3.5 Impact of the Contextual Information

In this paper, we translate the English questions into other four languages using Google Translate (GTrans), which takes into account contextual information during translation.

The basic idea is to capture the contextual information in modeling the translation of phrases as a whole, thus the word ambiguity problem is somewhat alleviated.

tions: (1) Contextual information is exploited during the translation from one language to another.

For example in Table 1, English words “interest” and “bank” that have multiple meanings under different contexts are correctly addressed by using the state-of-the-art translation tool — —Google Translate.4 Thus, word ambiguity based on contextual information is naturally involved when questions are translated.

Statistical machine translation (e.g., Google Translate) can utilize contextual information during the question translation, so it can solve the word ambiguity and word mismatch problems to some extent.

In this paper, we propose a general framework based on probabilistic inference to extract such context information from scientific papers.

Our experiments show greater pyramid scores for surveys generated using such context information rather than citation sentences alone.

Our experiments on generating surveys for Question Answering and Dependency Parsing show how surveys generated using such context information along with citation sentences have higher quality than those built using citations alone.

Our future goal is to combine summarization and bibliometric techniques towards building automatic surveys that employ context information as an important part of the generated surveys.

We refer to such implicit citations that contain information about a specific secondary source but do not explicitly cite it, as sentences with context information or context sentences for short.

In this section we propose our methodology that enables us to identify the context information of a cited paper.

To find the sentences from a paper that form the context information of a given cited paper, we build an MRF in which a hidden node :13,- and an observed node y,- correspond to each sentence.

Long distance reordering remains one of the greatest challenges in statistical machine translation research as the key contextual information may well be beyond the confine of translation units.

We presented a novel approach to address a kind of long-distance reordering that requires global cross-boundary contextual information .

Empirical results confirm our intuition that incorporating cross-boundaries contextual information improves translation quality.

As shown, the empirical results confirm our intuition that SMT can greatly benefit from reordering model that incorporate cross-unit contextual information .

Often, such reordering decisions require contexts that span across multiple translation units.1 Unfortunately, previous approaches fall short in capturing such cross-unit contextual information that could be

In this paper, we argue that reordering modeling would greatly benefit from richer cross-boundary contextual information

We introduce a reordering model that incorporates such contextual information , named the Two-Neighbor Orientation (TNO) model.

We also compared the performance of our model based on different contextual information , and show that both large-scale monolingual data and parallel bilingual data can be helpful to generate correct measure words.

Then, the collocation between measure words and head words and their surrounding contextual information are extracted to train the measure word selection models.

Then, contextual information within the windows in the source and the target sentence is extracted and fed to the measure word selection model.

We do not integrate our measure word generation module into the SMT decoder since there is only little target contextual information available during SMT decoding.

Based on contextual information contained in both input source sentence and SMT system’s output translation, a measure word candidate set M is constructed.

After obtaining the measure word candidate set M, a measure word selection model is employed to select the best one from M. Given the contextual information C in both source window and target

Rather than replacing single words in isolation, this model replaces sequences of words with sequences of words, thus incorporating contextual information .

Notice that when we set L=1, the phrase-based error model is reduced to a word-based error model which assumes that words are transformed independently from C to Q, without taking into account any contextual information .

Comparing to traditional error models that account for transformation probabilities between single characters (Kernighan et al., 1990) or sub-word strings (Brill and Moore, 2000), the phrase-based model is more powerful in that it captures some contextual information by retaining inter-term dependencies.

We show that this information is crucial to detect the correction of a query term, because unlike in regular written text, any query word can be a valid search term and in many cases the only way for a speller system to make the judgment is to explore its usage according to the contextual information .

Typically, a language model (source model) is used to capture contextual information, while an error model (channel model) is considered to be context free in that it does not take into account any contextual information in modeling word transformation probabilities.

In this study we argue that it is beneficial to capture contextual information in the error model.

Table 6: Contextual Information for Answer Detection.

Linear CRFs with contextual information perform better than those without context.

The results clearly shows that contextual information greatly improves the performance of answer detection.

As shown in the example, a forum question usually requires contextual information to provide background or constraints.

Moreover, it sometimes needs contextual information to provide explicit link to its answers.

We call contextual information the context of a question in this paper.

We also analyze how much each of our four main extensions (shape features, context information , noise, and first-order column dependencies) to EEA contributes to overall performance by ablating each in turn (also shown in Fig.

Our model can do better, since it makes use of context information and features, and it can put a person and an organization in one row even though they do not share common words.

It shows that in some cases context information is not adequate, and a possible improvement might be obtained by providing more context to the model.

We further incorporate context information and a notion of noise.

It is important to be able to se context information to determine which row mention should go into.

Our results show that encoding priors on words and context information contributes significantly to the performance of semantic clustering.

Rather than using single turn utterances, we hope to utilize the context information , e.g., information from previous turns for improving the performance of the semantic tagging of the current turns.

To include contextual information , we add binary features for all possible tags.

The results indicate that incorporating context information with MTR is an effective option for identifying semantic ambiguity.

A context-insensitive yet informed baseline was constructed to assess the impact of L2 context information in translating Ll fragments.

Nevertheless, we hope to show that our automated way of test set generation is sufficient to test the feasibility of our core hypothesis that L1 fragments can be translated to L2 using L2 context information .

The main research question in this research is how to disambiguate an L1 word or phrase to its L2 translation based on an L2 context, and whether such cross-lingual contextual approaches provide added value compared to baseline models that are not context informed or compared to standard language models.

If so, we are done quickly and need not rely on context information .

Another direction for further study will be the generalization of our model to larger syntactic contexts, including more than only the direct neighbors in the dependency graph, ultimately incorporating context information from the whole sentence in a recursive fashion.

3Note that the context information is the same for both words.

The main difference between verbs on the one hand, and nouns, adjectives, and adverbs on the other hand, is that verbs typically come with a rich context—subject, object, and so on—while non-verbs often have either no dependents at all or only closed class dependents such as determiners which provide only limited contextual informations , if any at all.

A more flexible approach than simple filtering, however, is to re-weight those dimensions with context information .

In order to take contextual information into account, a discriminative model is used on top of the parser to re—rank the n—best parse trees generated by the parser.

When there is enough labeled data, then a discriminative model can be trained on the labeled data to learn contextual information and to further enhance the tagging performance.

Contextual information often plays a big role in resolving tagging ambiguities and is one of the key benefits of discriminative models such as CRFs.

to take contextual information into account.

SSWE outperforms MVSA by exploiting more contextual information in the sentiment predictor function.

Among three sentiment-specific word embeddings, SSWEu captures more context information and yields best performance.

SSWE outperforms MVSA and ReEmb by exploiting more context information of words and sentiment information of sentences, respectively.

Rather than predicting word senses for ambiguous words, the reformulated WSD directly predicts target translations for source words with context information .

Lexical selection Our work is also related to lexical selection in SMT where appropriate target lexical items for source words are selected by a statistical model with context information (Bangalore et al., 2007; Mauser et al., 2009).

The sense-based translation model estimates the probability that a source word 0 is translated into a target phrase 6 given contextual information , including word senses that are obtained using the HDP-based WSI as described in the last section.

A pseudo document is composed of either a bag of neighboring words of a word token, or the Part-to-Speech tags of neighboring words, or other contextual information elements.

To make use of both word identity and context information of a given type, we use S-CODE co-occurrence modeling (Maron et al., 2010) as (Yatbaz et al., 2012) does.

In that experiment, POS induction is done by using word identities and context information represented by substitute words.

of a target word, we separate occurrences of the word into different groups depending on the context information represented by substitute vectors.

We aim to improve spoken term detection performance by incorporating contextual information beyond traditional N-gram language models.

We will show that by focusing on contextual information in the form of word repetition within documents, we obtain consistent improvement across five languages in the so called Base Phase of the IARPA BABEL program.

Clearly topic or context information is relevant to a retrieval type task, but we need a stable, consistent framework in which to apply it.

We first set up a human annotation of semantic links with or without contextual information to show the importance of the textual context in evaluating the relevance of semantic similarity, and to assess the prevalence of actual semantic relations between word tokens.

To verify that this methodology is useful, we did a preliminary annotation to contrast judgment on lexical pairs with or without this contextual information .

We present the experiments we set up to automatically filter semantic relations in context, with various groups of features that take into account information from the corpus used to build the thesaurus and contextual information related to occurrences of semantic neighbours 3).

As the stack of a state keeps changing during the decoding process, the context information needed to calculate dependency language model and maximum entropy model probabilities (e. g., root word, leftmost child, etc.)

As a result, the chance of risk-free hypothesis recombination (Koehn et al., 2003) significantly decreases because complicated contextual information is much less likely to be identical.

In the future, we plan to include more contextual information (e.g., the uncovered source phrases) in the maximum entropy model to resolve conflicts.

In addition, we will try to make better use of the context information when replacing paraphrase patterns in context sentences.

In Section 4.1, we have evaluated the precision of the paraphrase patterns without considering context information .

The context information was also considered by our judges.

CP enriches the representation of textual objects with typical contextual information that constrains or disambiguates their meaning, and provides matching functions that compare the preferences of objects involved in the inference.

Overall, such incorrect inferences may be avoided by considering contextual information for t, h and 7“ during their matching process.

In this framework, the representation of an object 2, where 2 may be a text, a template or an entailment rule, is enriched with contextual information denoted cp(z).