This paper presents and evaluates several original techniques for the latent classification of biographic attributes such as gender, age and native language , in diverse genres (conversation transcripts, email) and languages (Arabic, English).

(including true speaker gender, age, native language , etc.)

Corpus details for Age and Native Language : For age, we used the same training and test speakers from Fisher corpus as explained for gender in section 3 and binarized into greater-than or less-than-or-equal-to 40 for more parallel binary evaluation.

Based on the prior distribution, always guessing the most likely class for age ( age less-than-or—equal-to 40) results in 62.59% accuracy and always guessing the most likely class for native language (nonnative) yields 50.59% accuracy.

Speaker attributes such as gender, age, dialect, native language and educational level may be (a) stated overtly in metadata, (b) derivable indirectly from metadata such as a speaker’s phone number or userid, or (c) derivable from acoustic properties of the speaker, including pitch and f0 contours (Bocklet et al., 2008).

In this paper, we present a reinforcement learning approach for mapping natural language instructions to sequences of executable actions.

The problem of interpreting instructions written in natural language has been widely studied since the early days of artificial intelligence (Winograd, 1972; Di Eugenio, 1992).

This form of supervision allows us to learn interpretations of natural language instructions when standard supervised techniques are not applicable, due to the lack of human-created annotations.

Our formulation is unique in how it represents natural language in the reinforcement learning framework.

These systems converse with a human user by taking actions that emit natural language utterances.

Similar studies in parsing natural language sen-

Context-free phrase structure grammars are widely used for parsing natural language .

There are however natural languages that are free word order.

Although very intuitive, ID/LP rules are not widely used in the area of natural language processing.

This paper investigates the class of Tree-Tuple MCTAG with Shared Nodes, TT-MCTAG for short, an extension of Tree Adjoining Grammars that has been proposed for natural language processing, in particular for dealing with discontinuities and word order variation in languages such as German.

Some others generate only polynomial languages but their generative capacity is too limited to deal with all natural language phenomena.

However, from a first inspection of the MCTAG analyses proposed for natural languages (see Chen-Main and Joshi (2007) for an overview), it seems that there are no important natural language phenomena that can be described by LCFRS and not by TT-MCTAG.

As a result, one obtains a slight degree of locality that can be exploited for natural language phenomena that are unbounded only in a limited domain.

For natural language words and

Over the past decade, supervised learning algorithms have gained widespread acceptance in natural language processing (NLP).

The long-tailed distribution of natural language words implies that most of the word types will be either unseen or seen very few times in the labeled training data, even if the data set is a relatively large one (e. g., the Penn Treebank).

Out of context, natural language words are often ambiguous.

Using textual entailment analysis, we obtain entailment scores between a natural language question posed by the user and the candidate sentences returned from search engine.

Open domain natural language question answering (QA) is a process of automatically finding answers to questions searching collections of text files.

Recent research indicates that using labeled and unlabeled data in semi-supervised learning (SSL) environment, with an emphasis on graph-based methods, can improve the performance of information extraction from data for tasks such as question classification (Tri et al., 2006), web classification (Liu et al., 2006), relation extraction (Chen et al., 2006), passage-retrieval (Otterbacher et al., 2009), various natural language processing tasks such as part-of-speech tagging, and named-entity recognition (Suzuki and Isozaki, 2008), word-sense disam-

Through an investigation of Category-2 non-English queries, we find out that this is mostly due to the usage of some common internet or computer terms such as ”download”, ”software”, ”flash player”, among other native language query terms.

The language identification problem refers to the task of deciding in which natural language a given text is written.

Although the problem is heavily studied by the Natural Language Processing community, most of the research carried out to date has been concerned with relatively long texts such as articles or web pages which usually contain enough text for the systems built for this task to reach almost perfect accuracy.

Learning natural language in an unsupervised way commonly involves the expectation-maximization (EM) algorithm to optimize the parameters of a generative model, often a probabilistic grammar (Pereira and Schabes, 1992).

Later approaches include variational EM in a Bayesian setting (Beal and Gharamani, 2003), which has been shown to obtain even better results for various natural language tasks over EM (e.g., Cohen et al., 2008).

For example, Smith and Eisner (2006) have penalized the approximate posterior over dependency structures in a natural language grammar induction task to avoid long range dependencies between words.

We present a new approach to learning a semantic parser (a system that maps natural language sentences into logical form).

The resulting system produces improved results on standard corpora on natural language interfaces for database querying and simulated robot control.

Semantic parsing is the task of mapping a natural language (NL) sentence into a completely formal meaning representation (MR) or logical form.

Some businesses have recently allowed users to formulate queries in natural language using SMS.

These systems generally adopt one of the following three approaches: Human intervention based, Information Retrieval based, or Natural language processing based.

The natural language processing based system tries to fully parse a question to discover semantic structure and then apply logic to formulate the answer (Molla et al., 2003).