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We show that the newly proposed concept-distance measures outperform traditional [[ distributional word-distance measures ]] in the << tasks >> of -LRB- 1 -RRB- ranking word pairs in order of semantic distance , and -LRB- 2 -RRB- correcting real-word spelling errors .

We show that the newly proposed concept-distance measures outperform traditional distributional word-distance measures in the << tasks >> of -LRB- 1 -RRB- [[ ranking word pairs in order of semantic distance ]] , and -LRB- 2 -RRB- correcting real-word spelling errors .

We show that the newly proposed concept-distance measures outperform traditional distributional word-distance measures in the << tasks >> of -LRB- 1 -RRB- ranking word pairs in order of semantic distance , and -LRB- 2 -RRB- [[ correcting real-word spelling errors ]] .

We show that the newly proposed concept-distance measures outperform traditional distributional word-distance measures in the tasks of -LRB- 1 -RRB- << ranking word pairs in order of semantic distance >> , and -LRB- 2 -RRB- [[ correcting real-word spelling errors ]] .

In the latter [[ task ]] , of all the << WordNet-based measures >> , only that proposed by Jiang and Conrath outperforms the best distributional concept-distance measures .

In the latter [[ task ]] , of all the WordNet-based measures , only that proposed by Jiang and Conrath outperforms the best << distributional concept-distance measures >> .

In the latter task , of all the << WordNet-based measures >> , only that proposed by Jiang and Conrath outperforms the best [[ distributional concept-distance measures ]] .

One of the main results of this work is the definition of a relation between [[ broad semantic classes ]] and << LCS meaning components >> .

Our [[ acquisition program - LEXICALL - ]] takes , as input , the result of previous work on verb classification and thematic grid tagging , and outputs << LCS representations >> for different languages .

Our << acquisition program - LEXICALL - >> takes , as input , the result of previous work on [[ verb classification ]] and thematic grid tagging , and outputs LCS representations for different languages .

Our acquisition program - LEXICALL - takes , as input , the result of previous work on [[ verb classification ]] and << thematic grid tagging >> , and outputs LCS representations for different languages .

Our << acquisition program - LEXICALL - >> takes , as input , the result of previous work on verb classification and [[ thematic grid tagging ]] , and outputs LCS representations for different languages .

These [[ representations ]] have been ported into << English , Arabic and Spanish lexicons >> , each containing approximately 9000 verbs .

We are currently using these [[ lexicons ]] in an << operational foreign language tutoring >> and machine translation .

We are currently using these [[ lexicons ]] in an operational foreign language tutoring and << machine translation >> .

We are currently using these lexicons in an [[ operational foreign language tutoring ]] and << machine translation >> .

The theoretical study of the [[ range concatenation grammar -LSB- RCG -RSB- formalism ]] has revealed many attractive properties which may be used in << NLP >> .

In particular , << range concatenation languages -LSB- RCL -RSB- >> can be parsed in [[ polynomial time ]] and many classical grammatical formalisms can be translated into equivalent RCGs without increasing their worst-case parsing time complexity .

In particular , range concatenation languages -LSB- RCL -RSB- can be parsed in polynomial time and many classical << grammatical formalisms >> can be translated into equivalent RCGs without increasing their [[ worst-case parsing time complexity ]] .

For example , after translation into an equivalent RCG , any << tree adjoining grammar >> can be parsed in [[ O -LRB- n6 -RRB- time ]] .

In this paper , we study a [[ parsing technique ]] whose purpose is to improve the practical efficiency of << RCL parsers >> .

The non-deterministic parsing choices of the [[ main parser ]] for a << language L >> are directed by a guide which uses the shared derivation forest output by a prior RCL parser for a suitable superset of L .

The non-deterministic parsing choices of the main parser for a language L are directed by a guide which uses the << shared derivation forest >> output by a prior [[ RCL parser ]] for a suitable superset of L .

The results of a practical evaluation of this << method >> on a [[ wide coverage English grammar ]] are given .

In this paper we introduce [[ Ant-Q ]] , a family of algorithms which present many similarities with Q-learning -LRB- Watkins , 1989 -RRB- , and which we apply to the solution of << symmetric and asym-metric instances of the traveling salesman problem -LRB- TSP -RRB- >> .

<< Ant-Q algorithms >> were inspired by work on the [[ ant system -LRB- AS -RRB- ]] , a distributed algorithm for combinatorial optimization based on the metaphor of ant colonies which was recently proposed in -LRB- Dorigo , 1992 ; Dorigo , Maniezzo and Colorni , 1996 -RRB- .

Ant-Q algorithms were inspired by work on the [[ ant system -LRB- AS -RRB- ]] , a << distributed algorithm >> for combinatorial optimization based on the metaphor of ant colonies which was recently proposed in -LRB- Dorigo , 1992 ; Dorigo , Maniezzo and Colorni , 1996 -RRB- .

Ant-Q algorithms were inspired by work on the ant system -LRB- AS -RRB- , a [[ distributed algorithm ]] for << combinatorial optimization >> based on the metaphor of ant colonies which was recently proposed in -LRB- Dorigo , 1992 ; Dorigo , Maniezzo and Colorni , 1996 -RRB- .

We show that [[ AS ]] is a particular instance of the << Ant-Q family >> , and that there are instances of this family which perform better than AS .

We show that AS is a particular instance of the Ant-Q family , and that there are [[ instances ]] of this << family >> which perform better than AS .

We show that AS is a particular instance of the Ant-Q family , and that there are [[ instances ]] of this family which perform better than << AS >> .

We experimentally investigate the functioning of Ant-Q and we show that the results obtained by [[ Ant-Q ]] on << symmetric TSP >> 's are competitive with those obtained by other heuristic approaches based on neural networks or local search .

We experimentally investigate the functioning of Ant-Q and we show that the results obtained by [[ Ant-Q ]] on symmetric TSP 's are competitive with those obtained by other << heuristic approaches >> based on neural networks or local search .

We experimentally investigate the functioning of Ant-Q and we show that the results obtained by Ant-Q on symmetric TSP 's are competitive with those obtained by other << heuristic approaches >> based on [[ neural networks ]] or local search .

We experimentally investigate the functioning of Ant-Q and we show that the results obtained by Ant-Q on symmetric TSP 's are competitive with those obtained by other heuristic approaches based on [[ neural networks ]] or << local search >> .

We experimentally investigate the functioning of Ant-Q and we show that the results obtained by Ant-Q on symmetric TSP 's are competitive with those obtained by other << heuristic approaches >> based on neural networks or [[ local search ]] .

Finally , we apply [[ Ant-Q ]] to some difficult << asymmetric TSP >> 's obtaining very good results : Ant-Q was able to find solutions of a quality which usually can be found only by very specialized algorithms .

In this paper , we develop a [[ geometric framework ]] for << linear or nonlinear discriminant subspace learning and classification >> .

In our framework , the << structures of classes >> are conceptualized as a [[ semi-Riemannian manifold ]] which is considered as a submanifold embedded in an ambient semi-Riemannian space .

In our framework , the structures of classes are conceptualized as a semi-Riemannian manifold which is considered as a [[ submanifold ]] embedded in an << ambient semi-Riemannian space >> .

The << class structures >> of original samples can be characterized and deformed by [[ local metrics of the semi-Riemannian space ]] .

<< Semi-Riemannian metrics >> are uniquely determined by the [[ smoothing of discrete functions ]] and the nullity of the semi-Riemannian space .

Semi-Riemannian metrics are uniquely determined by the [[ smoothing of discrete functions ]] and the << nullity of the semi-Riemannian space >> .

<< Semi-Riemannian metrics >> are uniquely determined by the smoothing of discrete functions and the [[ nullity of the semi-Riemannian space ]] .

Based on the geometrization of class structures , optimizing << class structures >> in the [[ feature space ]] is equivalent to maximizing the quadratic quantities of metric tensors in the semi-Riemannian space .

Based on the geometrization of class structures , optimizing class structures in the feature space is equivalent to maximizing the << quadratic quantities of metric tensors >> in the [[ semi-Riemannian space ]] .

Based on the proposed [[ framework ]] , a novel << algorithm >> , dubbed as Semi-Riemannian Discriminant Analysis -LRB- SRDA -RRB- , is presented for subspace-based classification .

Based on the proposed framework , a novel [[ algorithm ]] , dubbed as Semi-Riemannian Discriminant Analysis -LRB- SRDA -RRB- , is presented for << subspace-based classification >> .

The performance of [[ SRDA ]] is tested on face recognition -LRB- singular case -RRB- and handwritten capital letter classification -LRB- nonsingular case -RRB- against existing << algorithms >> .

The performance of << SRDA >> is tested on [[ face recognition -LRB- singular case ]] -RRB- and handwritten capital letter classification -LRB- nonsingular case -RRB- against existing algorithms .

The performance of SRDA is tested on [[ face recognition -LRB- singular case ]] -RRB- and << handwritten capital letter classification -LRB- nonsingular case -RRB- >> against existing algorithms .

The performance of SRDA is tested on [[ face recognition -LRB- singular case ]] -RRB- and handwritten capital letter classification -LRB- nonsingular case -RRB- against existing << algorithms >> .

The performance of << SRDA >> is tested on face recognition -LRB- singular case -RRB- and [[ handwritten capital letter classification -LRB- nonsingular case -RRB- ]] against existing algorithms .

The performance of SRDA is tested on face recognition -LRB- singular case -RRB- and [[ handwritten capital letter classification -LRB- nonsingular case -RRB- ]] against existing << algorithms >> .

The experimental results show that [[ SRDA ]] works well on << recognition >> and classification , implying that semi-Riemannian geometry is a promising new tool for pattern recognition and machine learning .

The experimental results show that [[ SRDA ]] works well on recognition and << classification >> , implying that semi-Riemannian geometry is a promising new tool for pattern recognition and machine learning .

The experimental results show that SRDA works well on [[ recognition ]] and << classification >> , implying that semi-Riemannian geometry is a promising new tool for pattern recognition and machine learning .

The experimental results show that SRDA works well on recognition and classification , implying that [[ semi-Riemannian geometry ]] is a promising new tool for << pattern recognition >> and machine learning .

The experimental results show that SRDA works well on recognition and classification , implying that [[ semi-Riemannian geometry ]] is a promising new tool for pattern recognition and << machine learning >> .

The experimental results show that SRDA works well on recognition and classification , implying that semi-Riemannian geometry is a promising new tool for [[ pattern recognition ]] and << machine learning >> .

A [[ deterministic parser ]] is under development which represents a departure from traditional << deterministic parsers >> in that it combines both symbolic and connectionist components .

A deterministic parser is under development which represents a departure from traditional deterministic parsers in that << it >> combines both [[ symbolic and connectionist components ]] .

The << connectionist component >> is trained either from [[ patterns ]] derived from the rules of a deterministic grammar .

The connectionist component is trained either from << patterns >> derived from the [[ rules of a deterministic grammar ]] .

The development and evolution of such a [[ hybrid architecture ]] has lead to a << parser >> which is superior to any known deterministic parser .

The development and evolution of such a hybrid architecture has lead to a [[ parser ]] which is superior to any known << deterministic parser >> .

Experiments are described and powerful [[ training techniques ]] are demonstrated that permit << decision-making >> by the connectionist component in the parsing process .

Experiments are described and powerful training techniques are demonstrated that permit << decision-making >> by the [[ connectionist component ]] in the parsing process .

Experiments are described and powerful training techniques are demonstrated that permit decision-making by the [[ connectionist component ]] in the << parsing process >> .

Data are presented which show how a [[ connectionist -LRB- neural -RRB- network ]] trained with linguistic rules can parse both << expected -LRB- grammatical -RRB- sentences >> as well as some novel -LRB- ungrammatical or lexically ambiguous -RRB- sentences .

Data are presented which show how a [[ connectionist -LRB- neural -RRB- network ]] trained with linguistic rules can parse both expected -LRB- grammatical -RRB- sentences as well as some novel << -LRB- ungrammatical or lexically ambiguous -RRB- sentences >> .

Data are presented which show how a << connectionist -LRB- neural -RRB- network >> trained with [[ linguistic rules ]] can parse both expected -LRB- grammatical -RRB- sentences as well as some novel -LRB- ungrammatical or lexically ambiguous -RRB- sentences .

Data are presented which show how a connectionist -LRB- neural -RRB- network trained with linguistic rules can parse both [[ expected -LRB- grammatical -RRB- sentences ]] as well as some novel << -LRB- ungrammatical or lexically ambiguous -RRB- sentences >> .

Robust << natural language interpretation >> requires strong [[ semantic domain models ]] , fail-soft recovery heuristics , and very flexible control structures .

Robust natural language interpretation requires strong [[ semantic domain models ]] , << fail-soft recovery heuristics >> , and very flexible control structures .

Robust << natural language interpretation >> requires strong semantic domain models , [[ fail-soft recovery heuristics ]] , and very flexible control structures .

Robust natural language interpretation requires strong semantic domain models , [[ fail-soft recovery heuristics ]] , and very flexible << control structures >> .

Robust << natural language interpretation >> requires strong semantic domain models , fail-soft recovery heuristics , and very flexible [[ control structures ]] .

Although [[ single-strategy parsers ]] have met with a measure of success , a << multi-strategy approach >> is shown to provide a much higher degree of flexibility , redundancy , and ability to bring task-specific domain knowledge -LRB- in addition to general linguistic knowledge -RRB- to bear on both grammatical and ungrammatical input .

Although single-strategy parsers have met with a measure of success , a multi-strategy approach is shown to provide a much higher degree of flexibility , redundancy , and ability to bring [[ task-specific domain knowledge ]] -LRB- in addition to << general linguistic knowledge >> -RRB- to bear on both grammatical and ungrammatical input .

A << parsing algorithm >> is presented that integrates several different [[ parsing strategies ]] , with case-frame instantiation dominating .

A parsing algorithm is presented that integrates several different << parsing strategies >> , with [[ case-frame instantiation ]] dominating .

Each of these [[ parsing strategies ]] exploits different types of knowledge ; and their combination provides a strong framework in which to process << conjunctions >> , fragmentary input , and ungrammatical structures , as well as less exotic , grammatically correct input .

Each of these [[ parsing strategies ]] exploits different types of knowledge ; and their combination provides a strong framework in which to process conjunctions , << fragmentary input >> , and ungrammatical structures , as well as less exotic , grammatically correct input .

Each of these [[ parsing strategies ]] exploits different types of knowledge ; and their combination provides a strong framework in which to process conjunctions , fragmentary input , and << ungrammatical structures >> , as well as less exotic , grammatically correct input .

Each of these [[ parsing strategies ]] exploits different types of knowledge ; and their combination provides a strong framework in which to process conjunctions , fragmentary input , and ungrammatical structures , as well as less << exotic , grammatically correct input >> .

Each of these parsing strategies exploits different types of knowledge ; and their combination provides a strong framework in which to process [[ conjunctions ]] , << fragmentary input >> , and ungrammatical structures , as well as less exotic , grammatically correct input .

Each of these parsing strategies exploits different types of knowledge ; and their combination provides a strong framework in which to process conjunctions , [[ fragmentary input ]] , and << ungrammatical structures >> , as well as less exotic , grammatically correct input .

Each of these parsing strategies exploits different types of knowledge ; and their combination provides a strong framework in which to process conjunctions , fragmentary input , and [[ ungrammatical structures ]] , as well as less << exotic , grammatically correct input >> .

Several [[ specific heuristics ]] for handling << ungrammatical input >> are presented within this multi-strategy framework .

Several [[ specific heuristics ]] for handling ungrammatical input are presented within this << multi-strategy framework >> .

Recently , [[ Stacked Auto-Encoders -LRB- SAE -RRB- ]] have been successfully used for << learning imbalanced datasets >> .

In this paper , for the first time , we propose to use a [[ Neural Network classifier ]] furnished by an SAE structure for detecting the errors made by a strong << Automatic Speech Recognition -LRB- ASR -RRB- system >> .

In this paper , for the first time , we propose to use a << Neural Network classifier >> furnished by an [[ SAE structure ]] for detecting the errors made by a strong Automatic Speech Recognition -LRB- ASR -RRB- system .

[[ Error detection ]] on an << automatic transcription >> provided by a '' strong '' ASR system , i.e. exhibiting a small word error rate , is difficult due to the limited number of '' positive '' examples -LRB- i.e. words erroneously recognized -RRB- available for training a binary classi-fier .

In this paper we investigate and compare different types of [[ classifiers ]] for << automatically detecting ASR errors >> , including the one based on a stacked auto-encoder architecture .

In this paper we investigate and compare different types of << classifiers >> for automatically detecting ASR errors , including the [[ one ]] based on a stacked auto-encoder architecture .

In this paper we investigate and compare different types of classifiers for automatically detecting ASR errors , including the << one >> based on a [[ stacked auto-encoder architecture ]] .

We show the effectiveness of the latter by measuring and comparing performance on the << automatic transcriptions >> of an [[ English corpus ]] collected from TED talks .

We show the effectiveness of the latter by measuring and comparing performance on the automatic transcriptions of an << English corpus >> collected from [[ TED talks ]] .