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In the past, Maximum Entropy based language models were constrained by training data n-gram counts, topic estimates, and triggers. We will investigate the obtainable gains from imposing additional constraints related to linguistic clusters, such as parts of speech, semantic/syntactic word clusters, and semantic labels. It will be shown that there substantial profit is available provided the estimates use Gaussian a priori statistics.

In his LREC 2004 invited talk when awarded by the first ever Antonio Zampolli prize for his essential contributions to the use of spoken and written language resources, Frederick Jelinek has used the title “Some of My Best Friends Are Linguists”. He did so for many reasons, one of them being that he wanted to remove the perception that he dislikes linguists and linguistics after so many people used to cite his famous line from an old presentation at a Natural Language Processing Evaluation workshop in 1988, in which he said “Whenever I fire a linguist our system performance improves.”

We start with a brief overview of our work in speech recognition and understanding which led from monomodal (speech only) human-machine dialog to multimodal human-machine interaction and assistance. Our work in speech communication initially had the goal to develop a complete system for question answering by spoken dialog [7,15]. This goal was achieved in various projects funded by the German Research Foundation [14] and the German Federal Ministry of Education and Research [16]. Problems of multilingual communication were considered in projects supported by the European Union [2,4,10]. In the Verbmobil project the speech-to-speech translation problem was investigated and it turned out that and the recognition of was important and extremely useful – if not indispensible – to disambiguate utterances and to influence the dialog strategy [3,17]. Multimodal and multimedia aspects of human-machine communication became a topic in the follow-up projects Embassi [11], SmartKom [1], FORSIP [12], and SmartWeb [9].

The SmartWeb project [19], which involves 17 partners from companies, research institutes, and universities, has the general goal to provide the foundations for multimodal human-machine communication with distributed semantic web services using different mobile devices, hand-held, mounted in a car or to a motor cycle. It uses speech and video signals as well as signals from other sensors, e.g. ECG or skin resistance. A special problem in human-machine interaction and assistance is the question whether the user speaks to the machine or not, that is, the distinction of on- and off-talk. It is shown how on-/off-talk can be classified by the combination of prosodic and image features. Using additional sensors the user state in general is estimated to give further cues to the dialog control. This may be used, for example, to avoid input from the dialog system in a situation where a driver is under stress.

In other projects the special problem of children’s speech processing was considered [20]. Among others it was investigated whether a manual correction of automatically computed fundamental frequency and word boundaries might have a positive effect on the automatic classification of the 4 classes anger, motherese, emphatic, and neutral; this was not the case, leading to the conclusion that presently there is no need for improved algorithms in emotion recognition. The word accuracy (WA) of native and non-native English speaking children was investigated; it was shown that non-native speakers (age 10 – 15) achieve about the same WA as children aged 6 – 7 using a speech recognizer trained with native children speech. The recognizer also was used to develop an automatic scoring of the pronunciation quality of children learning English.

A special problem are impairments of speech which may be congenital (e.g. the cleft lip and palate) or acquired by disease (e.g. cancer of the larynx). Impairments are, among others, treated with speech training by speech therapists. They score the speech quality subjectively according to various criteria. The idea is that the WA of an automatic speech recognizer should be highly correlated with the human rating. Using speech samples from laryngectomees it is shown that the machine rating is about as good as the rating of five human experts and can also be done via telephone. This opens the possibility of an objective and standardized rating of speech quality.

This presentation will review the state of the art in spoken language understanding. After a brief introduction on conceptual structures, early approaches to spoken language understanding (SLU) followed in the seventies are described. They are based on augmented grammars and non stochastic parsers for interpretation.

We present experiments with automatic annotation of English texts, taken from the Penn Treebank, at the dependency-based tectogrammatical layer, as it is defined in the Prague Dependency Treebank. The proposed analyzer, which is based on machine-learning techniques, outperforms a tool based on hand-written rules, which is used for partial tectogrammatical annotation of English now, in the most important characteristics of tectogrammatical annotation. Moreover, both tools were combined and their combination gives the best results.

This paper studies the problem of name ambiguity which concerns the discovery of the different underlying meanings behind a name. We have developed a semantic approach on the basis of which a graph-based clustering algorithm determines the sets of the semantically related sentences that talk about the same name. Our approach is evaluated with the Bulgarian, Romanian, Spanish and English languages for various couples of city, country, person and organization names. The yielded results significantly outperform a majority based classifier and are compared to a bigram co-occurrence approach.

This paper describes a clustering method for organizing in semantic classes a list of terms. The experiments were made using a annotated corpus, the ACL Anthology, which consists of technical articles in the field of Computational Linguistics. The method, mainly based on some assumptions of , consists in building bi-dimensional clusters of both terms and their lexico-syntactic contexts. Each generated cluster is defined as a semantic class with a set of terms describing the extension of the class and a set of contexts perceived as the intensional attributes (or properties) valid for all the terms in the extension. The clustering process relies on two restrictive operations: and . The result is a concept lattice that describes a domain-specific ontology of terms.

Many state-of-the-art statistical parsers for English can be viewed as Probabilistic Context-Free Grammars (PCFGs) acquired from treebanks consisting of phrase-structure trees enriched with a variety of contextual, derivational (e.g., markovization) and lexical information. In this paper we empirically investigate the applicability and adequacy of the unlexicalized variety of such parsing models to Modern Hebrew, a Semitic language that differs in structure and characteristics from English. We show that contrary to experience with parsing the WSJ, the markovized, head-driven unlexicalized variety does not necessarily outperform plain PCFGs for Semitic languages. We demonstrate that enriching unlexicalized PCFGs with morphologically marked agreement features percolated up the parse tree (e.g., definiteness) outperforms plain PCFGs as well as a simple head-driven variation on the MH treebank. We further show that an (unlexicalized) head-driven variety enriched with the same features achieves even better performance. We conclude that morphologically rich languages introduce an additional dimension of parametrization that is orthogonal to the horizontal/vertical dimensions proposed before [1] and its contribution is essential and complementary.

Coreference resolution, determining the appropriate discourse referent for an anaphoric expression, is an essential but difficult task in natural language processing. It has been observed that an important source of errors in machine-learning based approaches to this task, is the wrong disambiguation of the third person singular neuter pronoun as either referential or non-referential. In this paper, we investigate whether a machine learning based approach can be successfully applied to the disambiguation of the neuter pronoun in Dutch and show a modest potential effect of this disambiguation on the results of a machine learning based coreference resolution system for Dutch.

The construction of a large scale corpus is a hard task. A novel approach is designed to automatically build a large scale text corpus with low cost and short building period based on the trustworthiness. It mainly solves two problems: how to automatically build a large scale text corpus on the Web and how to correct mistakes in the corpus. As Grid provides the infrastructure for processing large scale data, our approach uses Grid to collect and process language materials on the Web in the first stage. Then it picks out untrustworthy language materials in the corpus according to their trustworthiness, and checks them manually by users. After the check finishes, our approach computes the trustworthiness of each checked result and selects those ones with the highest trustworthiness as the correct results.