Ordering products and services through virtual agents is possible but suffers limitations on the kind of ordering that is possible or on the naturalness of the conversation. We address these limitations by collecting a corpus of human-human dialogs in the food ordering domain. We create a food focused annotation scheme that is tailored for this corpus but customizable for other applications. After annotating the corpus, we find corpus characteristics that may make it more natural, such as complexity of food item mentions and use of multiple intent utterances. Furthermore, we train and evaluate preliminary statistical item and intent models using the annotated corpus.

Full text discourse parsing relies on texts comprehensively annotated with discourse relations.To this end, we address a significant gap in the inter-sentential discourse relations annotated in the Penn Discourse Treebank (PDTB), namely the class of cross-paragraph implicit relations, which account for 30% of inter-sentential relations in the corpus.We present our annotation study to explore the incidence rate of adjacent vs. non-adjacent implicit relations in cross-paragraph contexts, and the relative degree of difficulty in annotating them.Our experiments show a high incidence of non-adjacent relations that are difficult to annotate reliably, suggesting the practicality of backing off from their annotation to reduce noise for corpusbased studies.Our resulting guidelines follow the PDTB adjacency constraint for implicits while employing an underspecified representation of non-adjacent implicits, and yield 62% inter-annotator agreement on this task.

Drug repositioning has been a topic of great attention to researchers and pharmaceutical companies due to its significant impact on the cost of drug discovery. There are several approaches to identify potentially novel drug candidates through repurposing. Literature mining has played a critical role in mining such information from scientific articles. In this paper, we used drug-gene and gene-disease semantic predications extracted from Medline abstracts to generate a list of potential drug-disease pairs. We further ranked the generated pairs, by assigning scores based on the predicates that qualify drug-gene and gene-disease relationships. On comparing the top-ranked drug-disease pairs against the Comparative Toxicogenomics Database (CTD), a curated database for drug-disease relations, we found that a significant percentage of top ranked pairs appeared in CTD. Co-occurrence of these high-ranked pairs in Medline abstracts further improves the confidence in our approach to rank the inferred drug-disease relations higher in the list. Finally, manual evaluation of top ten pairs ranked by our approach revealed that nine of them have some biological significance based on expert judgment.

The Penn Discourse Treebank (PDTB) was released to the public in 2008. It remains the largest manually annotated corpus of discourse relations to date. Its focus on discourse relations that are either lexically-grounded in explicit discourse connectives or associated with sentential adjacency has not only facilitated its use in language technology and psycholinguistics but also has spawned the annotation of comparable corpora in other languages and genres. Given this situation, this paper has four aims: (1) to provide a comprehensive introduction to the PDTB for those who are unfamiliar with it; (2) to correct some wrong (or perhaps inadvertent) assumptions about the PDTB and its annotation that may have weakened previous results or the performance of decision procedures induced from the data; (3) to explain variations seen in the annotation of comparable resources in other languages and genres, which should allow developers of future comparable resources to recognize whether the variations are relevant to them; and (4) to enumerate and explain relationships between PDTB annotation and complementary annotation of other linguistic phenomena. The paper draws on work done by ourselves and others since the corpus was released.

OBJECTIVE: To develop, evaluate, and share: (1) syntactic parsing guidelines for clinical text, with a new approach to handling ill-formed sentences; and (2) a clinical Treebank annotated according to the guidelines. To document the process and findings for readers with similar interest. METHODS: Using random samples from a shared natural language processing challenge dataset, we developed a handbook of domain-customized syntactic parsing guidelines based on iterative annotation and adjudication between two institutions. Special considerations were incorporated into the guidelines for handling ill-formed sentences, which are common in clinical text. Intra- and inter-annotator agreement rates were used to evaluate consistency in following the guidelines. Quantitative and qualitative properties of the annotated Treebank, as well as its use to retrain a statistical parser, were reported. RESULTS: A supplement to the Penn Treebank II guidelines was developed for annotating clinical sentences. After three iterations of annotation and adjudication on 450 sentences, the annotators reached an F-measure agreement rate of 0.930 (while intra-annotator rate was 0.948) on a final independent set. A total of 1100 sentences from progress notes were annotated that demonstrated domain-specific linguistic features. A statistical parser retrained with combined general English (mainly news text) annotations and our annotations achieved an accuracy of 0.811 (higher than models trained purely with either general or clinical sentences alone). Both the guidelines and syntactic annotations are made available at https://sourceforge.net/projects/medicaltreebank. CONCLUSIONS: We developed guidelines for parsing clinical text and annotated a corpus accordingly. The high intra- and inter-annotator agreement rates showed decent consistency in following the guidelines. The corpus was shown to be useful in retraining a statistical parser that achieved moderate accuracy.

OBJECTIVE: Relation extraction in biomedical text mining systems has largely focused on identifying clause-level relations, but increasing sophistication demands the recognition of relations at discourse level. A first step in identifying discourse relations involves the detection of discourse connectives: words or phrases used in text to express discourse relations. In this study supervised machine-learning approaches were developed and evaluated for automatically identifying discourse connectives in biomedical text. MATERIALS AND METHODS: Two supervised machine-learning models (support vector machines and conditional random fields) were explored for identifying discourse connectives in biomedical literature. In-domain supervised machine-learning classifiers were trained on the Biomedical Discourse Relation Bank, an annotated corpus of discourse relations over 24 full-text biomedical articles (~112,000 word tokens), a subset of the GENIA corpus. Novel domain adaptation techniques were also explored to leverage the larger open-domain Penn Discourse Treebank (~1 million word tokens). The models were evaluated using the standard evaluation metrics of precision, recall and F1 scores. RESULTS AND CONCLUSION: Supervised machine-learning approaches can automatically identify discourse connectives in biomedical text, and the novel domain adaptation techniques yielded the best performance: 0.761 F1 score. A demonstration version of the fully implemented classifier BioConn is available at: http://bioconn.askhermes.org.

BACKGROUND: Identification of discourse relations, such as causal and contrastive relations, between situations mentioned in text is an important task for biomedical text-mining. A biomedical text corpus annotated with discourse relations would be very useful for developing and evaluating methods for biomedical discourse processing. However, little effort has been made to develop such an annotated resource. RESULTS: We have developed the Biomedical Discourse Relation Bank (BioDRB), in which we have annotated explicit and implicit discourse relations in 24 open-access full-text biomedical articles from the GENIA corpus. Guidelines for the annotation were adapted from the Penn Discourse TreeBank (PDTB), which has discourse relations annotated over open-domain news articles. We introduced new conventions and modifications to the sense classification. We report reliable inter-annotator agreement of over 80% for all sub-tasks. Experiments for identifying the sense of explicit discourse connectives show the connective itself as a highly reliable indicator for coarse sense classification (accuracy 90.9% and F1 score 0.89). These results are comparable to results obtained with the same classifier on the PDTB data. With more refined sense classification, there is degradation in performance (accuracy 69.2% and F1 score 0.28), mainly due to sparsity in the data. The size of the corpus was found to be sufficient for identifying the sense of explicit connectives, with classifier performance stabilizing at about 1900 training instances. Finally, the classifier performs poorly when trained on PDTB and tested on BioDRB (accuracy 54.5% and F1 score 0.57). CONCLUSION: Our work shows that discourse relations can be reliably annotated in biomedical text. Coarse sense disambiguation of explicit connectives can be done with high reliability by using just the connective as a feature, but more refined sense classification requires either richer features or more annotated data. The poor performance of a classifier trained in the open domain and tested in the biomedical domain suggests significant differences in the semantic usage of connectives across these domains, and provides robust evidence for a biomedical sublanguage for discourse and the need to develop a specialized biomedical discourse annotated corpus. The results of our cross-domain experiments are consistent with related work on identifying connectives in BioDRB.

One of the biggest challenges in the development and deployment of spoken dialogue systems is the design of the spoken language generation module. This challenge arises from the need for the generator to adapt to many features of the dialogue domain, user population, and dialogue context. A promising approach is trainable generation, which uses general-purpose linguistic knowledge that is automatically adapted to the features of interest, such as the application domain, individual user, or user group. In this paper we present and evaluate a trainable sentence planner for providing restaurant information in the MATCH dialogue system. We show that trainable sentence planning can produce complex information presentations whose quality is comparable to the output of a template-based generator tuned to this domain. We also show that our method easily supports adapting the sentence planner to individuals, and that the individualized sentence planners generally perform better than models trained and tested on a population of individuals. Previous work has documented and utilized individual preferences for content selection, but to our knowledge, these results provide the first demonstration of individual preferences for sentence planning operations, affecting the content order, discourse structure and sentence structure of system responses. Finally, we evaluate the contribution of different feature sets, and show that, in our application, n-gram features often do as well as features based on higher-level linguistic representations.

Spoken language generation for dialogue systems requires a dictionary of mappings between semantic representations of concepts the system wants to express and realizations of those concepts. Dictionary creation is a costly process; it is currently done by hand for each dialogue domain. We propose a novel unsupervised method for learning such mappings from user reviews in the target domain, and test it on restaurant reviews. We test the hypothesis that user reviews that provide individual ratings for distinguished attributes of the domain entity make it possible to map review sentences to their semantic representation with high precision. Experimental analyses show that the mappings learned cover most of the domain ontology, and provide good linguistic variation. A subjective user evaluation shows that the consistency between the semantic representations and the learned realizations is high and that the naturalness of the realizations is higher than a hand-crafted baseline.

A challenging problem for spoken dialog systems is the design of utterance generation modules that are fast, flexible and general, yet produce high quality output in particular domains. A promising approach is trainable generation, which uses general-purpose linguistic knowledge automatically adapted to the application domain. This paper presents a trainable sentence planner for the MATCH dialog system. We show that trainable sentence planning can produce output comparable to that of MATCH's template-based generator even for quite complex information presentations.

As the complexity of spoken dialogue systems has increased, there has been increasing interest spoken language generation (SLG). SLG promises portability across application domains and dialogue situations through the development of applicationindependent linguistic modules. However in practice, rulebased SLGs often have to be tuned to the application. Recently, a number of research groups have been developing hybrid methods for spoken language generation, combining general linguistic modules with methods for training parameters for particular applications. This paper describes the use of boosting to train a sentence planner to generate recommendations for restaurants in MATCH, a multimodal dialogue system providing entertainment information for New York. 1.

Spoken dialogue systems promise efficient and natural access to information services from any phone. Recently, spoken dialogue systems for widely used applications such as email, travel information, and customer care have moved from research labs into commercial use. These applications can receive millions of calls a month. This huge amount of spoken dialogue data has led to a need for fully automatic methods for selecting a subset of caller dialogues that are most likely to be useful for further system improvement, to be stored, transcribed and further analyzed. This paper reports results on automatically training a Problematic Dialogue Identifier to classify problematic human-computer dialogues using a corpus of 1242 DARPA Communicator dialogues in the travel planning domain. We show that using fully automatic features we can identify classes of problematic dialogues with accuracies from 67% to 89%.

This paper describes the evaluation methodology and results of the DARPA Communicator spoken dialog system evaluation experiments in 2000 and 2001. Nine spoken dialog systems in the travel planning domain participated in the experiments resulting in a total corpus of 1904 dialogs. We describe and compare the experimental design of the 2000 and 2001 DARPA evaluations. We describe how we established a performance baseline in 2001 for complex tasks. We present our overall approach to data collection, the metrics collected, and the application of PARADISE to these data sets. We compare the results we achieved in 2000 for a number of core metrics with those for 2001. These results demonstrate large performance improvements from 2000 to 2001 and show that the Communicator program goal of conversational interaction for complex tasks has been achieved.

This paper describes the evaluation methodology and results of the 2001 DARPA Communicator evaluation. The experiment spanned 6 months of 2001 and involved eight DARPA Communicator systems in the travel planning domain. It resulted in a corpus of 1242 dialogs which include many more dialogues for complex tasks than the 2000 evaluation. We describe the experimental design, the approach to data collection, and the results. We compare the results by the type of travel plan and by system. The results demonstrate some large differences across sites and show that the complex trips are clearly more difficult.