Abstract

In a task oriented domain, recognizing the intention of a speaker is important so that the conversation can proceed in the correct direction. This is possible only if there is a way of labeling the utterance with its proper intent. One such labeling techniques is Dialog Act (DA) tagging. This work focuses on discussing various n-gram DA tagging techniques. In this paper, a new method is proposed for DA tagging in Telugu using n-gram karakas with back-off as n-gram language modeling technique at n-gram level and Memory Based Learning at utterance level. The results show that the proposed method is on par with manual DA tagging.

Abstract

Attribute information in a natural language query is one of the key features for converting a natural language query into a Structured Query Language1 (SQL) in Natural Language Interface to Database systems. In this paper, we explore the task of classifying the attributes present in a natural language query into different SQL clauses in a SQL query. In particular, we investigate the effectiveness of various features and Conditional Random Fields for this task. Our system uses a statistical classifier trained on manually prepared data. We report our results on three different domains and also show how our system can be used for generating a complete SQL query.

Abstract

In this paper, we present a Paninian grammar based heuristic model1 to resolve entity-pronoun references in Hindi dialogue. We explore the use of Paninian based dependency structures as a source of syntactico-semantic information. Our experiments illustrate that the use of dependency and dialogue structures help to resolve specific types of references. We also show that named entity, discourse information like subtopic boundary and animacy features increase the overall resolution accuracy to 64% for user-user interaction data and 59% for play-story corpora.

Abstract

This paper describes the techniques for the automatic detection of subtopic/subplan boundary in Hindi dialogue using structure of dialogue, dialogue acts, shallow linguistic features and word co-occurrence. Our experiments illustrate that the use of dialogue structure, word co-occurrence and wordnet improves the boundary identification for Hindi natural dialogues.

Abstract

In this paper we explain the identification of karaka relations in an English sentence. We explain the genesis of the problem and present two different approaches, rule based and statistical. We briefly describe about rule based and focus more on statistical approach. We process a sentence through various stages and extract features at each stage. We train our data and identify Karaka relations using Support Vector Machines (SVM). We also explain the impact of our work on Natural Language Interfaces for Database systems.

Abstract

This paper proposes a novel approach to capture the concepts1 of an NL query. Given an NL query, the query is mapped to a tagset, which carries the concept information. The tagset was created by mapping every noun chunk to the attribute of a table (tableName.attributeName) and every verb chunk to a relation in the ER schema. The approach is discussed using the Courses Management domain of a University and can be extended to other domains. The tagset here was formed using the ER-schema of the Courses Management Portal of our university. We used the statistical approach to identify the concepts. We ourselves formed a tagged corpus with different types of NL queries. Conditional Random Field algorithm was used for the classification. The results are very promising and are compared to the rule based approach seen in Gupta et al. (2012) [1] .

Abstract

Statistical morph analyzers have proved to be highly accurate while being comparatively easier to maintain than rule based approaches. Our morph analyzer (SMA++) is an improvement over the statistical morph analyzer (SMA) described in Malladi and Mannem (2013). SMA++ predicts the gender, number, person, case (GNPC) and the lemma (L) of a given token. We modified the SMA in Malladi and Mannem (2013), by adding some rich machine learning features. The feature set was chosen specifically to suit the characteristics of Indian Languages. In this paper we apply SMA++ to four Indian languages viz. Hindi, Urdu, Telugu and Tamil. Hindi and Urdu belong to the Indic1 language family. Telugu and Tamil belong to the Dravidian2 language family. We compare SMA++ with some state-of-art statistical morph analyzers viz. Morfette in Chrupała et al. (2008) and SMA in Malladi and Mannem (2013). In all four languages, our system performs better than the above mentioned state-of-art SMAs

Abstract

Earlier works has explored that a foreign language can be learnt from pictures than merely using native language word translations. These efforts on language learning using pictures were limited to colors, animals, birds, vehicles and common noun categories. In this paper, we present our research that learning of verbs is also possible by using right set of pictures and gestures, and we show that this is effective in second language learning. We report the acquisition of words in tourist domain in a second language by working with subjects who are between 14 and 48 years of age. From pre-learning and post-learning evaluations, we show that acquisition of vocabulary like nouns and verbs in a new language is better with the fusion of pictures and voice. We also show the subjects are able to generalize their learning towards phrase-level vocabulary without any additional training and efforts.

Abstract

This paper presents a novel approach to categorize, model and identify contextual information in natural language interface to database (NLIDB) systems. The interactions between user and system are categorized and modeled based on the way in which the contextual information is utilized in the interactions. A relationship schema among the responses (user and system responses) is proposed. We present a novel method to identify contextual information in one specific type of usersystem interaction. We report on results of experiments with the university related queries.

Abstract

Social networking sites provide users a virtual community interaction platform to share their thoughts, life experiences and opinions. Online debate forum is one such platform where people can take a stance and argue in support or opposition of debate topics. An important feature of such forums is that, they are dynamic and increase rapidly. In such situations, e↵ective opinion summarization approaches are needed so that readers need not go through the entire debate. This paper aims to summarize online debates by extracting highly topic relevant and sentiment rich sentences. The proposed approach takes into account topic relevant, document relevant and sentiment based features to capture topic opinionated sentences. ROUGE scores are used to evaluate our system. Our system significantly outperforms several baseline systems and show 5.2% (ROUGE-1), 7.3%(ROUGE-2) and 5.5% (ROUGE-L) improvement over the state-of-the-art opinion summarization system. The results verify that topic directed sentiment features are most important to generate e↵ective debate summaries.