Alejandro Figueroa

Category-specific models for ranking effective paraphrases in community Question Answering

Abstract Platforms for community-based Question Answering (cQA) are playing an increasing role in the synergy of information-seeking and social networks. Being able to categorize user questions is very important, since these categories are good predictors for the underlying question goal, viz. informational or subjective. Furthermore, an effective cQA platform should be capable of detecting similar past questions and relevant answers, because it is known that a high number of best answers are reusable. Therefore, question paraphrasing is not only a useful but also an essential ingredient for effective search in cQA. However, the generated paraphrases do not necessarily lead to the same answer set, and might differ in their expected quality of retrieval, for example, in their power of identifying and ranking best answers higher. We propose a novel category-specific learning to rank approach for effectively ranking paraphrases for cQA. We describe a number of different large-scale experiments using logs from Yahoo! Search and Yahoo! Answers, and demonstrate that the subjective and objective nature of cQA questions dramatically affect the recall and ranking of past answers, when fine-grained category information is put into its place. Then, category-specific models are able to adapt well to the different degree of objectivity and subjectivity of each category, and the more specific the models are, the better the results, especially when benefiting from effective semantic and syntactic features.

Language independent answer prediction from the web

This work presents a strategy that aims to extract and rank predicted answers from the web based on the eigenvalues of a specially designed matrix. This matrix models the strength of the syntactic relations between words by means of the frequency of their relative positions in sentences extracted from web snippets. We assess the rank of predicted answers by extracting answer candidates for three different kinds of questions. Due to the low dependence upon a particular language, we also apply our strategy to questions from four different languages: English, German, Spanish, and Portuguese.

Genetic algorithms for data-driven web question answering

We present an evolutionary approach for the computation of exact answers to natural languages (NL) questions. Answers are extracted directly from the Nbest snippets, which have been identified by a standard Web search engine using NL questions. The core idea of our evolutionary approach to Web question answering is to search for those substrings in the snippets whose contexts are most similar to contexts of already known answers. This context model together with the words mentioned in the NL question are used to evaluate the fitness of answer candidates, which are actually randomly selected substrings from randomly selected sentences of the snippets. New answer candidates are then created by applying specialized operators for crossover and mutation, which either stretch and shrink the substring of an answer candidate or transpose the span to new sentences. Since we have no predefined notion of patterns, our context alignment methods are very dynamic and strictly data-driven. We assessed our system with seven different datasets of question/answer pairs. The results show that this approach is promising, especially when it deals with specific questions.

A Multilingual Framework for Searching Definitions on Web Snippets

This work presents Mdef-WQA , a system that searches for answers to definition questions in several languages on web snippets. For this purpose, Mdef-WQA biases the search engine in favour of some syntactic structures that often convey definitions. Once descriptive sentences are identified, Mdef-WQA clusters them by potential sensesand presents the most relevant phrases of each potential senseto the user. The approach was assessed with TREC and CLEF data. As a result, Mdef-WQA was able to extract descriptive information for all definition questions in the TREC 2001 and 2003 data-sets.

Search clicks analysis for discovering temporally anchored questions in community Question Answering

We study temporal click patterns across search query logs in relation to cQA pages.Search activity analysis helps to define three temporal interests for cQA questions.We automatically tagged 35,000 questions according to these temporal anchors.Our approach was validated by two judges; and by predicting anchors on unseen data.Our outcomes indicate that some contexts are related to a particular temporal anchor. Nowadays, community Question-Answering (cQA) sites are massive repositories for user-generated content, where members prompt questions expecting satisfactory answers from other members. However, in this dynamic, there is an intrinsic delay between the moment questions are posted to the arrival of acceptable responses. Therefore, cQA platforms have the pressing need for promoting unresolved questions to potential answerers and for taking advantage of resolved questions contained in their archives, whenever possible.This paper studies cQA services from the viewpoint of the time frame where their questions attract the interest of their community members. By drawing a parallel with temporal patterns of user interests in web search activity, we are able to define three main types of temporally anchored questions: trend or bursty, periodic and permanent. Then, by analyzing user click distributions to Yahoo! Answers pages across Yahoo! Search logs, we automatically acquired a set of 35,000 cQA questions labeled with one of these three temporal anchors. Accordingly, we show the practicality of this approach by means of human assessments; and by using this automatically acquired corpus for studying several classification models.Essentially, the proposed method was found to correlate well with these human judgements, and proven to be effective in building systems that automatically identify the temporal anchor of unseen cQA questions. In substance, our outcomes indicate that some contexts are strongly related to a particular temporal anchor. We believe that these anchors will contribute to the discrimination of resolved questions that are capable of being revitalized, as well as to foster the opportune participation in questions that generate enthusiasm only for a short time.

Improving opinion retrieval in social media by combining features-based coreferencing and memory-based learning

Social networks messaging typically contains a lot of implicit linguistic information partially due to restrictions on a messages length (i.e., few named entities, short sentences, no discourse structure, etc.). This may significantly impact several applications including opinion mining, sentiment analysis, etc., as data collection tasks such as opinion retrieval tasks will fail to obtain all the relevant messages whenever the target topic, objects, or features are not explicit within the texts. In order to address these issues, in this paper a novel adaptive approach for opinion retrieval is proposed. It combines natural-language co-referencing techniques, features-based linguistic preprocessing and memory-based learning to resolving implicit co-referencing within informal opinion texts by using underlying hierarchies of thread messages. Experiments were conducted to assess the ability of the model to improve opinion retrieval by resolving implicit entities and features, showing the promise of our opinion retrieval approach when compared to state-of-the-art methods using text data from social networks.

Exploring effective features for recognizing the user intent behind web queries

HighlightsEffective features for web query classification.Detecting user intents behind search queries.Natural language processing for search queries.Multi-class classification for web queries. Automatically identifying the user intent behind web queries has started to catch the attention of the research community, since it allows search engines to enhance user experience by adapting results to that goal. It is broadly agreed that there are three archetypal intentions behind search queries: navigational, resource/transactional and informational.Thus, as a natural consequence, this task has been interpreted as a multi-class classification problem. At large, recent works have focused on comparing several machine learning methods built with words as features. Conversely, this paper examines the influence of assorted properties on three classification approaches. In particular, it focuses its attention on the contribution of linguistic-based attributes. However, most of natural language processing tools are designed for documents, not web queries. Therefore, as a means of bridging this linguistic gap, we benefited from caseless models, which are trained with traditionally labeled data, but all terms are converted to lowercase before their generation.Overall, tested attributes proved to be effective by improving on word-based classifiers by up to 8.347% (accuracy), and outperforming a baseline by up to 6.17%. Most notably, linguistic-oriented features, from caseless models, are shown to be instrumental in narrowing the linguistic gap between queries and documents.

Evolutionary optimization for ranking how-to questions based on user-generated contents

In this work, a new evolutionary model is proposed for ranking answers to non-factoid (how-to) questions in community question-answering platforms. The approach combines evolutionary computation techniques and clustering methods to effectively rate best answers from web-based user-generated contents, so as to generate new rankings of answers. Discovered clusters contain semantically related triplets representing question-answers pairs in terms of subject-verb-object, which is hypothesized to improve the ranking of candidate answers. Experiments were conducted using our evolutionary model and concept clustering operating on large-scale data extracted from Yahoo! Answers. Results show the promise of the approach to effectively discovering semantically similar questions and improving the ranking as compared to state-of-the-art methods.

Leveraging linguistic traits and semi-supervised learning to single out informational content across how-to community question-answering archives

Community Question-Answering sites (e.g., Yahoo! Answers) have become large-scale knowledge bases of natural language questions formulated by their own members. In other to provide quick answers, these sites are compelled to make the best out of the content stored in their repository. Researchers have discovered that, on the one hand, many of these services are the confluence of an information-seeking and a social network that are constantly overlapping, and on the other hand, how-to questions are frequently published across these platforms. By and large, informational procedural questions are highly likely to expect informational answers, while non-informational manner questions target at socially interacting with other members of the community. In order to enhance user experience by reducing the delay in answering, these services are heartened to identify, retrieve and revitalize the content maintained in their knowledge bases. For this purpose, it is key to match the intent of new posted questions with the intention of archived answers that will be presented to the asker.By manually annotating a reduced number of how-to questions and answers, we carried out an exploratory analysis that unveils a dichotomy between the interaction of these two networks. More precisely, we corroborate previous findings indicating that procedural questions are more likely to bear an informational goal, but our analysis is also extended to their answers, and it reveals that they exhibit a more conspicuous confluence. In substance, we find out that informational and non-informational answers are very likely to show up regardless of the end of the question. Then, we take advantage of this tagged set and of massive unlabelled material for exploiting two state-of-the-art single-view semi-supervised approaches aimed at discriminating informational from non-informational how-to content.Moreover, our proposed models leverage assorted linguistically-motivated features, such as sentiment analysis and dependency parsing as well as named entity recognition. Our outcomes show that attributes, harvested from morphological and sentiment analysis, proven to be effective under a semi-supervised framework. At the expenses of low annotation costs, these linguistically-motivated semi-supervised models reached an accuracy of 84.25% and 74.41% for classifying questions and answers, respectively. In addition, we quantify the impact of automatically detecting informational/non-informational intents on the retrieval of best answers, i.e., an improvement of 4.12% in terms of precision at one.

Searching for Definitional Answers on the Web Using Surface Patterns

A novel question-answering system employs query rewriting techniques to increase the probability of extracting nuggets from various Web snippets by matching surface patterns. Experimental results show the approachs promise versus existing techniques.