Young In Song

Bridging Lexical Gaps between Queries and Questions on Large Online Q&A Collections with Compact Translation Models

Lexical gaps between queries and questions (documents) have been a major issue in question retrieval on large online question and answer (Q&A) collections. Previous studies address the issue by implicitly expanding queries with the help of translation models pre-constructed using statistical techniques. However, since it is possible for unimportant words (e.g., non-topical words, common words) to be included in the translation models, a lack of noise control on the models can cause degradation of retrieval performance. This paper investigates a number of empirical methods for eliminating unimportant words in order to construct compact translation models for retrieval purposes. Experiments conducted on a real world Q&A collection show that substantial improvements in retrieval performance can be achieved by using compact translation models.

Click the search button and be happy: evaluating direct and immediate information access

We define Direct Information Access as a type of information access where there is no user operation such as clicking or scrolling between the users click on the search button and the users information acquisition; we define Immediate Information Access as a type of information access where the user can locate the relevant information within the system output very quickly. Hence, a Direct and Immediate Information Access (DIIA) system is expected to satisfy the users information need very quickly with its very first response. We propose a nugget-based evaluation framework for DIIA, which takes nugget positions into account in order to evaluate the ability of a system to present important nuggets first and to minimise the amount of text the user has to read. To demonstrate the integrity, usefulness and limitations of our framework, we built a Japanese DIIA test collection with 60 queries and over 2,800 nuggets as well as an offset-based nugget match evaluation interface, and conducted experiments with manual and automatic runs. The results suggest our proposal is a useful complement to traditional ranked retrieval evaluation based on document relevance.

A novel retrieval approach reflecting variability of syntactic phrase representation

In this paper, we introduce variability of syntactic phrases and propose a new retrieval approach reflecting the variability of syntactic phrase representation. With variability measure of a phrase, we can estimate how likely a phrase in a given query would appear in relevant documents and control the impact of syntactic phrases in a retrieval model. Various experimental results over different types of queries and document collections show that our retrieval model based on variability of syntactic phrases is very effective in terms of retrieval performance, especially for long natural language queries.

Investigation of Weakly Supervised Learning for Semantic Role Labeling

In this paper, we investigate the possibility of the weakly supervised learning for Semantic Role Labeling. First, we attempt to achieve feature splitting which is the base constraint of co-training, and examine if co-training works for the task of Semantic Role Labeling. We also examine the possibility of self-training which uses the identical features with co-training, and compare the performance of co-training and self-training. From the experiments, we found some interesting points about Semantic Role Labeling task and the weakly supervised learning. As far as we know, this is the first experiment to apply weakly supervised learning to Semantic Role Labeling and the experimental results show that Semantic Role Labeling can be successfully done by weakly supervised learning.

Answer extraction and ranking strategies for definitional question answering using linguistic features and definition terminology

We propose answer extraction and ranking strategies for definitional question answering using linguistic features and definition terminology. A passage expansion technique based on simple anaphora resolution is introduced to retrieve more informative sentences, and a phrase extraction method based on syntactic information of the sentences is proposed to generate a more concise answer. In order to rank the phrases, we use several evidences including external definitions and definition terminology. Although external definitions are useful, it is obvious that they cannot cover all the possible targets. The definition terminology score which reflects how the phrase is definition-like is devised to assist the incomplete external definitions. Experimental results show that the proposed answer extraction and ranking method are effective and also show that our proposed system is comparable to state-of-the-art systems.

Combining Local and Global Resources for Constructing an Error-Minimized Opinion Word Dictionary

A lexical dictionary consisting of opinion words and their polar orientations plays a crucial contribution to opinion mining tasks (e.g., sentiment classification). Previous works on automatic construction of such dictionary have a problem of generating errors (i.e., incorrect identification of polar orientations of words in dictionary). To address the problem, this paper proposes an Error Minimization Algorithm for reducing errors caused by automatic compiling process to construct a reasonable opinion word dictionary. The proposed algorithm combines global and local resources for extracting and refining the dictionary with minimum errors. Empirical results show that our proposed approach is effective for enhancing the performance of the sentiment classification task.

High precision opinion retrieval using sentiment-relevance flows

Opinion retrieval involves the measuring of opinion score of a document about the given topic. We propose a new method, namely sentiment-relevance flow, that naturally unifies the topic relevance and the opinionated nature of a document. Experiments conducted over a large-scaled Web corpus show that the proposed approach improves performance of opinion retrieval in terms of precision at top ranks.

Finding advertising keywords on video scripts

A key to success to contextual in-video advertising is finding advertising keywords on video contents effectively, but there has been little literature in the area so far. This paper presents some preliminary results of our learning-based system that finds relevant advertising keywords on particular scene of video contents using their scripts. The system is trained with not only features proven useful in earlier studies but novel features that reflect the situation of a targeted scene. Experimental results show that the new features are potentially helpful for enhancing the accuracy of keyword extraction for contextual in-video advertising.

A new generative opinion retrieval model integrating multiple ranking factors

In this paper, we present clear and formal definitions of ranking factors that should be concerned in opinion retrieval and propose a new opinion retrieval model which simultaneously combines the factors from the generative modeling perspective. The proposed model formally unifies relevance-based ranking with subjectivity detection at the document level by taking multiple ranking factors into consideration: topical relevance, subjectivity strength, and opinion-topic relatedness. The topical relevance measures how strongly a document relates to a given topic, and the subjectivity strength indicates the likelihood that the document contains subjective information. The opinion-topic relatedness reflects whether the subjective information is expressed with respect to the topic of interest. We also present the universality of our model by introducing the model’s derivations that represent other existing opinion retrieval approaches. Experimental results on a large-scale blog retrieval test collection demonstrate that not only are the individual ranking factors necessary in opinion retrieval but they cooperate advantageously to produce a better document ranking when used together. The retrieval performance of the proposed model is comparable to that of previous systems in the literature.

Automatic Acronym Dictionary Construction Based on Acronym Generation Types

In this paper, we propose a new model of automatically constructing an acronym dictionary. The proposed model generates possible acronym candidates from a definition, and then verifies each acronym-definition pair with a Naive Bayes classifier based on web documents. In order to achieve high dictionary quality, the proposed model utilizes the characteristics of acronym generation types: a syllable-based generation type, a word-based generation type, and a mixed generation type. Compared with a previous model recognizing an acronym-definition pair in a document, the proposed model verifying a pair in web documents improves approximately 50% recall on obtaining acronym-definition pairs from 314 Korean definitions. Also, the proposed model improves 7.25% F-measure on verifying acronym-definition candidate pairs by utilizing specialized classifiers with the characteristics of acronym generation types.