A fundamental issue in opinion mining is to search a corpus for opinion units, each of which typically comprises the evaluation by an author for a target object from an aspect, such as "This hotel is in a good location".However, few attempts have been made to address cases where the validity of an evaluation is restricted on a condition in the source text, such as "for traveling with small kids".In this paper, we propose a method to extract condition-opinion relations from online reviews, which enables fine-grained analysis for the utility of target objects depending the user attribute, purpose, and situation.Our method uses supervised machine learning to identify sequences of words or phrases that comprise conditions for opinions.We propose several features associated with lexical and syntactic information, and show their effectiveness experimentally.

We organized a machine translation (MT) task at the Seventh NTCIR Workshop. Participating groups were requested to machine translate sentences in patent documents and also search topics for retrieving patent documents across languages. We analyzed the relationship between the accuracy of MT and its effects on the retrieval accuracy.

Several types of queries are widely used on the World Wide Web and the expected retrieval method can vary depending on the query type. We propose a method for classifying queries into informational and navigational types. Because terms in navigational queries often appear in anchor text for links to other pages, we analyze the distribution of query terms in anchor texts on the Web for query classification purposes. While content-based retrieval is effective for informational queries, anchor-based retrieval is effective for navigational queries. Our retrieval system combines the results obtained with the content-based and anchor-based retrieval methods, in which the weight for each retrieval result is determined automatically depending on the result of the query classification. We also propose a method for improving anchor-based retrieval. Our retrieval method, which computes the probability that a document is retrieved in response to the given query, identifies synonyms of query terms in the anchor texts on the Web and uses these synonyms for smoothing purposes in the probability estimation. We use the NTCIR test collections and show the effectiveness of individual methods and the entire Web retrieval system experimentally.

This paper proposes a method to combine text-based and citation-based retrieval methods in the invalidity patent search. Using the NTCIR-6 test collection including eight years of USPTO patents, we show the effectiveness of our method experimentally.

This paper proposes methods for extracting loanwords from Cyrillic Mongolian corpora and producing a Japanese-Mongolian bilingual dictionary. We extract loanwords from Mongolian corpora using our own handcrafted rules. To complement the rule-based extraction, we also extract words in Mongolian corpora that are phonetically similar to Japanese Katakana words as loanwords. In addition, we correspond the extracted loanwords to Japanese words and produce a bilingual dictionary. We propose a stemming method for Mongolian to extract loanwords correctly. We verify the effectiveness of our methods experimentally.

To generate content for multimedia encyclopedias, we propose a method for searching the Web, seeking images associated with a specific word sense. We use text in an HTML file that links to an image as a pseudo-caption for the image, enabling text-based indexing and retrieval. We use term descriptions in a Web search site called "Cyclone" as queries and match images and texts based on word senses. We show the effectiveness of our method experimentally.

We propose an associative document retrieval method, in which a document is used as a query to search for other similar documents. Because a long document usually includes more than one topic, we first analyze a query document to extract multiple subtopics. For each subtopic element, a sub-query is produced and similar documents are retrieved with a relevance score. The relevance scores are weighted by the importance of each subtopic element and are integrated to determine the final relevant documents. In the calculation of the subtopic importance, the specificity of a query term is evaluated using entropy, which is the deviation degree of the appearances of the term in each subtopic element. We apply this method to an invalidity patent search. By exploiting certain unique features of Japanese patent claims, we use features distinguishing the preamble and the essential portion in a query patent claim. To demonstrate the effectiveness of our method, we experimentally evaluated our associative document retrieval method on five years of patent documents.

We are developing a cross-media information retrieval system, in which users can view specific segments of lecture videos by submitting text queries. To produce a text index, the audio track is extracted from a lecture video and a transcription is generated by automatic speech recognition. In this paper, to improve the quality of our retrieval system, we extensively investigate the effects of adapting acoustic and language models on speech recognition. We perform an MLLR-based method to adapt an acoustic model. To obtain a corpus for language model adaptation, we use the textbook for a target lecture to search a Web collection for the pages associated with the lecture topic. We show the effectiveness of our method by means of experiments. 1.

We integrate automatic speech recognition (ASR) and question answering (QA) to realize a speech-driven QA system, and evaluate its performance. We adapt an Ngram language model to natural language questions, so that the input of our system can be recognized with a high accuracy. We target WH-questions which consist of the topic part and fixed phrase used to ask about something. We first produce a general N-gram model intended to recognize the topic and emphasize the counts of the N-grams that correspond to the fixed phrases. Given a transcription by the ASR engine, the QA engine extracts the answer candidates from target documents. We propose a passage retrieval method robust against recognition errors in the transcription. We use the QA test collection produced in NTCIR, which is a TREC-style evaluation workshop, and show the effectiveness of our method by means of experiments.

This paper describes the Patent Retrieval Task in the Fourth NTCIR Workshop, and the test collections produced in this task. We perform the invalidity search task, in which each participant group searches a patent collection for the patents that can invalidate the demand in an existing claim. We also perform the automatic patent map generation task, in which the patents associated with a specific topic are organized in a multi-dimensional matrix.

In support of speech-driven question answering, we propose a method to construct N-gram language models for recognizing spoken questions with high accuracy. Question-answering sys-tems receive queries that often consist of two parts: one conveys the query topic and the other is a fixed phrase used in query sentences. A language model constructed by using a target col-lection of QA, for example, newspaper articles, can model the former part, but cannot model the latter part appropriately. We tackle this problem as task adaptation from language models ob-tained from background corpora (e.g., newspaper articles) to the fixed phrases, and propose a method that does not use the task-specific corpus, which is often difficult to obtain, but instead uses only manually listed fixed phrases. The method empha-sizes a subset of N-grams obtained from a background corpus that corresponds to fixed phrases specified by the list. Theoret-ically, this method can be regarded as maximizing a posteriori probability (MAP) estimation using the subset of the N-grams as a posteriori distribution. Some experiments show the effec-tiveness of our method. 1.

This paper describes a test collection (benchmark data) for retrieval systems driven by spoken queries. This collection was produced in the subtask of the NTCIR-3 Web retrieval task, which was performed in a TREC-style evaluation workshop. The search topics and document collection for the Web retrieval task were used to produce spoken queries and language models for speech recognition, respectively. We used this collection to evaluate the performance of our retrieval system. Experimental results showed that (a) the use of target documents for language modeling and (b) enhancement of the vocabulary size in speech recognition were effective in improving the system performance.

We propose a cross-media lecture-on-demand system, in which users can selectively view specific segments of lecture videos by submitting text queries. Users can easily formulate queries by using the textbook associated with a target lecture, even if they cannot come up with effective keywords. Our system extracts the audio track from a target lecture video, generates a transcription by large vocabulary continuous speech recognition, and produces a text index. Experimental results showed that by adapting speech recognition to the topic of the lecture, the recognition accuracy increased and the retrieval accuracy was comparable with that obtained by human transcription. 1.

Reflecting the rapid growth in the utilization of large test collections for information retrieval since the 1990s, extensive comparative experiments have been performed to explore the effectiveness of various retrieval models. However, most collections were intended for retrieving newspaper articles and technical abstracts. In this paper, we describe the process of producing a test collection for patent retrieval, the NTCIR-3 Patent Retrieval Collection, which includes two years of Japanese patent applications and 31 topics produced by professional patent searchers. We also report experimental results obtained by using this collection to re-examine the effectiveness of existing retrieval models in the context of patent retrieval. The relative superiority among existing retrieval models did not significantly differ depending on the document genre, that is, patents and newspaper articles. Issues related to patent retrieval are also discussed.

While recent retrieval techniques do not limit the number of index terms, out-ofvocabulary (OOV) words are crucial in speech recognition.Aiming at retrieving information with spoken queries, we fill the gap between speech recognition and text retrieval in terms of the vocabulary size.Given a spoken query, we generate a transcription and detect OOV words through speech recognition.We then correspond detected OOV words to terms indexed in a target collection to complete the transcription, and search the collection for documents relevant to the completed transcription.We show the effectiveness of our method by way of experiments.

Spoken queries submitted to question answering systems usually consist of query contents (e.g. about newspaper articles) and frozen patterns (e.g. WH-words), which can be modeled with N-gram models and grammar-based models, respectively. We propose a method to integrate those different types of models into a single N-gram model. We represent the two types of language models in a single word network. However, common smoothing methods, which are effective for N-gram models, decrease grammatical constraints for frozen patterns. For this problem, we propose a selective back-off smoothing method, which controls a degree to which smoothing is applied depending the network fragment. Additionally, resulting models are compatible with the conventional back-off N-gram models, and thus existing N-gram decoders can easily be used. We show the effectiveness of our method by way of experiments. 1.

We propose a method to generate large-scale encyclopedic knowledge, which is valuable for much NLP research, based on the Web. We first search the Web for pages containing a term in question. Then we use linguistic patterns and HTML structures to extract text fragments describing the term. Finally, we organize extracted term descriptions based on word senses and domains. In addition, we apply an automatically generated encyclopedia to a question answering system targeting the Japanese Information-Technology Engineers Examination.

In this paper, we propose a method to extract descriptions of technical terms from Web pages in order to utilize the World Wide Web as an encyclopedia. We use linguistic patterns and HTML text structures to extract text fragments containing term descriptions. We also use a language model to discard extraneous descriptions, and a clustering method to summarize resultant descriptions. We show the effectiveness of our method by way of experiments.