Automatic sentiment classification has been extensively studied and applied in recent years. However, sentiment is expressed differently in different domains, and annotating corpora for every possible domain of interest is impractical. We investigate domain adaptation for sentiment classifiers, focusing on online reviews for different types of products. First, we extend to sentiment classification the recently-proposed structural correspondence learning (SCL) algorithm, reducing the relative error due to adaptation between domains by an average of 30% over the original SCL algorithm and 46% over a supervised baseline. Second, we identify a measure of domain similarity that correlates well with the potential for adaptation of a classifier from one domain to another. This measure could for instance be used to select a small set of domains to annotate whose trained classifiers would transfer well to many other domains.

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Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification

Discriminative learning methods are widely used in natural language processing. These methods work best when their training and test data are drawn from the same distribution. For many NLP tasks, however, we are confronted with new domains in which labeled data is scarce or non-existent. In such cases, we seek to adapt existing models from a resource-rich source domain to a resource-poor target domain. We introduce structural correspondence learning to automatically induce correspondences among features from different domains. We test our technique on part of speech tagging and show performance gains for varying amounts of source and target training data, as well as improvements in target domain parsing accuracy using our improved tagger.

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Domain Adaptation with Structural Correspondence Learning

An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

Intelligent Automated Assistant

In order to respond correctly to a free form factual question given a large collection of texts, one needs to understand the question to a level that allows determining some of the constraints the question imposes on a possible answer. These constraints may include a semantic classification of the sought after answer and may even suggest using different strategies when looking for and verifying a candidate answer.This paper presents a machine learning approach to question classification. We learn a hierarchical classifier that is guided by a layered semantic hierarchy of answer types, and eventually classifies questions into fine-grained classes. We show accurate results on a large collection of free-form questions used in TREC 10.

Learning question classifiers

An adaptive interface for a programmable system, for predicting a desired user function, based on user history, as well as machine internal status and context. The apparatus receives an input from the user and other data. A predicted input is presented for confirmation by the user, and the predictive mechanism is updated based on this feedback. Also provided is a pattern recognition system for a multimedia device, wherein a user input is matched to a video stream on a conceptual basis, allowing inexact programming of a multimedia device. The system analyzes a data stream for correspondence with a data pattern for processing and storage. The data stream is subjected to adaptive pattern recognition to extract features of interest to provide a highly compressed representation that may be efficiently processed to determine correspondence. Applications of the interface and system include a video cassette recorder (VCR), medical device, vehicle control system, audio device, environmental control system, securities trading terminal, and smart house. The system optionally includes an actuator for effecting the environment of operation, allowing closed-loop feedback operation and automated learning.

Adaptive pattern recognition based control system and method

A system of mobile units are installed in multiple vehicles in traffic. These mobile units include both wireless communications devices and apparatus that determines the location of each vehicle. Monitoring a vehicle's position as a function of time also reveals the velocity of the vehicle. Position and speed information is periodically broadcast by the vehicles to a central monitoring station and to neighboring vehicles. At the central monitoring station, the collective input of a set of vehicles is processed to provide an instant chart of traffic conditions in the area. Warnings of delays or updates on traffic conditions on the road ahead are then automatically returned to subscribers of the information or are used as part of an Intelligent Vehicle Highway System (IVHS). Neighboring vehicles within a region communicating with one another form a network in which the broadcast information is processed locally on the respective vehicles to estimate possible problems ahead and consider computing an alternate road and/or checking with the central monitoring station for more information. If out of range of the central monitoring station, the vehicles in the network form a local area network for the exchange and update of information, and when any vehicle in the network is within range of the central monitoring station, the local area network data is uploaded to help update the overall traffic information.

Automated traffic mapping

: Entity detection and tracking is a relatively new addition to the repertoire of natural language tasks. In this paper, we present a statistical language-independent framework for identifying and tracking named, nominal and pronominal references to entities within unrestricted text documents, and chaining them into clusters corresponding to each logical entity present in the text. Both the mention detection model and the novel entity tracking model can use arbitrary feature types, being able to integrate a wide array of lexical, syntactic and semantic features. In addition, the mention detection model crucially uses feature streams derived from different named entity classifiers. The proposed framework is evaluated with several experiments run in Arabic, Chinese and English texts; a system based on the approach described here and submitted to the latest Automatic Content Extraction (ACE) evaluation achieved top-tier results in all three evaluation languages.

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A Statistical Model for Multilingual Entity Detection and Tracking

Abraham Itty heriah, Martin Franz, Wei-Jing Zhu, Adwait Ratnaparkhi P.O.Box 218, Yorktown Heights, NY 10598 fabei,franzm,wjzhu,adwaitrg watson.ibm. om Ri hard J. Mammone Dept. of Ele tri al Engineering, Rutgers University, Pis ataway, NJ 08854 mammone aip.rutgers.edu Abstra t We des ribe the IBM Statisti al Question Answering for TREC-9 system in detail and look at several examples and errors. The system is an appli ation of maximum entropy lassi ation for question/answer type predi tion and named entity marking. We des ribe our system for information retrieval whi h in the rst step did do ument retrieval from a lo al en y lopedia, and in the se ond step performed an expansion of the query words and nally did passage retrieval from the TREC olle tion. We will also dis uss the answer sele tion algorithm whi h determines the best senten e given both the question and the o urren e of a phrase belonging to the answer lass desired by the question. Results at the 250 byte and 50 byte levels for the overall system as well as results on ea h sub omponent are presented. 1 System Des ription Systems that perform question answering automati ally by omputer have been around for some time as des ribed by (Green et al., 1963). Only re ently though have systems been developed to handle huge databases and a slightly ri her set of questions. The types of questions that an be dealt with today are restri ted to be short answer fa t based questions. In TREC-8, a number of sites parti ipated in the rst question-answering evaluation (Voorhees and Ti e, 1999) and the best systems identi ed four major subomponents: Question/Answer Type Classi ation Query expansion/Information Retrieval Named Entity Marking Answer Sele tion Our system ar hite ture for this year was built around these four major omponents as shown in Fig. 1. Here, the question is input and lassi ed as asking for an answer whose ategory is one of the named entity lasses to be des ribed below. Additionally, the question is presented to the information retrieval (IR) engine for query expansion and do ument retrieval. This engine, given the query, looks at the database of do uments and outputs the best do uments or passages annotated with the named entities. The nal stage is to sele t the exa t answer, given the information about the answer lass and the top s oring passages. Minimizing various distan e metri s applied over phrases or windows of text results in the best s oring se tion that has a phrase belonging to answer lass. This then represents the best s oring answer.

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IBM's Statistical Question Answering System.

A method and apparatus is disclosed that allows people to carry on unobtrusive phone conversations in business or other settings where it is either not possible or impolite to talk In the system of FIG 1, the telephone user one will listen in the same manner as with a regular telephone However, he will not speak into the telephone microphone User one instead employs a unit including a keyboard to enter the text corresponding to what he wants to say The text is converted into a synthesized speech using TTS apparatus and a voice output is sent to the microphone of the phone apparatus The telephone apparatus transmits the synthesized voice signal over a standard telephone line to a unit including a conventional telephone speaker 26 and telephone microphone User two, the party using the telephone at the other end, listens to a synthesized voice, but user one listens to the actual voice of user two with the telephone speaker, unless user two is also using a system similar to that of user one Handwritten text may also be used in the system by employing a computer with a character recognition program as an input In such a case handwriting is converted into synthesized sound and inputted into the telephone microphone The telephone system can be used by the hearing impaired without involving a third party human transcriber

Method and apparatus for a communication device for use by a hearing impaired/mute or deaf person or in silent environments

: In this paper, we document our efforts to extend our statistical question answering system for TREC-11. We incorporated a web search feature, and novel extensions of statistical machine translation as well as extracting lexical patterns for exact answers from a supervised corpus. Without modification to our base set of thirty-one categories, we were able to achieve a confidence weighted score of 0.455 and an accuracy of 29%. We improved our model on selecting exact answers by insisting on exact answers in the training corpus and this resulted in a 7% gain on TREC-11 but a much larger gain of 46% on TREC-10.

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Abraham Ittycheriah

Papers

Automated traffic mapping

A Statistical Model for Multilingual Entity Detection and Tracking

IBM's Statistical Question Answering System.

Method and apparatus for a communication device for use by a hearing impaired/mute or deaf person or in silent environments

IBM's Statistical Question Answering System - TREC-11