Analysis of technologies that recognize and/or respond directly to spoken or written human languages.
I believe there are two ways search will improve significantly in the future. First, since talking is easier than typing, speech recognition will allow longer and more accurate input strings. Second, search will be informed by much more persistent user information, with search companies having very detailed understanding of searchers. Based on that, I expect:
- A small oligopoly dominating the conjoined businesses of mobile device software and search. The companies most obviously positioned for membership are Google and Apple.
- The continued and growing combination of search, advertisement/recommendation, and alerting. The same user-specific data will be needed for all three.
- A whole lot of privacy concerns.
My reasoning starts from several observations:
- Enterprise search is greatly disappointing. My main reason for saying that is anecdotal evidence — I don’t notice users being much happier with search than they were 15 years ago. But business results are suggestive too:
- HP just disclosed serious problems with Autonomy.
- Microsoft’s acquisition of FAST was a similar debacle.
- Lesser enterprise search outfits never prospered much. (E.g., when’s the last time you heard mention of Coveo?)
- My favorable impressions of the e-commerce site search business turned out to be overdone. (E.g., Mercado’s assets were sold for a pittance soon after I wrote that, while Endeca and Inquira were absorbed into Oracle.)
- Lucene/Solr’s recent stirrings aren’t really in the area of search.
- Web search, while superior to the enterprise kind, is disappointing people as well. Are Google’s results any better than they were 8 years ago? Google’s ongoing hard work notwithstanding, are they even as good?
- Consumer computer usage is swinging toward mobile devices. I hope I don’t have to convince you about that one.
In principle, there are two main ways to make search better:
- Understand more about the documents being searched over. But Google’s travails, combined with the rather dismal history of enterprise search, suggest we’re well into the diminishing-returns part of that project.
- Understand more about what the searcher wants.
The latter, I think, is where significant future improvement will be found.
|Categories: Autonomy, Coveo, Endeca, Enterprise search, FAST, Google, Lucene, Mercado, Microsoft, Search engines, Speech recognition, Structured search||4 Comments|
The newsletter/column excerpted below was originally published in 1998. Some of the specific references are obviously very dated. But the general points about the requirements for successful natural language computer interfaces still hold true. Less progress has been made in the intervening decade-plus than I would have hoped, but some recent efforts — especially in the area of search-over-business-intelligence — are at least mildly encouraging. Emphasis added.
Natural language computer interfaces were introduced commercially about 15 years ago*. They failed miserably.
*I.e., the early 1980s
For example, Artificial Intelligence Corporation’s Intellect was a natural language DBMS query/reporting/charting tool. It was actually a pretty good product. But it’s infamous among industry insiders as the product for which IBM, in one of its first software licensing deals, got about 1700 trial installations — and less than a 1% sales close rate. Even its successor, Linguistic Technologies’ English Wizard*, doesn’t seem to be attracting many customers, despite consistently good product reviews.
*These days (i.e., in 2009) it’s owned by Progress and called EasyAsk. It still doesn’t seem to be selling well.
Another example was HAL, the natural language command interface to 1-2-3. HAL is the product that first made Bill Gross (subsequently the founder of Knowledge Adventure and idealab!) and his brother Larry famous. However, it achieved no success*, and was quickly dropped from Lotus’ product line.
*I loved the product personally. But I was sadly alone.
In retrospect, it’s obvious why natural language interfaces failed. First of all, they offered little advantage over the forms-and-menus paradigm that dominated enterprise computing in both the online-character-based and client-server-GUI eras. If you couldn’t meet an application need with forms and menus, you couldn’t meet it with natural language either. Read more
|Categories: BI integration, IBM and UIMA, Language recognition, Natural language processing (NLP), Progress and EasyAsk, Search engines, Speech recognition||2 Comments|
Google held a superbly-received preview of a new technology called Google Wave, which promises to “reinvent communication.” In simplest terms, Google Wave is a software platform that:
- Offers the possibility to improve upon a broad range of communication, collaboration, and/or text-based product categories, such as:
- Word processing
- Instant messaging
- Mini-portals (Facebook-style)
- Mini-portals (Sharepoint-style)
- In particular, allows these applications to be both much more integrated and interactive than they now are.
- Will have open developer APIs.
- WIll be open-sourced.
If this all works out, Google Wave could play merry hell with Microsoft Outlook, Microsoft Word, Microsoft Exchange, Microsoft SharePoint, and more.
I suspect it will.
And by the way, there’s a cool “natural language” angle as well. Read more
|Categories: Google, Language recognition, Microblogging, Microsoft, Natural language processing (NLP), Search engines, Social software and online media, Software as a Service (SaaS)||3 Comments|
Stephen Shankland reviewed Yahoo’s mobile voice search, which works by taking voice input and returning results onscreen (in his case on his Blackberry Pearl). He found:
- There are plenty of times when voice is a more convenient form of input than typing.
- Voice recognition was good but far from perfect.
- Editing search strings was annoyingly difficult.
- Search results themselves aren’t 100% perfect.
No big surprises there.
|Categories: Language recognition, Search engines, Specialized search, Speech recognition, Yahoo||Leave a Comment|
Marie Wallace of IBM wrote back in response to my post on Languageware. In particular, it seems I got the Languageware/UIMA relationship wrong. Marie’s email was long and thoughtful enough that, rather than just pointing her at the comment thread, I asked for permission to repost it. Here goes:
Thanks for your mention to LanguageWare on your blog, albeit a skeptical one I totally understand your scepticism as there is so much talk about text analytics these days and everyone believes they have solved the problem. I guess I can only hope that our approach will indeed prove to be different and offers some new and interesting perspectives.
The key differentiation in our approach is that we have completely decoupled the language model from the code that runs the analysis. This has been generalized to a set of data-driven algorithms that apply across many languages so that you can have an approach that makes the solution hugely and rapidly customizable (without having to change code). It is this flexibility that we believe is core to realizing multi-lingual and multi-domain text analysis applications in a real-word scenario. This customization environment is available for download from Alphaworks, http://www.alphaworks.ibm.com/tech/lrw, and we would love to get feedback from your community.
On your point about performance, we actually consider UIMA one of our greatest performance optimizations and core to our design. The point about one-pass is that we never go back over the same piece of text twice at the same “level” and take a very careful approach when defining our UIMA Annotators. Certain layers of language processing just don’t make sense to split up due to their interconnectedness and therefore we create our UIMA annotators according to where they sit in the overall processing layers. That’s the key point.
Anyway those are my thoughts, and thanks again for the mention. It’s really great to see these topics being discussed in an open and challenging forum.
Marie Wallace of IBM wrote in from Ireland to call my attention to Languageware, IBM’s latest try at natural language processing (NLP). Obviously, IBM has been down this road multiple times before, from ViaVoice (dictation software that got beat out by Dragon NaturallySpeaking) to Penelope (research project that seemingly went on for as long as Odysseus was away from Ithaca — rumor has it that the principals eventually decamped to Microsoft, and continued to not produce commercial technology there). Read more
I forget how I got the URL, but something called the Chatbot Game purports to be a combination of Eliza and Digg. That is, it’s a chatbot with a lot of rules; anybody can submit rules; rules are voted up and down.
I don’t think I’ll want to play with it for a while (I’m heading off on vacation for a while), so I thought I’d post it here to see if anybody else had any thoughts about or familiarity with it.
TechCrunchIT ranted yesterday against voice recognition. Parts of the argument have validity, but I think the overall argument was overstated.
Key points included:
1. Microsoft and Bill Gates have been overoptimistic about voice recognition.
2. Who needs voice when you have keyboards big and small?
3. The office environment is too noisy for voice recognition to work.
|Categories: Language recognition, Natural language processing (NLP), Spam and antispam, Speech recognition||2 Comments|
As I see it, there are eight distinct market areas that each depend heavily on linguistic technology. Five are off-shoots of what used to be called “information retrieval”:
1. Web search
2. Public-facing site search
3. Enterprise search and knowledge management
4. Custom publishing
5. Text mining and extraction
Three are more standalone:
6. Spam filtering
7. Voice recognition
8. Machine translation