Diego Mollá Aliod
Since the beginning of Artificial Intelligence, programmers have tried to implement the ultimate help system: a program which would accurately answer any question asked by the user about how to use this or that, how to perform this or that, what is available in the system, etc. As time passes by, we are all too aware that such a goal is all too difficult to achieve with the current technology.
It is possible, though, to attempt to solve simpler goals, and nowadays one can find programs which try to solve tasks such as information retrieval and information extraction. In the case of information retrieval, the goal is to find those files that possibly contain the information the user wants. This information is typically asked in form of lists of keywords, but it can be also asked in plain English. But even if the question is asked in English, these systems will typically convert the user question into a keyword-type question, by removing all the "irrelevant" words such as articles, very common words, etc. As a result, many of the restrictions given by the user are lost, and the system would retrieve far more documents than one would want (anybody who regularly searches the Internet with a standard search engine will know what I am talking about). In these systems, then, the user can freely ask the question, but the results given are far from ideal, needing a lot of human filtering.
The case of information extraction is different. Now the goal is to find specific information which suits a specific type of questions. This information will be generally returned in a structured fashion which can be processed later by another program. The draw-back of this approach is, however, that the type of questions is very restricted. Questions will typically belong to a specific type, according to a number of pre-defined frames.
In our project, we are trying to devise a system, ExtrAns, with a high degree of recall and precision, which can be freely queried in plain English, without any restrictions, and who finds the exact passage that directly answers the question, not just a pointer to the document. This is what we call answer extraction. Due to the flexibility we want to achieve and the degree of recall and precision, using a degree of linguistic information is inevitable. We will not, however, resort to a full-fledged question-answering system. Possible applications will typically have data of moderate size, and the data will be fairly stable. Examples are help systems of big programs or operating systems.
The specific goal of ExtrAns is to find the relevant passages of the Unix "man pages" which directly answer the user query. In order to do that, the text is preprocessed, analysed, and the information translated into a set of logical forms that can be queried over. We have used public-domain software in as many modules as possible, but still we had to fully implement some of them.
Overall, the system analyses the data in this sequence: first, the source files of the Unix "man pages" are preprocessed and the data tokenised and converted into a format which can be understood by the parser. Then, the data are parsed. After that, the parses are disambiguated as much as possible and finally converted into logical forms which can express semantic information and dependencies between the words. For example, a typical logical form of the sentence "cp copies filename1 onto filename2" would state that cp is a command, filename1 and filename2 are arguments, and furthermore they refer to files, that a copying event is involved, that cp does the copying, that the source file is filename1 and the destination is filename2. In ExtrAns, all of this information is stated by means of a subset of the Predicate Logic, the Horn Clauses, which can be easily handled by the Prolog programming language.
When the user asks a question, the sequence of steps is as follows. First, the logical form of the query is constructed following the same steps as above, and the logical form is converted into a Prolog query. Second, the system uses the standard Prolog unification and backtracking mechanisms to find the logical forms of the data which are compatible with the query, and all the relevant sentences are retrieved. Third, the results are displayed in a user-friendly fashion, highlighting those parts of the sentences which are more likely to directly answer the question.
In the next sections we will see some particularities of some of the steps mentioned above, what type of problems we found, and a hint on how we solved (or how we are trying to solve) them.
In general, tokenising a text means merely identifying word forms and sentences. However, in highly technical documents such as the Unix man pages, this may become a formidable task. Apart from regular word forms, the ExtrAns tokeniser has to recognise all of the following as tokens and represent them as normalised expressions:
Normalising such tokens means, among other things, to appropriately mark special tokens such as command names (otherwise the parser chokes on them). Luckily, the Unix man pages contain a considerable amount of useful information beyond the purely textual level, namely the information conveyed by the formatting commands. Thus command names are, as a rule, printed in boldface, and expressions used as arguments, in italics, as in:
compress [ -cfv ] [ -b bits ] [ filename ...]
This type of information is extracted from the formatting commands and added to the tokens for later modules to use (e.g. "eject", when used as the name of a command, is turned into "eject.com", and "filename", when used as an argument, into "filename.arg"). Unfortunately, the formatting instructions in the Unix man pages are used in a fairly unsystematic fashion (these pages were written by dozens of different persons). The tokeniser must cope with this by collecting as much information as possible about the special words. Thus, for instance, it must collect all the command and argument names from the SYNOPSIS section of the man page to make sure that it will recognise them in the body of the text, even if they are formatted incorrectly.
Once the text is properly tokenised, it must be parsed and the logical form of each sentence computed. We were initially using Grover et al.'s (1993) Alvey system. This system contains a large dictionary and a wide-coverage grammar, and it provides the semantic information of the parsed sentence, which we can easily adapt for our purposes. However, the Alvey system contained a few disadvantages which made its use difficult. For example, the number of parses of each sentence was much larger than what one would expect, and the system did not cope well with unknown words or ungrammatical sentences. For that reason we decided to shift to Sleator and Temperley's (1991) Link Grammar, which was more advantageous than the Alvey system in those points.
The disadvantage of the Link Grammar is that the result only contains syntactic information, and we must build the logical form from scratch. The Link Grammar is dependency-based (as opposed to constituency-based) and the syntactic information is given as a set of labelled connections between the words forming the sentence (as opposed to a parse tree). We cannot use any approach based on parse trees to build the logical form, and since we have no knowledge of any theory attempting to use a dependency grammar to build the logical form (any suggestions?), we had to do all the work unassisted. The task is not as formidable as it seems, though, since it is possible to "see" the parse tree out of the set of links. Consider the following example:
+------------MV----------------+ +----O------+ | +-W--+-S--+ +-D---+-M--+--J---+ +--J----+ | | | | | | | | | ///// cp copies the contents of filename1 onto filename2
Here, the subject is "cp", the verb is "copies", the object is "the contents of filename1", and there is a prepositional phrase "onto filename2". We can actually follow the links as if we were exploring the branches of a parse tree, and use the labels to determine the type of dependency. For example, a S denotes a subject, an O an object, and a MV a prepositional phrase modifying a verb. The "root" of the tree would be the link to the wall (the "/////" at the left). There are, however, some features of the linkages which make the extraction of the logical form cumbersome or difficult. Some of them are:
The query posed by the user may retrieve several sentences. ExtrAns displays all the sentences retrieved, and highlights those passages of the sentences which directly answer the query, in such a way that a stronger highlighting implies that the text is more likely to directly answer the query (see the next section). Furthermore, it is possible to view the manual page which contains the sentence, by clicking the link at the left of the sentence. The manual page shows the same highlighting in the relevant sentences, allowing the user to easily spot the sentences in the manual. We can see this in the enclosed figures, who show the result of the query "which command copies files?", and the manual page of the "cp" command, with the relevant highlighting.
Figure 1: The result of the query "which
command copies files?"
Figure 2: Unix manpage with highlighting
Disambiguation is a problem our system must deal with. Sleator and Temperley's parser does not try to solve syntactic nor semantic ambiguities, and a long sentence may generate hundreds, even thousands, different parses. The following sentence, extracted from the Unix manual pages, has 96 parses:
"If you do not have write permission on the file and the standard input is a terminal, rm displays the file's permissions and waits for you to type in a response."
ExtrAns tries to resolve (syntactic) ambiguities in two steps. First of all, some hand-crafted rules filter out the most straightforward cases. An example of such a rule is: "A prepositional phrase headed by of can attach only to the immediately preceding noun or noun coordination." After applying this and other rules, the sentence above retains 48 parses.
In a second step, we adopt Brill and Resnik's (1994) prepositional phrase disambiguation approach. Their original algorithm is extended to cover as many types of ambiguities as possible, and the resulting program is trained with specific data extracted from the manual pages. In this way, the number of different parses of the sentence above is reduced to 12.
As we can see above, not all of the spurious ambiguities are filtered out. We are working on the way to improve the disambiguation efficiency by adding word-classes and other semantic information. Still, we are well aware that we cannot resolve all the ambiguities, among other things because some of them may be genuinely irresoluble. For example, the sentence
"I saw a man with a telescope"
has two readings. In the normal reading, I am saying that I used a telescope to see a man. There is another reading, however, namely that it is the man who has the telescope, and I saw him and his telescope. Clearly, sentences like this cannot be disambiguated. In fact, there may be cases where the speaker intentionally keeps the ambiguity unresolved.
For this reason, ExtrAns tries to reduce the number of ambiguities, but it does not try to obtain exactly one logical form out of every sentence. If a sentence has several irreducible interpretations, ExtrAns stores in its database all of them. When the user asks a query, a sentence may be retrieved several times (because the sentence may have several logical forms which are compatible with the query). These different logical forms may highlight different words, as a result of the different interpretations. ExtrAns handles this mismatch of highlighted words by superimposing all of the highlights of each sentence, in such a way that those parts which are retrieved more times are highlighted with a brighter colour. For example, consider the string "If filename2 has a mode which forbids writing, mv prints the mode" in figure 1. This string formed part of all of the interpretations of the sentence labelled as mv.1/DESCRIPTION/6 which have been retrieved in response to the user query. Therefore, it is highlighted with more intensity. Thanks to this, the user has an accurate idea of what parts of the sentence are more relevant to the query being asked. This way of presenting search results makes even multiple ambiguities fairly unobtrusive.
Our work is not completed yet, but we are optimistic about the possibilities of ExtrAns. Currently it can parse 30 manpages, but we intend to extend the number of pages, until we eventually reach a critical point from which a new manpage will represent (almost) no problems for the system. In the current prototype, the user can freely ask any question about the contents of the manual pages, and an accurate answer is retrieved and displayed, highlighting the parts of the sentences which are most relevant to the user, with pointers to the original manpages.
Apart from extending the linguistic coverage of the system, we are also starting to tackle more complex problems, such as semantics and the use of world knowledge to establish useful inferences, refine the disambiguation process, and anaphora resolution.